Computational drug discovery

MolSnapper: Conditioning Diffusion for Structure-Based Drug Design

Generative models have emerged as potentially powerful methods for molecular design, yet challenges persist in generating molecules that effectively bind to the intended target. The ability to control the design process and incorporate prior knowledge would be highly beneficial for better tailoring molecules to fit specific binding sites. In this paper, we introduce MolSnapper, a novel tool that is able to condition diffusion models for structure-based drug design by seamlessly integrating expert knowledge in the form of 3D pharmacophores. We demonstrate through comprehensive testing on both the CrossDocked and Binding MOAD data sets that our method generates molecules better tailored to fit a given binding site, achieving high structural and chemical similarity to the original molecules. Additionally, MolSnapper yields approximately twice as many valid molecules as alternative methods.

High-throughput computational screening for lead discovery and development

High-throughput computational screening (HTCS) has revolutionized the drug discovery process by enabling the rapid identification and optimization of potential lead compounds. Leveraging the power of advanced algorithms, machine learning, and molecular simulations, HTCS facilitates the efficient exploration of vast chemical spaces, significantly accelerating early-stage drug discovery. The time, cost, and labor in the case of traditional experimental approaches are reduced by the ability to virtually screen millions of compounds for biological activity. This paradigm shift is also facilitated by the combination of omics data, genomics, proteomics, and metabolomics in computational pipelines, allowing detailed understanding of complex biological systems and paving the way toward personalized medicine. Core methods such as molecular docking, QSAR models, and pharmacophore modeling are the foundation of HTCS, providing predictive information on molecular interactions and binding affinities. Machine learning and artificial intelligence are augmenting these tools with more precise prediction accuracy and revealing rich patterns embedded in molecular data. With the development of HTCS, more and more, computational methods are used as a powerful tool in de novo drug design, in which computational tools produce a novel chemical entity that shows optimal fit to the target. Despite its transformative potential, HTCS faces challenges related to data quality, model validation, and the need for robust regulatory frameworks. Nevertheless, as AI-driven approaches, quantum computing, and big data analytics continue to evolve, HTCS is set to become a cornerstone of modern drug discovery, reshaping the field with smarter, more personalized therapeutic strategies that address complex diseases with precision and efficiency.

Deciphering the potential of plant metabolites as insecticides against melon fly (Zeugodacus cucurbitae): Exposing control alternatives to assure food security

In the absence of effective biological or chemical controls, the melon fly poses a significant threat to food security, particularly impacting cucurbit crops in tropical and subtropical regions. Melon fly infestations have resulted in yield losses of 30 %-100 %, depending on the specific cucurbit species and season. Current control methods using synthetic chemicals are challenging due to their environmental and biological impacts. This study identified 59 phytocompounds with potential insecticidal properties against the melon fly, exhibiting minimal environmental impact. Key protein targets-hedgehog protein, spastin protein, and ABC-type heme transporter ABCB6 protein-were selected for binding affinity analysis. Camptothecin demonstrated the highest binding affinities for hedgehog protein (-57.32 kcal/mol) and spastin protein (-50.84 kcal/mol), while jervine had the strongest binding affinity for ABC-type heme transporter ABCB6 protein (-43.92 kcal/mol). The control compound, malathion, showed lower binding affinities across all three proteins. Stability of the top compound-protein complexes was further confirmed through a 100 ns molecular dynamics simulation. In insecticide-likeness evaluations, jervine consistently scored high, with camptothecin also performing well, while neriifolin ranked lower. The leading compounds showed no adverse effects that could diminish their insecticidal potential. These findings indicate that jervine and camptothecin are promising candidates for melon fly management, offering potential to prevent significant crop losses. However, as this study was conducted solely through computational methods, we recommend subsequent in vitro and field trials for the future drug development.

Targeting cyclin-dependent kinase 11: a computational approach for natural anti-cancer compound discovery

Cancer, a leading global cause of death, presents considerable treatment challenges due to resistance to conventional therapies like chemotherapy and radiotherapy. Cyclin-dependent kinase 11 (CDK11), which plays a pivotal role in cell cycle regulation and transcription, is overexpressed in various cancers and is linked to poor prognosis. This study focused on identifying potential inhibitors of CDK11 using computational drug discovery methods. Techniques such as pharmacophore modeling, virtual screening, molecular docking, ADMET predictions, molecular dynamics simulations, and binding free energy analysis were applied to screen a large natural product database. Three pharmacophore models were validated, leading to the identification of several promising compounds with stronger binding affinities than the reference inhibitor. ADMET profiling indicated favorable drug-like properties, while molecular dynamics simulations confirmed the stability and favorable interactions of top candidates with CDK11. Binding free energy calculations further revealed that UNPD29888 exhibited the strongest binding affinity. In conclusion, the identified compound shows potential as a CDK11 inhibitor based on computational predictions, suggesting their future application in cancer treatment by targeting CDK11. These computational findings encourage further experimental validation as anti-cancer agents.

Defining ortholog-specific UHRF1 inhibition by STELLA for cancer therapy

UHRF1 maintains DNA methylation by recruiting DNA methyltransferases to chromatin. In mouse, these dynamics are potently antagonized by a natural UHRF1 inhibitory protein STELLA, while the comparable effects of its human ortholog are insufficiently characterized, especially in cancer cells. Herein, we demonstrate that human STELLA (hSTELLA) is inadequate, while mouse STELLA (mSTELLA) is fully proficient in inhibiting the abnormal DNA methylation and oncogenic functions of UHRF1 in human cancer cells. Structural studies reveal a region of low sequence homology between these STELLA orthologs that allows mSTELLA but not hSTELLA to bind tightly and cooperatively to the essential histone-binding, linked tandem Tudor domain and plant homeodomain (TTD-PHD) of UHRF1, thus mediating ortholog-specific UHRF1 inhibition. For translating these findings to cancer therapy, we use a lipid nanoparticle (LNP)-mediated mRNA delivery approach in which the short mSTELLA, but not hSTELLA regions are required to reverse cancer-specific DNA hypermethylation and impair colorectal cancer tumorigenicity.

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-specific scoring function for hDHODH (TSSF-hDHODH). The Pearson correlation coefficient values of TSSF-hDHODH in the cross-validation and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random forest (RF) based generic scoring function RF-Score. TSSF-hDHODH is further used for the virtual screening of potential inhibitors in the FDA-Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynamics simulations, crizotinib is identified as a candidate for subsequent structural optimization. This study can be useful for the discovery of hDHODH inhibitors and the development of scoring functions for additional targets.

Indole Derivatives as Promising Anti-Dengue Agents: A Review of Recent Advances

Dengue, a mosquito-borne disease transmitted by Aedes mosquitoes, is a significant global health concern. Despite extensive research, effective treatments remain limited. The indole nucleus, known for its diverse pharmacological properties, has emerged as a promising scaffold for anti-dengue drug discovery. This review comprehensively examines recent advancements in the fields of natural products, medicinal chemistry, and computer-aided drug design focused on discovering indole-based anti-dengue agents. We discuss the rationale for targeting indole frameworks, highlight key structural features associated with anti-dengue activity, and summarize recent research findings. The review aims to provide valuable insights for researchers working on developing novel anti-dengue therapeutics.

Ichor: A Python library for computational chemistry data management and machine learning force field development

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Indian Medicinal Plant-Derived Phytochemicals as Potential Antidotes for Snakebite: A Pharmacoinformatic Study of Atrolysin Inhibitors

Snakebite envenoming is a significant health threat, particularly in tropical regions, causing substantial morbidity and mortality. Traditional treatments, including antivenom therapy, have limitations and associated risks. This research aims to discover novel phytochemical antidotes for snakebites, specifically targeting the western diamondback rattlesnake (Crotalus atrox) venom metalloproteinase Atrolysin. Utilizing pharmacoinformatic techniques such as molecular docking, high-throughput ligand screening, pharmacophore mapping, pharmacokinetic profiling, and molecular dynamics (MD) simulations, we analyzed phytochemicals from the Indian Medicinal Plants, Phytochemistry And Therapeutics (IMPPAT) database alongside well-known nine metalloproteinase inhibitors from the PubChem database. From an initial set of 17,967 compounds, 4708 unique compounds were identified for further study. These compounds were evaluated based on drug likeness, molecular descriptors, ADME properties, and toxicity profiles. Binding site predictions and molecular docking identified key interacting residues and binding energies, highlighting several promising compounds. Density functional theory (DFT) analysis provided insights into these compounds' electronic properties and stability. MD simulations assessed the dynamic stability of protein-ligand complexes using parameters such as RMSD, RMSF, the radius of gyration, and hydrogen bond interactions. This study identified top candidates, including CID5291, IMPHY001495, IMPHY014737, IMPHY008983, IMPHY008176, and IMPHY003833, based on their favorable binding energies, interaction forces, and structural stability. These findings suggest that the selected phytochemicals have the potential to serve as effective alternatives to traditional antivenom treatments, offering a promising avenue for further research and development in snakebite management.

GADIFF: a transferable graph attention diffusion model for generating molecular conformations

The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features. In addition, we utilize MSA to calculate dynamic noise weights to boost molecular conformation noise prediction. Upon the improvements, GADIFF achieves competitive performance compared with recently reported state-of-the-art models in terms of generation diversity(COV-R, COV-P), accuracy (MAT-R, MAT-P), and property prediction for GEOM-QM9 and GEOM-Drugs datasets. In particular, on the GEOM-Drugs dataset, the average COV-R is improved by 3.75% compared with the best baseline model at a threshold (1.25 Å). Furthermore, a transfer model named GADIFF-NCI based on GADIFF is developed to generate conformations for noncovalent interaction (NCI) molecular systems. It takes GADIFF with GEOM-QM9 dataset as a pre-trained model, and incorporates a graph encoder for learning molecular vectors at the NCI molecular level. The resulting NCI molecular conformations are reasonable, as assessed by the evaluation of conformation and property predictions. This suggests that the proposed transferable model may hold noteworthy value for the study of multi-molecular conformations. The code and data of GADIFF is freely downloaded from https://github.com/WangDHg/GADIFF.

Antibiotic resistance challenge: evaluating anthraquinones as rifampicin monooxygenase inhibitors through integrated bioinformatics analysis

OBJECTIVE

Antibiotic resistance poses a pressing and crucial global public health challenge, leading to significant clinical and health-related consequences. Substantial evidence highlights the pivotal involvement of rifampicin monooxygenase (RIFMO) in the context of antibiotic resistance. Hence, inhibiting RIFMO could offer potential in the treatment of various infections. Anthraquinones, a group of organic compounds, have shown promise in addressing tuberculosis. This study employed integrated bioinformatics approaches to evaluate the potential inhibitory effects of a selection of anthraquinones on RIFMO. The findings were subsequently compared with those of rifampicin (RIF), serving as a positive control inhibitor.

METHODS

The AutoDock 4.0 tool assessed the binding free energy between 21 anthraquinones and the RIFMO catalytic cleft. The ligands were ranked based on the most favorable scores derived from ΔGbinding. The docking analyses for the highest-ranked anthraquinone and RIF underwent a cross-validation process. This validation procedure utilized the SwissDock server and the Schrödinger Maestro docking software. Molecular dynamics simulations were conducted to scrutinize the stability of the backbone atoms in free RIFMO, RIFMO-RIF, and RIFMO complexed with the top-ranked anthraquinone throughout a 100-ns computer simulation. The Discovery Studio Visualizer tool visualized interactions between RIFMO residues and ligands. An evaluation of the pharmacokinetics and toxicity profiles of the tested compounds was also conducted.

RESULTS

Five anthraquinones were indicated with ΔGbinding scores less than - 10 kcal/mol. Hypericin emerged as the most potent RIFMO inhibitor, boasting a ΔGbinding score and inhibition constant value of - 12.11 kcal/mol and 798.99 pM, respectively. The agreement across AutoDock 4.0, SwissDock, and Schrödinger Maestro results highlighted hypericin's notable binding affinity to the RIFMO catalytic cleft. The RIFMO-hypericin complex achieved stability after a 70-ns computer simulation, exhibiting a root-mean-square deviation of 0.55 nm. Oral bioavailability analysis revealed that all anthraquinones except hypericin, sennidin A, and sennidin B may be suitable for oral administration. Furthermore, the carcinogenicity prediction analysis indicated a favorable safety profile for all examined anthraquinones.

CONCLUSION

Inhibiting RIFMO, particularly with anthraquinones such as hypericin, holds promise as a potential therapeutic strategy for infectious diseases.

Antibacterial activity of natural flavones against bovine mastitis pathogens: in vitro, SAR analysis, and computational study

Bovine mastitis is a worldwide disease affecting dairy cattle and causes major economic losses in the dairy industry. Recently, the emergence of microbial resistance to the current antibiotics complicates the treatment protocol which necessitates antibiotic stewardship and further research to find new active compounds. Recently, phytobiotics have gained interest in being used as an alternative to antibiotics in the poultry industry as an antibiotic stewardship intervention. This study evaluated the in vitro antibacterial activity of 16 flavonoids against bovine mastitis pathogens. Two flavones: 2-(4-methoxyphenyl)chromen-4-one (1) and 2-(3-hydroxyphenyl)chromen-4-one (4) showed inhibition of the growth of Klebsiella oxytoca with MIC values range (25-50 µg mL- 1) followed by a structure-activity relationship (SAR) study indicating that the presence of a hydroxyl group at C-3` or methoxy at C-4` increases the activity against Klebsiella oxytoca while the presence of hydroxyl group at C-7 decreases the activity. Furthermore, a structure-based drug development approach was applied using several in silico tools to understand the interactions of active flavones at the active site of the DNA gyrase protein. Compound (4) showed a higher docking score than quercetin (standard) which is known to have antibacterial activity by inhibiting the DNA gyrase. In addition, the structure-based pharmacophores of compound (4) and quercetin showed similar pharmacophoric features and interactions with DNA gyrase. Based on our findings, compounds (1) and (4) are promising for further study as potential anti-microbial phytochemicals that can have a role in controlling bovine mastitis as well as to investigate their mechanism of action further.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00253-w.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

A multitier virtual screening study of phytoconstituents as Myeloid Cell Leukemias 1 inhibitors

Myeloid Cell Leukemia 1 (MCL1) is an anti-apoptotic protein that plays a critical role in regulating cell survival, particularly in cancer cells. It is a member of the BCL-2 family of proteins, which control the intrinsic pathway of apoptosis. MCL1 has emerged as a promising target for cancer therapy because it is overexpressed in a wide range of cancers, including breast, lung, prostate, and hematologic malignancies. Due to its remarkable role in cancer progression, it has been reflected as a promising drug target for cancer therapy. A few MCL1 inhibitors have been identified previously, but further research is needed to develop novel, effective and safe MCL1 inhibitors that can overcome resistance mechanisms and minimize toxicity in normal cells. In this study, we aim to search for compounds that target the critical binding site of MCL1 from phytoconstituent library from the IMPPAT database. To accomplish this, a multitier virtual screening approach involving molecular docking and molecular dynamics simulations (MDS) were used to evaluate their suitability for the receptor. Notably, certain screened phytoconstituents have appreciable docking scores and stable interactions toward the binding pocket of MCL1. The screened compounds underwent ADMET and bioactivity analysis to establish their anticancer properties. One phytoconstituent, Isopongaflavone, was identified that exhibiting higher docking and drug-likeness than the already reported MCL1 inhibitor, Tapotoclax. Isopongaflavone and and Tapotoclax, along with MCL1, were subjected to 100 nanoseconds (ns) MDS study to verify their stability inside the binding site of MCL1. The MDS findings demonstrated a strong binding affinity between Isopongaflavone and the MCL1 binding pocket, resulting in reduced conformational fluctuations. This investigation proposes Isopongaflavone as a promising candidate for the development of innovative anticancer therapeutics, pending the necessary validation procedures. Also, the findings provide valuable information for designing MCL1 inhibitors based on the protein's structure.Communicated by Ramaswamy H. Sarma.

Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives

Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome and (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2H)-one (1), aryl(or heteroaryl)glyoxal monohydrates (2a-h), and hydrazine monohydrate (NH2NH2•H2O) for the regioselective preparation of some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnoline derivatives (3a-h). After synthesis and characterization of the mentioned cinnolines (3a-h), the in silico multi-targeting inhibitory properties of these heterocyclic scaffolds have been investigated upon various Homo sapiens-type enzymes, including hMAO-A, hMAO-B, hAChE, hBChE, hBACE-1, hBACE-2, hNQO-1, hNQO-2, hnNOS, hiNOS, hPARP-1, hPARP-2, hLRRK-2(G2019S), hGSK-3β, hp38α MAPK, hJNK-3, hOGA, hNMDA receptor, hnSMase-2, hIDO-1, hCOMT, hLIMK-1, hLIMK-2, hRIPK-1, hUCH-L1, hPARK-7, and hDHODH, which have confirmed their functions and roles in the neurodegenerative diseases (NDs), based on molecular docking studies, and the obtained results were compared with a wide range of approved drugs and well-known (with IC50, EC50, etc.) compounds. In addition, in silico ADMET prediction analysis was performed to examine the prospective drug properties of the synthesized heterocyclic compounds (3a-h). The obtained results from the molecular docking studies and ADMET-related data demonstrated that these series of 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines (3a-h), especially hit ones, can really be turned into the potent core of new drugs for the treatment of neurodegenerative diseases (NDs), and/or due to the having some reactionable locations, they are able to have further organic reactions (such as cross-coupling reactions), and expansion of these compounds (for example, with using other types of aryl(or heteroaryl)glyoxal monohydrates) makes a new avenue for designing novel and efficient drugs for this purpose.

Structural insights into the potential binding sites of Cathepsin D using molecular modelling techniques

Alzheimer's disease (AD) is the most prevalent type of dementia caused by the accumulation of amyloid beta (Aβ) peptides. The extracellular deposition of Aβ peptides in human AD brain causes neuronal death. Therefore, it has been found that Aβ peptide degradation is a possible therapeutic target for AD. CathD has been known to breakdown amyloid beta peptides. However, the structural role of CathD is not yet clear. Hence, for the purpose of gaining a deeper comprehension of the structure of CathD, the present computational investigation was performed using virtual screening technique to predict CathD's active site residues and substrate binding mode. Ligand-based virtual screening was implemented on small molecules from ZINC database against crystal structure of CathD. Further, molecular docking was utilised to investigate the binding mechanism of CathD with substrates and virtually screened inhibitors. Localised compounds obtained through screening performed by PyRx and AutoDock 4.2 with CathD receptor and the compounds having highest binding affinities were picked as; ZINC00601317, ZINC04214975 and ZINCC12500925 as our top choices. The hydrophobic residues Viz. Gly35, Val31, Thr34, Gly128, Ile124 and Ala13 help stabilising the CathD-ligand complexes, which in turn emphasises substrate and inhibitor selectivity. Further, MM-GBSA approach has been used to calculate binding free energy between CathD and selected compounds. Therefore, it would be beneficial to understand the active site pocket of CathD with the assistance of these discoveries. Thus, the present study would be helpful to identify active site pocket of CathD, which could be beneficial to develop novel therapeutic strategies for the AD.

Identification of osteoporosis ferroptosis-related markers and potential therapeutic compounds based on bioinformatics methods and molecular docking technology

RESEARCH BACKGROUND AND PURPOSE

Osteoporosis (OP) is one of the most common bone diseases worldwide, characterized by low bone mineral density and susceptibility to pathological fractures, especially in postmenopausal women and elderly men. Ferroptosis is one of the newly discovered forms of cell death regulated by genes in recent years. Many studies have shown that ferroptosis is closely related to many diseases. However, there are few studies on ferroptosis in osteoporosis, and the mechanism of ferroptosis in osteoporosis is still unclear. This study aims to identify biomarkers related to osteoporosis ferroptosis from the GEO (Gene Expression Omnibus) database through bioinformatics technology, and to mine potential therapeutic small molecule compounds through molecular docking technology, trying to provide a basis for the diagnosis and treatment of osteoporosis in the future.

MATERIALS AND METHODS

We downloaded the ferroptosis-related gene set from the FerrDb database ( http://www.zhounan.org/ferrdb/index.html ), downloaded the data sets GSE56815 and GSE7429 from the GEO database, and used the R software "limma" package to screen differentially expressed genes (DEGs) from GSE56815, and intersected with the ferroptosis gene set to obtain ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by the R software "clusterProfiler" package. The random forest model was further screened to obtain essential ferroptosis genes. R software "corrplot" package was used for correlation analysis of essential ferroptosis genes, and the Wilcox test was used for significance analysis. The lncRNA-miRNA-mRNA-TF regulatory network was constructed using Cytoscape software. The least absolute shrinkage and selection operator (LASSO) was used to construct a disease diagnosis model, and a Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic performance, and then GSE7429 was used to verify the reliability of the diagnosis model. Molecular docking technology was used to screen potential small molecule compounds from the Drugbank database. Finally, a rat osteoporosis model was constructed, and peripheral blood mononuclear cells were extracted for qRT-PCR detection to verify the mRNA expression levels of crucial ferroptosis genes.

RESULT

Six DEGs related to ferroptosis were initially screened out. GO function and KEGG pathway enrichment analysis showed that ferroptosis-related DEGs were mainly enriched in signaling pathways such as maintenance of iron ion homeostasis, copper ion binding function, and ferroptosis. The random forest model identified five key ferroptosis genes, including CP, FLT3, HAMP, HMOX1, and SLC2A3. Gene correlation analysis found a relatively low correlation between these five key ferroptosis genes. The lncRNA-miRNA-mRNA-TF regulatory network shows that BAZ1B and STAT3 may also be potential molecules. The ROC curve of the disease diagnosis model shows that the model has a good diagnostic performance. Molecular docking technology screened out three small molecule compounds, including NADH, Midostaurin, and Nintedanib small molecule compounds. qRT-PCR detection confirmed the differential expression of CP, FLT3, HAMP, HMOX1 and SLC2A3 between OP and normal control group.

CONCLUSION

This study identified five key ferroptosis genes (CP, FLT3, HAMP, HMOX1, and SLC2A3), they were most likely related to OP ferroptosis. In addition, we found that the small molecule compounds of NADH, Midostaurin, and Nintedanib had good docking scores with these five key ferroptosis genes. These findings may provide new clues for the early diagnosis and treatment of osteoporosis in the future.

Computational model for drug research

This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape.

Environmentally realistic dose of tire-derived metabolite 6PPD-Q exposure causes intestinal jejunum and ileum damage in mice via cannabinoid receptor-activated inflammation

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) is a quinone derivative of a common tire additive 6PPD, whose occurrence has been widely reported both in the environment and human bodies including in adults, pregnant women and children. Yet, knowledge on the potential intestinal toxicity of 6PPD-Q in mammals at environmentally relevant dose remain unknown. In this study, the effects of 6PPD-Q on the intestines of adult ICR mice were evaluated by orally administering environmentally relevant dose or lower levels of 6PPD-Q (0.1, 1, 10, and 100 μg/kg) for 21 days. We found that 6PPD-Q disrupted the integrity of the intestinal barrier, mostly in the jejunum and ileum, but not in the duodenum or colon, in a dose-dependent manner. Moreover, intestinal inflammation manifested with elevated levels of TNF-α, IL-1, and IL-6 mostly observed in doses at 10 and 100 μg/kg. Using reverse target screening technology combining molecular dynamic simulation modeling we identified key cannabinoid receptors including CNR2 activation to be potentially mediating the intestinal inflammation induced by 6PPD-Q. In summary, this study provides novel insights into the toxic effects of emerging contaminant 6PPD-Q on mammalian intestines and that the chemical may be a cannabinoid receptor agonist to modulate inflammation.

iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models

MOTIVATION

Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization.

RESULTS

We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the target graphs are created based on 3D structures using Alphafold2. This architecture is highly expressive in capturing substructures of the graph data. We use a cross-attention module to capture interaction information between the substructures of drugs and targets. To improve feature representations, we integrate features learned by models that are pre-trained on large unlabeled small molecule and protein datasets, respectively. We evaluate our model on three benchmark datasets, and it shows a consistent improvement on all baseline models in all datasets. We also run an experiment with previously unseen drugs or targets in the test set, and our model outperforms all of the baselines. Furthermore, the iNGNN-DTI can provide more insights into the interaction by visualizing the weights learned by the cross-attention module.

AVAILABILITY AND IMPLEMENTATION

The source code of the algorithm is available at https://github.com/syan1992/iNGNN-DTI.

Antimalarial potential, LC-MS secondary metabolite profiling and computational studies of Zingiber officinale

Malaria is among the top-ranked parasitic diseases that pose a threat to the existence of the human race. This study evaluated the antimalarial effect of the rhizome of Zingiber officinale in infected mice, performed secondary metabolite profiling and detailed computational antimalarial evaluation through molecular docking, molecular dynamics (MD) simulation and density functional theory methods. The antimalarial potential of Z. officinale was performed using the in vivo chemosuppressive model; secondary metabolite profiling was carried out using liquid chromatography-mass spectrometry (LC-MS). Molecular docking was performed with Autodock Vina while the MD simulation was performed with Schrodinger desmond suite for 100 ns and DFT calculations with B3LYP (6-31G) basis set. The extract showed 64% parasitaemia suppression, with a dose-dependent increase in activity up to 200 mg/kg. The chemical profiling of the extract tentatively identified eight phytochemicals. The molecular docking studies with plasmepsin II and Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS) identified gingerenone A as the hit molecule, and MMGBSA values corroborate the binding energies obtained. The electronic parameters of gingerenone A revealed its significant antimalarial potential. The antimalarial activity elicited by the extract of Z. officinale and the bioactive chemical constituent supports its usage in ethnomedicine.Communicated by Ramaswamy H. Sarma.

Effects and Mechanisms of Luteolin, a Plant-Based Flavonoid, in the Prevention of Cancers via Modulation of Inflammation and Cell Signaling Molecules

Luteolin, a flavonoid, is mainly found in various vegetables and fruits, including carrots, cabbages, onions, parsley, apples, broccoli, and peppers. Extensive research in vivo and in vitro has been performed to explore its role in disease prevention and treatment. Moreover, this compound possesses the ability to combat cancer by modulating cell-signaling pathways across various types of cancer. The studies have confirmed that luteolin can inhibit cancer-cell survival and proliferation, angiogenesis, invasion, metastasis, mTOR/PI3K/Akt, STAT3, Wnt/β-catenin, and cell-cycle arrest, and induce apoptosis. Further, scientific evidence describes that this compound plays a vital role in the up/down-regulation of microRNAs (miRNAs) in cancer therapy. This review aims to outline the anti-cancer mechanisms of this compound and its molecular targets. However, a knowledge gap remains regarding the studies on its safety and efficacy and clinical trials. Therefore, it is essential to conduct more research based on safety, efficacy, and clinical trials to explore the beneficial role of this compound in disease management, including cancer.

Assessment of iridoids and their similar structures as antineoplastic drugs by in silico approach

Iridoids commonly found in plants as secondary metabolites have been reported to possess significant biological activities such as anticancer, antioxidant, hypoglycemic, antimicrobial etc. The strong interactions of iridoids with cyclic-dependent kinase 8 (CDK8) protein could show inhibitory effects that could modulate tumour growth. From the molecular docking calculations, some iridoids interacted effectively with the target CDK8 protein (PDB ID: 5ICP) with better binding affinities of -9.1, -9.0, -9.0, -8.9 kcal/mol, than that observed for the native ligand with -8.7 kcal/mol and for the reference compound gemcitabine with -6.9 kcal/mol. The GI50 values (<5 μM) obtained from graph-based signatures showed activity in breast, colon, leukaemia, and renal cancer cell lines. The IC50 predictions as CDK2 inhibitors were greater than 10 µM with type I non-allosteric binding mode. The stability analysis of protein-ligand complex from 125 ns long molecular dynamics simulations showed moderately smooth trajectories and RSMD value around 5 Å for the docked ligands. The binding free energy changes up to -47.65 ± 5.97 kcal/mol from MMGBSA method and -30.33 ± 5.40 kcal/mol from MMPBSA method hinted at the spontaneous nature of the complex formation. Furthermore, geometrical evaluators like RMSF, Rg, SASA, and hydrogen bond count also corroborated with the structural stability of the complexes and the capacity of hit molecules to inhibit the target, indicating its therapeutic potential against cancer. The toxicity and drug-likeness from ADMET predictions suggested experimental verification and that the proposed candidates could be employed for further trials in the development of safer and more effective anticancer drugs.

Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLpro: Insights from the Computational and Experimental Investigations

The emergence of the COVID-19 situation has become a global issue due to the lack of effective antiviral drugs for treatment. Flavonoids are a class of plant secondary metabolites that have antiviral activity against SARS-CoV-2 through inhibition of the main protease (3CLpro). In this study, 22 flavonoids obtained from natural sources and semisynthetic approaches were investigated for their inhibitory activity against SARS-CoV-2 3CLpro, along with cytotoxicity on Vero cells. The protein-ligand interactions were examined using molecular dynamics simulation. Moreover, QSAR analysis was conducted to clarify the structural effects on bioactivity. Accordingly, the in vitro investigation demonstrated that four flavonoids, namely, tectochrysin (7), 6″,6″-dimethylchromeno-[2″,3″:7,8]-flavone (9), panduratin A (19), and genistein (20), showed higher protease inhibitory activity compared to the standard flavonoid baicalein. Finally, our finding suggests that genistein (20), an isoflavone discovered in Millettia brandisiana, has potential for further development as a SARS-CoV-2 3CLpro inhibitor.

Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches

Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Computational and Experimental Drug Repurposing of FDA-Approved Compounds Targeting the Cannabinoid Receptor CB1

The cannabinoid receptor 1 (CB1R) plays a pivotal role in regulating various physiopathological processes, thus positioning itself as a promising and sought-after therapeutic target. However, the search for specific and effective CB1R ligands has been challenging, prompting the exploration of drug repurposing (DR) strategies. In this study, we present an innovative DR approach that combines computational screening and experimental validation to identify potential Food and Drug Administration (FDA)-approved compounds that can interact with the CB1R. Initially, a large-scale virtual screening was conducted using molecular docking simulations, where a library of FDA-approved drugs was screened against the CB1R's three-dimensional structures. This in silico analysis allowed us to prioritize compounds based on their binding affinity through two different filters. Subsequently, the shortlisted compounds were subjected to in vitro assays using cellular and biochemical models to validate their interaction with the CB1R and determine their functional impact. Our results reveal FDA-approved compounds that exhibit promising interactions with the CB1R. These findings open up exciting opportunities for DR in various disorders where CB1R signaling is implicated. In conclusion, our integrated computational and experimental approach demonstrates the feasibility of DR for discovering CB1R modulators from existing FDA-approved compounds. By leveraging the wealth of existing pharmacological data, this strategy accelerates the identification of potential therapeutics while reducing development costs and timelines. The findings from this study hold the potential to advance novel treatments for a range of CB1R -associated diseases, presenting a significant step forward in drug discovery research.

In silico and in vivo anti-angiogenic validation on ethanolic extract of Curcuma longa and curcumin compound in hepatocellular carcinoma through mitogen activated protein kinase expression in male and female wistar rats

Hepatocellular carcinoma (HCC) is the most frequent primary malignancy of the liver. The aim of this study is to evaluate the comparative in silico and in vivo ameliorative potential of the ethanolic extract of Curcuma longa (EECL) in male and female Wistar rats administered N-nitrosodiethylamine-induced hepatocellular carcinoma. The MAPK compound was obtained from a protein data bank (PDB ID: 7AUV) for molecular docking. One hundred and twenty Wistar rats, were randomly selected into twelve groups (n = 5): Group A received regular diets as a basal control; groups B to G were administered 100 mg/kg NDEA twice in two weeks; while groups C to E received 200 mg/kg, 400 mg/kg, and 600 mg/kg of EECL; group F was treated with 200 mg/kg pure curcumin; and group G received 100 mg/kg Sylibon-140. Group H received only 200 mg/kg pure curcumin, and group I received 200 mg/kg of dimethylsulfoxide (DMSO). Groups J, K, and L received 200 mg/kg, 400 mg/kg and 600 mg/kg of EECL. MAPK and AFP mRNA in Wistar rats administered NDEA were upregulated as compared to EECL groups. In conclusion, the in silico and in vitro study validates the mitigating role of ethanolic extract of Curcuma longa and pure curcumin.

Ecotoxicological Evaluation of Bisphenol A and Alternatives: A Comprehensive In Silico Modelling Approach

Bisphenol A (BPA), a compound widely used in industrial applications, has raised concerns due to its environmental impact. As a key component in the manufacture of polycarbonate plastics and epoxy resins used in many consumer products, concerns about potential harm to human health and the environment are unavoidable. This study seeks to address these concerns by evaluating a range of potential BPA alternatives, focusing on their ecotoxicological properties. The research examines 76 bisphenols, including BPA derivatives, using a variety of in silico ecotoxicological models, although it should be noted that these models were not developed exclusively for this particular class of compounds. Consequently, interpretations should be made with caution. The results of this study highlight specific compounds of potential environmental concern and underscore the need to develop more specific models for BPA alternatives that will allow for more accurate and reliable assessment.

Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis

Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R2train = 0.8646, R2test = 0.8864, Q2 = 0.5773) and the predicted inhibitory activity (pIC50) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules.Communicated by Ramaswamy H. Sarma.

New drug approvals for 2022: Synthesis and clinical applications

The U.S. Food and Drug Administration has approved a total of 37 new drugs in 2022, which are composed of 20 chemical entities and 17 biologics. In particular, 20 chemical entities, including 17 small molecule drugs, 1 radiotherapy, and 2 diagnostic agents, provide privileged scaffolds, breakthrough clinical benefits, and a new mechanism of action for the discovery of more potent clinical candidates. The structure-based drug development with clear targets and fragment-based drug development with privileged scaffolds have always been the important modules in the field of drug discovery, which could easily bypass the patent protection and bring about improved biological activity. Therefore, we summarized the relevant valuable information about clinical application, mechanism of action, and chemical synthesis of 17 newly approved small molecule drugs in 2022. We hope this timely and comprehensive review could bring about creative and elegant inspiration on the synthetic methodologies and mechanism of action for the discovery of new drugs with novel chemical scaffolds and extended clinical indications.

Development of Kinase-Centric Drugs: A Computational Perspective

Kinases are prominent drug targets in the pharmaceutical and research community due to their involvement in signal transduction, physiological responses, and upon dysregulation, in diseases such as cancer, neurological and autoimmune disorders. Several FDA-approved small-molecule drugs have been developed to combat human diseases since Gleevec was approved for the treatment of chronic myelogenous leukemia. Kinases were considered "undruggable" in the beginning. Several FDA-approved small-molecule drugs have become available in recent years. Most of these drugs target ATP-binding sites, but a few target allosteric sites. Among kinases that belong to the same family, the catalytic domain shows high structural and sequence conservation. Inhibitors of ATP-binding sites can cause off-target binding. Because members of the same family have similar sequences and structural patterns, often complex relationships between kinases and inhibitors are observed. To design and develop drugs with desired selectivity, it is essential to understand the target selectivity for kinase inhibitors. To create new inhibitors with the desired selectivity, several experimental methods have been designed to profile the kinase selectivity of small molecules. Experimental approaches are often expensive, laborious, time-consuming, and limited by the available kinases. Researchers have used computational methodologies to address these limitations in the design and development of effective therapeutics. Many computational methods have been developed over the last few decades, either to complement experimental findings or to forecast kinase inhibitor activity and selectivity. The purpose of this review is to provide insight into recent advances in theoretical/computational approaches for the design of new kinase inhibitors with the desired selectivity and optimization of existing inhibitors.

Exploring cardiac impact of oral nicotine exposure in a transplantable Neoplasm Mice Model: Insights from biochemical analysis, morphometry, and molecular docking: Chlorella vulgaris green algae support

Nicotine-induced cardiac tissue damage is a concern for cancer patients, but the exact pathogenesis from nicotine oral exposure is unclear. This study was designed to investigate the impact of nicotine and Chlorella vulgaris (Ch. V) on cardiac glutathione homeostasis, inflammatory response, cardiac damage markers, apoptotic proteins and histopathological findings in an experimentally transplantable neoplasm mouse model (Ehrlich ascites carcinoma; EAC). In the in-vivo experiment, the female Swiss mice were divided into four groups: control, Ch.V (100 mg/kg), Nicotine (100 µg/ml/kg), and a combination group ( Nocotine+ Ch.V) for 40 days. Furthermore, in this study,the effects of C. vulgaris components on caspase-3, TNF-α, and IL-1β activity were explored using Molecular Operating Environment (MOE) docking software to ensure its ability to counteract the toxic effects of nicotine. The results indicated that nicotine has induced significant (P < 0.001) cardiopathic alterations in EAC-bearing mice with changes in cardiac tissue enzymes. C. Vulgaris attenuated the nicotine-induced cardiac glutathione inhibition, suppressed the inflammatory response, exerted antiapoptotic effects, mitigated myocardial injury biomarkers, and repaired cellular and tissue damage. Moreover, the molecular docking results revealed the ability of C. vulgaris to bind with interleukin-1 receptor type 1 (IL1R1) and tumor necrosis factor receptor superfamily member 1 A (TNFRSF1A) in the mice tissues, ameliorating apoptosis and inflammatory processes associated with nicotine-induced cardiotoxicity. This study provides a model for understanding nicotine-induced myocardial injury during experimentally transplantable neoplasm. It highlights C. vulgaris as a beneficial food supplement for cancer patients exposed to nicotine orally.

CBPDdb: a curated database of compounds derived from Coumarin-Benzothiazole-Pyrazole

The present article describes the building of a small-molecule web server, CBPDdb, employing R-shiny. For the generation of the web server, three compounds were chosen, namely coumarin, benzothiazole and pyrazole, and their derivatives were curated from the literature. The two-dimensional (2D) structures were drawn using ChemDraw, and the .sdf file was created employing Discovery Studio Visualizer v2017. These compounds were read on the R-shiny app using ChemmineR, and the dataframe consisting of a total of 1146 compounds was generated and manipulated employing the dplyr package. The web server is provided with JSME 2D sketcher. The descriptors of the compounds are obtained using propOB with a filter. The users can download the filtered data in the .csv and .sdf formats, and the entire dataset of a compound can be downloaded in .sdf format. This web server facilitates the researchers to screen plausible inhibitors for different diseases. Additionally, the method used in building the web server can be adapted for developing other small-molecule databases (web servers) in RStudio. Database URL: https://srampogu.shinyapps.io/CBPDdb_Revised/.

Pentafuhalol-B, a Phlorotannin from Brown Algae, Strongly Inhibits the PLK-1 Overexpression in Cancer Cells as Revealed by Computational Analysis

Polo-like kinase-1 (PLK-1) is an essential mitotic serine/threonine (Ser/Thr) kinase that belongs to the Polo-like kinase (PLK) family and is overexpressed in non-small cell lung cancer (NSCLC) via promotion of cell division. Therefore, PLK-1 may act as a promising target for the therapeutic cure of various cancers. Although a variety of anti-cancer drugs, both synthetic and naturally occurring, such as volasertib, onvansertib, thymoquinone, and quercetin, are available either alone or in combination with other therapies, they have limited efficacy, especially in the advanced stages of cancer. To the best of our knowledge, no anticancer agent has been reported from marine algae or microorganisms to date. Thus, the aim of the present study is a high-throughput virtual screening of phlorotannins, obtained from edible brown algae, using molecular docking and molecular dynamic simulation analysis. Among these, Pentafuhalol-B (PtB) showed the lowest binding energy (best of triplicate runs) against the target protein PLK-1 as compared to the reference drug volasertib. Further, in MD simulation (best of triplicate runs), the PtB-PLK-1 complex displayed stability in an implicit water system through the formation of strong molecular interactions. Additionally, MMGBSA calculation (best of triplicate runs) was also performed to validate the PtB-PLK-1 complex binding affinities and stability. Moreover, the chemical reactivity of PtB towards the PLK-1 target was also optimised using density functional theory (DFT) calculations, which exhibited a lower HOMO-LUMO energy gap. Overall, these studies suggest that PtB binds strongly within the pocket sites of PLK-1 through the formation of a stable complex, and also shows higher chemical reactivity than the reference drug volasertib. The present study demonstrated the inhibitory nature of PtB against the PLK-1 protein, establishing its potential usefulness as a small molecule inhibitor for the treatment of different types of cancer.

De novo drug design based on patient gene expression profiles via deep learning

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

Antioxidant capacity of simplified oxygen heterocycles and proposed derivatives by theoretical calculations

CONTEXT

Some structural properties can be involved in the antioxidant capacity of several polyphenol derivatives, among them their simplified structures. This study examines the contribution of simplified structure for the antioxidant capacity of some natural and synthetic antioxidants. The resonance structures were related to the π-type electron system of carbon-carbon double bonds between both phenyl rings. Trans-resveratrol, phenyl-benzofuran, phenyl-indenone, and benzylidene-benzofuranone are the best basic antioxidant templates among the simplified derivatives studied here. Additionally, the stilbene moiety was found on the molecules with the best antioxidant capacity. Furthermore, our investigation suggests that these compounds can be used as antioxidant scaffold for designing and developing of new promising derivatives.

METHODS

To investigate the structure-antioxidant capacity for sixteen simplified natural and proposed derivatives we have employed density functional theory and used Gaussian 09. Our DFT calculations were performed using the B3LYP functional and the 6-31+G(d,p) basis set. All electron transfer mechanisms were investigated by using values of HOMO, ionization potential, energy affinity, stabilization energies, and spin density distributions.

Discovery of an OTUD3 inhibitor for the treatment of non-small cell lung cancer

The ubiquitin-proteasome system (UPS) controls protein turnover, and its dysfunction contributes to human diseases including cancer. Deubiquitinating enzymes (DUBs) remove ubiquitin from proteins to maintain their stability. Inhibition of DUBs could induce the degradation of selected oncoproteins and has therefore become a potential therapeutic strategy for cancer. The deubiquitylase OTUD3 was reported to promote lung tumorigenesis by stabilizing oncoprotein GRP78, implying that inhibition of OTUD3 may be a therapeutic strategy for lung cancer. Here, we report a small-molecule inhibitor of OTUD3 (named OTUDin3) by computer-aided virtual screening and biological experimental verification. OTUDin3 exhibited pronounced antiproliferative and proapoptotic effects by inhibiting deubiquitinating activity of OTUD3 in non-small-cell lung cancer (NSCLC) cell lines. Moreover, OTUDin3 efficaciously inhibited growth of lung cancer xenografts in mice. In summary, our results support OTUDin3 as a potent inhibitor of OTUD3, the inhibition of which may be a promising therapeutic strategy for NSCLC.

Antihyperlipidemic mechanisms of a formula containing Curcuma xanthorrhiza, Sechium edule, and Syzigium polyanthum: In silico and in vitro studies

Herbal medicines are multi-component and can exhibit synergistic effects in the treatment of diseases. Sechium edule, Syzigium polyanthum, and Curcuma xanthorrhiza have been used in traditional medicine to reduce serum lipid levels. However, the molecular mechanism was not described clearly, especially as a mixture. Thus, we performed a network pharmacology study combined with molecular docking to find a rational explanation regarding the molecular mechanisms of this antihyperlipidemic formula. According to the network pharmacology study, we predicted that this extract mixture would act as an antihyperlipidemic agent by modulating several pathways including insulin resistance, endocrine resistance, and AMP-activated protein kinase (AMPK) signaling pathway. Based on the topology parameters, we identified 6 significant targets that play an important role in reducing lipid serum levels: HMG-CoA reductase (HMGCR), peroxisome proliferator-activated receptor alpha (PPARA), RAC-alpha serine/threonine-protein kinase (AKT1), epidermal growth factor receptor (EGFR), matrix metalloproteinase-9 (MMP9), and tumor necrosis factor-alpha (TNF). Meanwhile, 8 compounds: β-sitosterol, bisdesmethoxycurcumin, cucurbitacin D, cucurbitacin E, myricetin, phloretin, quercitrin, and rutin were the compounds with a high degree, indicating that these compounds have a multitarget effect. Our consensus docking study revealed that HMGCR was the only protein targeted by all potential compounds, and rutin was the compound with the best consensus docking score for almost all targets. The in vitro study revealed that the extract combination could inhibit HMGCR with an IC₅₀ value of 74.26 µg/mL, indicating that HMGCR inhibition is one of its antihyperlipidemic mechanisms.

Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop classification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.

Recent Advances in Chemotherapeutics for Leishmaniasis: Importance of the Cellular Biochemistry of the Parasite and Its Molecular Interaction with the Host

Leishmaniasis, a category 1 neglected protozoan disease caused by a kinetoplastid pathogen called Leishmania, is transmitted through dipteran insect vectors (phlebotomine, sand flies) in three main clinical forms: fatal visceral leishmaniasis, self-healing cutaneous leishmaniasis, and mucocutaneous leishmaniasis. Generic pentavalent antimonials have long been the drug of choice against leishmaniasis; however, their success is plagued with limitations such as drug resistance and severe side effects, which makes them redundant as frontline therapy for endemic visceral leishmaniasis. Alternative therapeutic regimens based on amphotericin B, miltefosine, and paromomycin have also been approved. Due to the unavailability of human vaccines, first-line chemotherapies such as pentavalent antimonials, pentamidine, and amphotericin B are the only options to treat infected individuals. The higher toxicity, adverse effects, and perceived cost of these pharmaceutics, coupled with the emergence of parasite resistance and disease relapse, makes it urgent to identify new, rationalized drug targets for the improvement in disease management and palliative care for patients. This has become an emergent need and more relevant due to the lack of information on validated molecular resistance markers for the monitoring and surveillance of changes in drug sensitivity and resistance. The present study reviewed the recent advances in chemotherapeutic regimens by targeting novel drugs using several strategies including bioinformatics to gain new insight into leishmaniasis. Leishmania has unique enzymes and biochemical pathways that are distinct from those of its mammalian hosts. In light of the limited number of available antileishmanial drugs, the identification of novel drug targets and studying the molecular and cellular aspects of these drugs in the parasite and its host is critical to design specific inhibitors targeting and controlling the parasite. The biochemical characterization of unique Leishmania-specific enzymes can be used as tools to read through possible drug targets. In this review, we discuss relevant metabolic pathways and novel drugs that are unique, essential, and linked to the survival of the parasite based on bioinformatics and cellular and biochemical analyses.

Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers

Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor for D-2HG formation in mutant IDH enzymes is a challenging task in cancer research. The mutation in the cytosolic IDH1 enzyme at R132H, especially, may be associated with higher frequency of all types of cancers. So, the present work specifically focuses on the design and screening of allosteric site binders to the cytosolic mutant IDH1 enzyme. The 62 reported drug molecules were screened along with biological activity to identify the small molecular inhibitors using computer-aided drug design strategies. The designed molecules proposed in this work show better binding affinity, biological activity, bioavailability, and potency toward the inhibition of D-2HG formation compare to the reported drugs in the in silico approach.