Computational Multitarget Drug Design

Small-Molecule Inhibitors of Amyloid Beta: Insights from Molecular Dynamics-Part A: Endogenous Compounds and Repurposed Drugs

The amyloid hypothesis is the predominant model of Alzheimer's disease (AD) pathogenesis, suggesting that amyloid beta (Aβ) peptide is the primary driver of neurotoxicity and a cascade of pathological events in the central nervous system. Aβ aggregation into oligomers and deposits triggers various processes, such as vascular damage, inflammation-induced astrocyte and microglia activation, disrupted neuronal ionic homeostasis, oxidative stress, abnormal kinase and phosphatase activity, tau phosphorylation, neurofibrillary tangle formation, cognitive dysfunction, synaptic loss, cell death, and, ultimately, dementia. Molecular dynamics (MD) is a powerful structure-based drug design (SBDD) approach that aids in understanding the properties, functions, and mechanisms of action or inhibition of biomolecules. As the only method capable of simulating atomic-level internal motions, MD provides unique insights that cannot be obtained through other techniques. Integrating experimental data with MD simulations allows for a more comprehensive understanding of biological processes and molecular interactions. This review summarizes and evaluates MD studies from the past decade on small molecules, including endogenous compounds and repurposed drugs, that inhibit amyloid beta. Furthermore, it outlines key considerations for future MD simulations of amyloid inhibitors, offering a potential framework for studies aimed at elucidating the mechanisms of amyloid beta inhibition by small molecules.

Research Progress in In Vitro Screening Techniques for Natural Antithrombotic Medicines

Natural medicines play an indispensable role in treating thrombotic-related diseases and a thorough investigation of their material basis is crucial for medicine development. The rapid advancement in medicine-active component screening technologies has paved new avenues for studying natural medicines, holding significant theoretical and practical value. This review focuses on the application progress of multimodal screening technologies, including high-throughput screening, chip technology, molecular biology methods, fluorescence sensors, and computational biology, in the screening of anticoagulant medicines. The aim is to provide a reference framework for screening and validating active components in natural medicines. The early application of these technologies can swiftly assess the safety and efficacy of medicines, accelerating the medicine development process and reducing the failure rate in clinical trials. Nonetheless, the overall mechanisms of action of natural medicines and the correlation between their chemical components and thrombotic diseases remain challenging areas that require further in-depth exploration and technological innovation.

Anticancer benzimidazole derivatives as inhibitors of epigenetic targets: a review article

Cancer is one of the leading causes of morbidity and mortality worldwide. One of the primary causes of cancer development and progression is epigenetic dysregulation, which is a heritable modification that alters gene expression without changing the DNA sequence. Therefore, targeting these epigenetic changes has emerged as a promising therapeutic strategy. Benzimidazole derivatives have gained attention for their potent epigenetic modulatory effects as they interact with various epigenetic targets, including DNA methyltransferases, histone deacetylases and histone methyltransferases. This review provides a comprehensive overview of benzimidazole derivatives that inhibit different acetylation and methylation reader, writer and eraser epigenetic targets. Herein, we emphasize the therapeutic potential of these compounds in developing targeted, less toxic cancer therapies. Presently, some promising benzimidazole derivatives have entered clinical trials and shown great advancements in the fields of hematological and solid malignancy therapies. Accordingly, we highlight the recent advancements in benzimidazole research as epigenetic agents that could pave the way for designing new multi-target drugs to overcome resistance and improve clinical outcomes for cancer patients. This review can help researchers in designing new anticancer benzimidazole derivatives with better properties.

Design of bisamide inhibitors of the TASK-1 potassium channel in silico

TWIK-related acid-sensitive potassium channel 1 (TASK-1) is expressed ubiquitously across various tissues and plays a significant role in neural activity and anesthetic modulation, making it a crucial target for pharmaceutical research. The high conservation of binding site residues within the TASK family, particularly between TASK-1 and TASK-3, necessitates the development of selective inhibitors for TASK-1. In this study, we utilized a combination of structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches. Initially, several bisamide-centered molecules were designed using the program MolAICal, which is recognized for its ability to generate selective inhibitors containing bisamide segments, and conducted preliminary screening via molecular docking. Subsequently, 3D-QSAR models were developed for 56 bisamide derivatives targeting TASK-1 and TASK-3, with the models exhibiting robust predictive capabilities (TASK-1: Q2 = 0.61, R2pred = 0.84; TASK-3: Q2 = 0.60, R2pred = 0.71). Using these models, the candidate molecules were subjected to activity prediction and subsequent filtering. Ultimately, molecular dynamics simulations, coupled with free energy calculations, pinpointed two bisamide-core molecules with favorable ADMET properties as potential selective inhibitors for TASK-1. Furthermore, molecular dynamics simulations revealed the critical role of the key residue Leu122 in conferring selectivity to bisamide compounds for TASK-1 channel proteins.

Harnessing potential COX-2 engagement for boosting anticancer activity of substituted 2-mercapto-4(3H)-quinazolinones with promising EGFR/VEGFR-2 inhibitory activities

We designed and synthesized new quinazolinone-tethered phenyl thiourea/thiadiazole derivatives 4-26. Based on their structural characteristics, these compounds were proposed to have a multi-target mode of action for their anticancer activities. Using the MTT assay method, antiproliferative effects were assessed against three human cancer cell lines (HEPG-2, MCF-7, and HCT-116). In vitro assessment for enzymatic inhibitory activity of the most active compounds 4, 9 and 20 was done for EGFR, VEGFR-2 and COX-2 as potential targets. The screened compounds showed low micromolar IC50 inhibitory effects against the three targets. Compound 9 demonstrated similar EGFR/VEGFR-2 inhibitory effect to the control drugs and potential inhibitory activity for COX-2 enzyme. In MCF-7 cells, the most active analog 9 caused 41.02% total apoptosis, and arrested the cell cycle at the G2/M phase. Taken as a whole, the findings of this study provide significant new understandings into the relationship between COX inhibition and cancer therapy. Furthermore, the outcomes showcased the encouraging efficacy of these compounds with a multi-target mechanism, making them excellent choices for additional research and development into possible anticancer drug.

Novel dual-targeting inhibitors of NSD2 and HDAC2 for the treatment of liver cancer: structure-based virtual screening, molecular dynamics simulation, and in vitro and in vivo biological activity evaluations

Liver cancer exhibits a high degree of heterogeneity and involves intricate mechanisms. Recent research has revealed the significant role of histone lysine methylation and acetylation in the epigenetic regulation of liver cancer development. In this study, five inhibitors capable of targeting both histone lysine methyltransferase nuclear receptor-binding SET domain 2 (NSD2) and histone deacetylase 2 (HDAC2) were identified using a structure-based virtual screening approach. Notably, DT-NH-1 displayed a potent inhibition of NSD2 (IC50 = 0.08 ± 0.03 μM) and HDAC2 (IC50 = 5.24 ± 0.87 nM). DT-NH-1 also demonstrated a strong anti-proliferative activity against various liver cancer cell lines, particularly HepG2 cells, and exhibited a high level of biological safety. In an experimental xenograft model involving HepG2 cells, DT-NH-1 showed a significant reduction in tumour growth. Consequently, these findings indicate that DT-NH-1 will be a promising lead compound for the treatment of liver cancer with epigenetic dual-target inhibitors.

Novel Coumarin-Steroid/Terpenoid Hybrids: In Vitro and In Silico Anticancer Studies

We have synthesized a series of novel coumarin-steroid and triterpenoid hybrids and evaluated their potential anticancer activity through molecular docking calculations and in vitro antiproliferative assays. These hybrids, derived from estrone and oleanolic acid, were linked via hydrocarbon spacers of varying lengths. Molecular docking studies against human aromatase revealed strong interactions, particularly for compound 11d, which exhibited significant binding affinity (-12.6308 kcal/mol). In vitro assays demonstrated that compounds 6b and 11d had notable antiproliferative effects, with GI50 values of 5.4 and 7.0 μM against WiDr (colon) and HeLa (cervix) cancer cells, respectively. These findings highlight the potential of these hybrids as novel anticancer agents targeting aromatase, warranting further investigation and optimization.

"Several birds with one stone": exploring the potential of AI methods for multi-target drug design

Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies have been adopted to cut costs and speed up the drug development process, serving as promising in silico approaches to efficiently design novel drug candidates targeting various health conditions. Most existing AI-driven drug discovery studies follow a single-target approach which focuses on identifying compounds that bind a target (i.e., one-drug-one-target approach). Polypharmacology is a relatively new concept that takes a systematic approach to search for a compound (or a combination of compounds) that can bind two or more carefully selected protein biomarkers simultaneously to synergistically treat the disease. Recent studies have demonstrated that multi-target drugs offer superior therapeutic potentials compared to single-target drugs. However, it is intuitively thought that searching for multi-target drugs is more challenging than finding single-target drugs. At present, it is unclear how AI approaches perform in designing multi-target drugs. In this paper, we comprehensively investigated the performance of multi-objective AI approaches for multi-target drug design. Our findings are quite counter-intuitive demonstrating that, in fact, AI approaches for multi-target drug design are able to efficiently generate more high-quality novel compounds than the single-target approaches while satisfying a number of constraints.

Discovery of a Novel and Potent Dual-Targeting Inhibitor of ATM and HDAC2 Through Structure-Based Virtual Screening for the Treatment of Testicular Cancer

Purpose

Dual inhibition of ataxia telangiectasia mutated (ATM) and histone deacetylase 2 (HDAC2) may be a potential strategy to improve antitumor efficacy in testicular cancer.

Methods

A combined virtual screening protocol including pharmacophore modeling and molecular docking was used for screening potent dual-target ATM/HDAC2 inhibitors. In order to obtain the optimal lead compound, the dual ATM/HDAC2 inhibitory activity of the screened compounds was further evaluated using enzyme inhibition methods. The binding stability of the optimal compound to the dual targets was verified by molecular dynamics (MD) simulation. MTT assay and in vivo antitumor experiment were performed to validate antitumor efficacy of the optimal compound in testicular cancer.

Results

Here, we successfully discovered six potent dual-target ATM/HDAC2 inhibitors (AMHs 1-6), which exhibited good inhibitory activity against both ATM and HDAC2. Among them, AMH-4 showed strong inhibitory activity against both ATM (IC50 = 1.12 ± 0.03 nM) and HDAC2 (IC50 = 3.04 ± 0.08 nM). MD simulation indicated that AMH-4 binds to ATM and HDAC2 with satisfactory stability. Importantly, AMH-4 had significant antiproliferative activity on human testicular tumor cells, especially NTERA-2 cL.D1 cells, and no inhibitory effect on normal human testicular cells. In vivo experiments exhibited that AMH-4 was more effective than lartesertib and vorinostat in inhibiting the growth of NTERA-2 cL.D1 xenograft tumors with low toxicity.

Conclusion

Overall, these results suggest that AMH-4 is an effective and low toxicity candidate for the treatment of testicular germ cell tumors.

Jatropha Diterpenes: An Updated Review Concerning Their Structural Diversity, Therapeutic Performance, and Future Pharmaceutical Applications

The Euphorbiaceae family is a rich source of bioactive terpenoids. Among its genera, Jatropha is a conspicuous producer of diterpenes and includes approximately 175 species, many of which have medicinal uses. To date, 140 diterpenes from Jatropha (JTDs) have been reported. Given their structural diversity and notable biological activities, this work aims to highlight the pharmaceutical potential of JTDs. To achieve this goal, an extensive literature review was conducted, encompassing studies on structural elucidation through NMR and pharmacological assays, both in vitro and in vivo. Based on 132 selected papers, a thorough discussion is presented on the biosynthesis, extraction, isolation, and structural characterization of JTDs, including a compilation of their 13C NMR chemical shifts. The review also covers their synthetic production and biological effects. Additionally, an in silico analysis predicting the drug-likeness of 141 JTDs was carried out. Notably, the occurrence of macrocyclic diterpenes has doubled in the past decade, and the summary of their NMR data provides a useful resource for future research. Furthermore, 21 distinct pharmacological activities were identified, with potent cytotoxic effects targeting new molecular pathways being particularly significant. Recent advances highlight the contributions of modern approaches in organic synthesis and the pharmacological evaluation of natural products. The drug-likeness analysis identified JTD classes and compounds with favorable physicochemical and ADMET features for pharmaceutical development. In light of these findings, the use of nanotechnology is proposed as a future direction for continued research on JTDs, a fascinating class of natural compounds. This work opens up new avenues for the study of Euphorbiaceae species, particularly the Jatropha genus and its bioactive compounds.

Discovery of Novel 2-Aminopyridine-Based and 2-Aminopyrimidine-Based Derivatives as Potent CDK/HDAC Dual Inhibitors for the Treatment of Refractory Solid Tumors and Hematological Malignancies

Co-inhibition of histone deacetylase (HDAC) and cyclin-dependent kinase (CDK) synergizes to produce enhanced antitumor effects and potentially overcomes the drug resistance. In this work, we discovered a series of novel CDK9/HDACs dual inhibitors. Among them, compound 8e was identified to show potent CDK9 and HDAC1 inhibitory activities, with IC50 values at 88.4 and 168.9 nM, respectively, and exhibited antiproliferative capacities against hematological and solid tumor cells. Meanwhile, 8e showed high selectivity for CDK9 and HDAC1, remarkably induced MV-4-11 cell apoptosis and S cell cycle arrests. Furthermore, 8e possessed a significant antitumor potency with a T/C value of 29.98% in the MV-4-11 xenograft model. Interestingly, a potent FLT3/HDAC dual inhibitor 9e was also identified (FLT3/HDAC1/3 IC50 = 30.4/52.4/14.7 nM) and found to possess powerful apoptosis induction ability in MV-4-11 cell and potent antiproliferative capacities against FLT3 mutant-transformed BaF3 cells. Overall, our work provided valuable lead compounds for dual inhibitors with potent anticancer activity.

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

A novel multitargeted self-assembling peptide-siRNA complex for simultaneous inhibition of SARS-CoV-2-host cell interaction and replication

Effective therapeutics are necessary for managing severe COVID-19 disease despite the availability of vaccines. Small interfering RNA (siRNA) can silence viral genes and restrict SARS-CoV-2 replication. Cell-penetrating peptides is a robust method for siRNA delivery, enhancing siRNA stability and targeting specific receptors. We developed a peptide HE25 that blocks SARS-CoV-2 replication by various mechanisms, including the binding of multiple receptors involved in the virus's internalization, such as ACE2, integrins and NRP1. HE25 not only acts as a vehicle to deliver the SARS-CoV-2 RNA-dependent RNA polymerase siRNA into cells but also facilitates their internalization through endocytosis. Once inside endosomes, the siRNA is released into the cytoplasm through the Histidine-proton sponge effect and the selective cleavage of HE25 by cathepsin B. These mechanisms effectively inhibited the replication of the ancestral SARS-CoV-2 and the Omicron variant BA.5 in vitro. When HE25 was administered in vivo, either by intravenous injection or inhalation, it accumulated in lungs, veins and arteries, endothelium, or bronchial structure depending on the route. Furthermore, the siRNA/HE25 complex caused gene silencing in lung cells in vitro. The SARS-CoV-2 siRNA/HE25 complex is a promising therapeutic for COVID-19, and a similar strategy can be employed to combat future emerging viral diseases.

A review on computational studies and bioinformatics analysis of potential drugs against monkeypox virus

Monkeypox, a viral disease that is caused by monkeypox virus and occurs mainly in central and western Africa. However, recently it is spreading worldwide and took the focus of the scientific world towards it. Therefore, we made an attempt to cluster all the related information that may make it easy for the researchers to get the information easily and carry out their research smoothly to find prophylaxis against this emerging virus. There are very few researches found available on monkeypox. Almost all the studies were focused on smallpox virus and the recommended vaccines and therapeutics for monkeypox virus were originally developed for smallpox virus. Though these are recommended for emergency cases, they are not fully effective and specific against monkeypox. For this, here we also took the help of bioinformatics tools to screen potential drug candidates against this growing burden. Some potential antiviral plant metabolites, inhibitors and available drugs were scrutinized that can block the essential survival proteins of this virus. All the compounds Amentoflavone, Pseudohypericin, Adefovirdipiboxil, Fialuridin, Novobiocin and Ofloxacin showed elite binding efficiency with suitable ADME properties and Amentoflavone and Pseudohypericin showed stability in MD simulation study indicating their potency as probable drugs against this emerging virus.Communicated by Ramaswamy H. Sarma.

Developing a Semi-Supervised Approach Using a PU-Learning-Based Data Augmentation Strategy for Multitarget Drug Discovery

Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration of machine learning (ML) and deep learning (DL) in drug discovery has revolutionized virtual screening. This study investigates the synergy between ML/DL methodologies, molecular representations, and data augmentation strategies. Notably, we found that SVM can match or even surpass the performance of state-of-the-art DL methods. However, conventional data augmentation often involves a trade-off between the true positive rate and false positive rate. To address this, we introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, a novel semi-supervised learning framework. By leveraging ensemble SVM classifiers trained on resampled bags containing positive, negative, and unlabeled data, our approach is capable of managing false positive rates while maintaining high recall rates. We applied this method to the identification of multitarget-directed ligands (MTDLs), where high recall rates are critical for compiling a list of interaction candidate compounds. Case studies demonstrate that NAPU-bagging SVM can identify structurally novel MTDL hits for ALK-EGFR with favorable docking scores and binding modes, as well as pan-agonists for dopamine receptors. The NAPU-bagging SVM methodology should serve as a promising avenue to virtual screening, especially for the discovery of MTDLs.

Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

Evaluation of the interaction between potent small molecules against the Nipah virus Glycoprotein in Malaysia and Bangladesh strains, accompanied by the human Ephrin-B2 and Ephrin-B3 receptors; a simulation approach

Malaysia reported the first human case of Nipah virus (NiV) in late September 1998 with encephalitis and respiratory symptoms. As a result of viral genomic mutations, two main strains (NiV-Malaysia and NiV-Bangladesh) have spread around the world. There are no licensed molecular therapeutics available for this biosafety level 4 pathogen. NiV attachment glycoprotein plays a critical role in viral transmission through its human receptors (Ephrin-B2 and Ephrin-B3), so identifying small molecules that can be repurposed to inhibit them is crucial to developing anti-NiV drugs. Consequently, in this study annealing simulations, pharmacophore modeling, molecular docking, and molecular dynamics were used to evaluate seven potential drugs (Pemirolast, Nitrofurantoin, Isoniazid Pyruvate, Eriodictyol, Cepharanthine, Ergoloid, and Hypericin) against NiV-G, Ephrin-B2, and Ephrin-B3 receptors. Based on the annealing analysis, Pemirolast for efnb2 protein and Isoniazid Pyruvate for efnb3 receptor were repurposed as the most promising small molecule candidates. Furthermore, Hypericin and Cepharanthine, with notable interaction values, are the top Glycoprotein inhibitors in Malaysia and Bangladesh strains, respectively. In addition, docking calculations revealed that their binding affinity scores are related to efnb2-pem (- 7.1 kcal/mol), efnb3-iso (- 5.8 kcal/mol), gm-hyp (- 9.6 kcal/mol), gb-ceph (- 9.2 kcal/mol). Finally, our computational research minimizes the time-consuming aspects and provides options for dealing with any new variants of Nipah virus that might emerge in the future.

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

Research progress of DDR1 inhibitors in the treatment of multiple human diseases

Discoidin domain receptor 1 (DDR1) is a collagen-activated receptor tyrosine kinase (RTK) and plays pivotal roles in regulating cellular functions such as proliferation, differentiation, invasion, migration, and matrix remodeling. DDR1 is involved in the occurrence and progression of many human diseases, including cancer, fibrosis, and inflammation. Therefore, DDR1 represents a highly promising therapeutic target. Although no selective small-molecule inhibitors have reached clinical trials to date, many molecules have shown therapeutic effects in preclinical studies. For example, BK40143 has demonstrated significant promise in the therapy of neurodegenerative diseases. In this context, our perspective aims to provide an in-depth exploration of DDR1, encompassing its structure characteristics, biological functions, and disease relevance. Furthermore, we emphasize the importance of understanding the structure-activity relationship of DDR1 inhibitors and highlight the unique advantages of dual-target or multitarget inhibitors. We anticipate offering valuable insights into the development of more efficacious DDR1-targeted drugs.

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

Potentiality of bioactive compounds as inhibitor of M protein and F protein function of human respiratory syncytial virus

The human respiratory syncytial virus (RSV) creates a pandemic every year in several countries in the world. Lack of target therapeutics and absence of vaccines have prompted scientists to create novel vaccines or small chemical treatments against RSV's numerous targets. The matrix (M) protein and fusion (F) glycoprotein of RSV are well characterized and attractive drug targets. Five bioactive compounds from Alnus japonica (Thunb.) Steud. were taken into consideration as lead compounds. Drug-likeness characters of them showed the drugs are non-toxic and non-mutagenic and mostly lipophobic. Molecular docking reveals that all bioactive compounds have better binding and better inhibitory effect than ribavirin which is currently used against RSV. Praecoxin A appeared as the best lead compound between them. It creates 7 different types of bonds with amino acids of M protein and 5 different types of bonds with amino acids of F protein. Van der Waals interactions highly influenced the binding energies. Molecular dynamic simulations represent the non-deviated and less fluctuating nature of praecoxin A. Principal Component Analysis showed praecoxin A complex with RSV matrix protein is more stable than ribavirin complex. This study will help to develop a new drug to inhibit RSV. All ligands were minimized through semi-empirical PM3 process with MOPAC. Toxicity was tested by ProTox-II server. Molecular docking studies were carried out using AutoDock 4.2. Molecular dynamics simulations for 100 ns were carried out through GROMACS 5.12 MD and GROMOS96 43a1 force field. The graphs were produced by GROMACS's XMGrace program.

Graphical abstract

Prioritization of bioactive compounds envisaging yohimbine as a multi targeted anticancer agent: insight from molecular docking and molecular dynamics simulation

Recently, multi-targeted drugs have attracted much attention in cancer therapy where several therapeutic proteins are targeted by a single agent. Using the published scientific literature, we selected sixteen well-known anticancer targets and seven potential phytobioactive chemicals to find a multitargeted compound by screening through molecular docking. The feasible protein-ligand interaction was further predicted by protein-ligand interaction analysis and molecular dynamic simulation. The phytochemical yohimbine exhibited the lowest docking score in the range of -8.3 to -10.0 kcal/mol over other ligands with all the studied protein targets. Molecular interaction data also revealed the feasible binding of yohimbine with all targets. Moreover, the molecular simulation data also confirmed the stability of protein-ligand complexes with three most scored targets viz. ERK2, PARP1 and PIK3α. Based on our results, yohimbine seems to be the most potent compound out of those selected compounds and can be considered as effective lead molecule against the studied target proteins.Communicated by Ramaswamy H. Sarma.

New pyrazolyl-thiazolidinone/thiazole derivatives as celecoxib/dasatinib analogues with selective COX-2, HER-2 and EGFR inhibitory effects: design, synthesis, anti-inflammatory/anti-proliferative activities, apoptosis, molecular modelling and ADME studies

Two new series of pyrazolyl-thiazolidinone/thiazole derivatives 16a-b and 18a-j were synthesised, merging the scaffolds of celecoxib and dasatinib. Compounds 16a, 16b and 18f inhibit COX-2 with S.I. 134.6, 26.08 and 42.13 respectively (celecoxib S.I. = 24.09). Compounds 16a, 16b, 18c, 18d and 18f inhibit MCF-7 with IC50 = 0.73-6.25 μM (dasatinib IC50 = 7.99 μM) and (doxorubicin IC50 = 3.1 μM) and inhibit A549 with IC50 = 1.64-14.3 μM (dasatinib IC50 = 11.8 μM and doxorubicin IC50 = 2.42 μM) with S.I. (F180/MCF7) of 33.15, 7.13, 18.72, 13.25 and 8.28 respectively higher than dasatinib (4.03) and doxorubicin (3.02) and S.I. (F180/A549) of 14.75, 12.96, 4.16, 7.07 and 18.88 respectively higher than that of dasatinib (S.I. = 2.72) and doxorubicin (S.I = 3.88). Derivatives 16a, 18c, 18d, 18f inhibit EGFR and HER-2 IC50 for EGFR of 0.043, 0.226, 0.388, 0.19 μM respectively and for HER-2 of 0.032, 0.144, 0.195, 0.201 μM respectively.

Discovery of first novel sigma/HDACi dual-ligands with a potent in vitro antiproliferative activity

Designing and discovering compounds for dual-target inhibitors is challenging to synthesize new, safer, and more efficient drugs than single-target drugs, especially to treat multifactorial diseases such as cancer. The simultaneous regulation of multiple targets might represent an alternative synthetic approach to optimize patient compliance and tolerance, minimizing the risk of target-based drug resistance due to the modulation of a few targets. To this end, we conceived for the first time the design and synthesis of dual-ligands σR/HDACi to evaluate possible employment as innovative candidates to address this complex disease. Among all synthesized compounds screened for several tumoral cell lines, compound 6 (Kiσ1R = 38 ± 3.7; Kiσ2R = 2917 ± 769 and HDACs IC50 = 0.59 µM) is the most promising candidate as an antiproliferative agent with an IC50 of 0.9 µM on the HCT116 cell line and no significant toxicity to normal cells. Studies of molecular docking, which confirmed the affinity over σ1R and a pan-HDACs inhibitory behavior, support a possible balanced affinity and activity between both targets.

Multitarget antibacterial drugs: An effective strategy to combat bacterial resistance

The rise of antibiotic resistance and the decrease in the discovery of new antibiotics have caused a global health crisis. Of particular concern is the fact that despite efforts to develop new antibiotics, drug discovery is unable to keep up with the rapid development of resistance. This ongoing crisis highlights the fact that single-target drugs may not always exhibit satisfactory therapeutic effects and are prone to target mutations and resistance due to the complexity of bacterial mechanisms. Retrospective studies have shown that most successful antibiotics have multiple targets. Compared with single-target drugs, successfully designed multitarget drugs can simultaneously regulate multiple targets to reduce resistance caused by single-target mutations or expression changes. In addition to a lower risk of drug-drug interactions, multitarget drugs show superior pharmacokinetics and higher patient compliance compared with combination therapies. Therefore, to reduce resistance, many efforts have been made to discover and design multitarget drugs with different chemical structures and functions. Although there have been numerous studies on how to develop drugs and slow down the development of drug resistance, the reduction of bacterial resistance by multitarget antibacterial drugs has not received widespread attention and is rarely mentioned in the peer-reviewed literature. This review summarises the development of antibiotic resistance and the mechanisms proposed for its emergence, examines the potential of multitarget drugs as an effective strategy to slow the development of resistance, and discusses the rationale for multitarget drug therapy. We also describe multitarget antibacterial compounds with the potential to reduce drug resistance and the available strategies to develop multitarget drugs.

Dual-target drugs against Leishmania donovani for potential novel therapeutics

Antioxidant defense mechanisms are important for a parasite to overcome oxidative stress and survive within host macrophage cells. Mitochondrial iron superoxide dismutase A (FeSODA) and trypanothione reductase (TR) are critical enzymes in the antioxidant defense mechanism of Leishmania donovani. FeSODA is responsible for neutralizing reactive oxygen species in mitochondria, while TR is responsible for reducing trypanothione, the molecules that help the parasite fight oxidative stress in Leishmania. In this study, we used multitarget ligands to inhibit both the FeSODA and TR enzymes. We combined structure-based drug design using virtual screening approach to find inhibitors against both the targets. The ZINC15 database of biogenic compounds was utilized to extract drugs-like molecules against leishmaniasis. The compounds were screened by standard precision (SP) and extra precision (XP) docking methods. Two compounds, ZINC000008876351 and ZINC000253403245, were selected based on molecular docking based on the binding affinity for both the targets. The screened molecules ZINC000008876351 and ZINC000253403245 showed strong hydrogen bonding with the target proteins according to the Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) techniques. These two compounds were also experimentally investigated on promastigotes stage of L. donovani. Under in vitro condition, the compounds show inhibitory effects on L. donovani promastigotes with IC50 values of 24.82 ± 0.61 µM for ZINC000008876351 and 7.52 ± 0.17 µM for ZINC000253403245. Thus, the screened compounds seem to have good potential as therapeutic candidates for leishmaniasis.

Predicting the molecular mechanism-driven progression of breast cancer through comprehensive network pharmacology and molecular docking approach

Identification of key regulators is a critical step toward discovering biomarker that participate in BC. A gene expression dataset of breast cancer patients was used to construct a network identifying key regulators in breast cancer. Overexpressed genes were identified with BioXpress, and then curated genes were used to construct the BC interactome network. As a result of selecting the genes with the highest degree from the BC network and tracing them, three of them were identified as novel key regulators, since they were involved at all network levels, thus serving as the backbone. There is some evidence in the literature that these genes are associated with BC. In order to treat BC, drugs that can simultaneously interact with multiple targets are promising. When compared with single-target drugs, multi-target drugs have higher efficacy, improved safety profile, and are easier to administer. The haplotype and LD studies of the FN1 gene revealed that the identified variations rs6707530 and rs1250248 may both cause TB, and endometriosis respectively. Interethnic differences in SNP and haplotype frequencies might explain the unpredictability in association studies and may contribute to predicting the pharmacokinetics and pharmacodynamics of drugs using FN1.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature

INTRODUCTION

Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations.

AREAS COVERED

The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process.

EXPERT OPINION

Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Consensus Ensemble Multitarget Neural Network Model of Anxiolytic Activity of Chemical Compounds and Its Use for Multitarget Pharmacophore Design

A classification consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready been tested for anxiolytic activity and were structurally similar to the 15 studied nitrogen-containing heterocyclic chemotypes. Seventeen biotargets relevant to anxiolytic activity were selected, taking into account the possible effect on them of the derivatives of these chemotypes. The generated model consistedof three ensembles of artificial neural networks for predicting three levels of anxiolytic activity, with sevenneural networks in each ensemble. A sensitive analysis of neurons in an ensemble of neural networks for a high level of activity made it possible to identify four biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut, which were the most significant for the manifestation of the anxiolytic effect. For these four key biotargets for 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives, eight monotarget pharmacophores of high anxiolytic activity were built. Superposition of monotarget pharmacophores built two multitarget pharmacophores of high anxiolytic activity, reflecting the universal features of interaction 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives with the most significant biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut.

Trichoderma asperellum Extract Isolated from Brazil Nuts (Bertholletia excelsa BONPL): In Vivo and In Silico Studies on Melanogenesis in Zebrafish

Endophytic fungi are those that present part of their life cycle in healthy tissues of different plant hosts in symbiosis without causing harm. At the same time, fungus-plant symbiosis makes it possible for microorganisms to synthesize their own bioactive secondary metabolites while in the stationary stage. To accomplish this, the endophytic fungus Trichoderma asperellum was isolated from Bertholletia excelsa (Brazil nut) almonds. The fungus was cultivated and extracted with ethyl acetate, obtaining AM07Ac. Then, using HPTLC (High-performance thin-layer chromatography) and nuclear magnetic resonance (1H NMR), β-amyrin, kaempferol, and brucine were identified as major compounds. Further in vivo assays in zebrafish demonstrated the activity of AM07Ac on melanogenesis by producing a concentration-response inhibitory effect, which, through an in silico study, proved to be related to the noted major compounds known to inhibit tyrosinase activity. The inhibition of tyrosinase prevents melanin accumulation in skin. Therefore, these results imply the importance of investigating microorganisms and their pharmacological activities, in particular the endophytic fungus Trichoderma asperellum as a generator of active metabolites for melanogenesis modulation.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

Threat of respiratory syncytial virus infection knocking the door: a proposed potential drug candidate through molecular dynamics simulations, a future alternative

The discovery of antiviral approaches to prevent or cure respiratory syncytial virus (RSV) infections is critical, particularly because RSV is one of the most common causes of infant respiratory problems. There is currently no approved vaccination available to treat RSV infections. FDA has approved the drug ribavirin, but it is not sufficient to treat RSV. This work aimed to find and study in silico anti-RSV drugs that target matrix protein and nucleoprotein. In this study, we have identified five drug candidates that had better binding energies than ribavirin. Garenoxacin appeared as top lead compounds between them. AutoDock Vina was used to execute molecular docking of a library of chosen chemicals. The high-score compound was then confirmed using the Maestro 12.3 module's molecular dynamics simulation and the binding energies derived using Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA). Comparative molecular dynamics simulations revealed that garenoxacin has better stability and high residue contacts with high binding affinity than ribavirin. This study showed garenoxacin could prevent RSV infection better than ribavirin. In pursuing a more effective RSV control drug, additional research into these chemicals in vitro and in vivo is essential.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Molecular docking, ADMET and molecular dynamics simulation revealed metralindole as a multitargeted inhibitor for division kinases

Lung cancer is the most common type of cancer in the world, and alone, in 2020, almost 2.21 million new cases were diagnosed, with 1.80 million deaths, and are increasing daily. Non-small cell lung (NSCLC) is the primary type of lung cancer, predominantly forms around 80% of cases compared to small cell carcinoma, and about 75% of patients are already in an advanced state when diagnosed. Despite notable advances in early diagnosis and treatment, the five-year survival rate for NSCLC is not encouraging. Therefore, it is crucial to investigate the molecular causes of non-small cell lung cancer to create more efficient therapeutic approaches. Lung cancer showed a more significant and persistent binding affinity and energy landscape with the target CDK2 staurosporine and FGF receptor-1. In this study, we have picked two essential target proteins, human cyclin-dependent kinase-2 and Human Protein Kinase CK2 Holoenzyme and screened the entire prepared DrugBank prepared library of 1,55,888 compounds and identified 2-(2-methyl-5-nitroimidazole-1-yl) ethanol (Metralindole) as a major inhibitor. Metralindole has displayed high docking scores of -5.159 Kcal/mol and -5.99 Kcal/mol with good hydrogen bonding and other bonding topologies such as van der Waals force, and ADMET results shown excellent bioavailability, outstanding solubility, no side effects, and toxicity. The molecular dynamics simulation for 100ns in a water medium confirmed the compound's stability and interaction pattern with the lowest deviation and fluctuations. Our in-silico study suggests Metralindole, an experimental compound, can effectively cure lung cancer. Further, the experimental validation of the compound is a must before any prescription.

Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19

SARS-CoV-2 has posed serious threat to the health and has inflicted huge costs in the world. Discovering potent compounds is a critical step to inhibit coronavirus. 3CL^pro and RdRp are the most conserved targets associated with COVID-19. In this study, three-dimensional pharmacophore modeling, scaffold hopping, molecular docking, structure-based virtual screening, QSAR-based ADMET predictions and molecular dynamics analysis were used to identify inhibitors for these targets. Binding free energies estimated by molecular docking for each ligand in different binding sites of RdRp were used to predict the active site. Previously reported active 3CL^pro and RdRp inhibitors were used to build a pharmacophore model to develop different scaffolds. Structure-based simulations and pharmacophore modeling based on Hip Hop algorithm converged in a state that suggest hydrogen bond acceptor and donor features have a critical role in the two binding sites. Further validations indicated that the best pharmacophore model has fairly good correlation values compared with approved inhibitors. Structure-based simulation results approved that GLu166 and Gln189 in 3CL^pro and Lys551 and Glu811 in RdRp, are critical residues for dual activities. Ten compounds were extracted from pharmacophore-based virtual screening in six databases. The results, gained by repurposing approach, suggest the effectiveness of these ten compounds with different scaffolds as possible inhibitors of the two targets. Some quinoline-based hybrid derivatives also were designed. QSAR descriptors plot predicted that the scaffolds have had accepted pharmacokinetic profiles. Multiple molecular dynamics simulations in 100 ns and MM/PBSA studies of some reference inhibitors and the novel compounds in complex with both targets demonstrated stable complexes and confirmed the interaction modes. Based on different computational methods, COVID-19 multi-target inhibitors are proposed. Communicated by Ramaswamy H. Sarma.

Toward the Discovery of SARS-CoV-2 Main Protease Inhibitors: Exploring Therapeutic Potentials of Evodiamine and Its Derivatives, Virtual Screening, Molecular Docking, and Molecular Dynamic Studies

Continuous scientific research is necessary to help in the discovery of new promising remedies for the treatment of COVID-19, caused by the SARS-CoV-2 virus. This current research was aimed at identifying potential novel inhibitors of the SARS-CoV-2 main protease, which represents one of the most important targets in the viral life cycle. Protein data bank file ID: 7JQ2 was used containing the co-crystallized inhibitor MPI5 with the Main protease. A virtual screening process for natural evodiamine compounds was performed through absorption, distribution, metabolism, elimination, and toxicity studies, and the promising hits were docked into the binding site of the enzyme. 13-(4-Chlorobenzoyl)-10-hydroxy-14-methyl-8,13,13 b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1- b]-quinazolin-5(7 H)-one (29) interacted favorably with the enzyme; it showed high similarity to MPI5. Molecular dynamic simulations for 29 proved the stability of its binding to SARS-CoV-2 protease over 100 ns; subsequent MMGBSA analysis also supported this principle. Furthermore, 29 elucidated higher limiting action on enzymatic behavior throughout the whole process when compared to MPI5. This provides sufficient evidence for the potential of evodiamine compounds in modern antiviral research, especially compound 29, against the modern COVID-19 pandemic.

Metabolomics and Network Pharmacology in the Exploration of the Multi-Targeted Therapeutic Approach of Traditional Medicinal Plants

Metabolomic is generally characterized as a comprehensive and the most copious analytical technique for the identification of targeted and untargeted metabolite diversity in a biological system. Recently, it has exponentially been used for phytochemical analysis and variability among plant metabolites, followed by chemometric analysis. Network pharmacology analysis is a computational technique used for the determination of multi-mechanistic and therapeutic evaluation of chemicals via interaction with the genomes involved in targeted or untargeted diseases. In considering the facts, the present review aims to explore the role of metabolomics and network pharmacology in the scientific validation of therapeutic claims as well as to evaluate the multi-targeted therapeutic approach of traditional Indian medicinal plants. The data was collected from different electronic scientific databases such as Google Scholar, Science Direct, ACS publication, PubMed, Springer, etc., using different keywords such as metabolomics, techniques used in metabolomics, chemometric analysis, a bioinformatic tool for drug discovery and development, network pharmacology, methodology and its role in biological evaluation of chemicals, etc. The screened articles were gathered and evaluated by different experts for their exclusion and inclusion in the final draft of the manuscript. The review findings suggest that metabolomics is one of the recent most precious and effective techniques for metabolite identification in the plant matrix. Various chemometric techniques are copiously used for metabolites discrimination analysis hence validating the unique characteristic of herbal medicines and their derived products concerning their authenticity. Network pharmacology remains the only option for the unique and effective analysis of hundreds of chemicals or metabolites via genomic interaction and thus validating the multi-mechanistic and therapeutic approach to explore the pharmacological aspects of herbal medicines for the management of the disease.

In Combo Studies for the Optimization of 5-Aminoanthranilic Acid Derivatives as Potential Multitarget Drugs for the Management of Metabolic Syndrome

Metabolic syndrome is a set of risk factors that consist of abdominal obesity, arterial hypertension, alterations in the lipid profile, and hyperglycemia. The current therapeutic strategy includes polypharmacy, using three or more drugs to control each syndrome component. However, this approach has drawbacks that could lead to therapeutic failure. Multitarget drugs are molecules with the ability to act on different targets simultaneously and are an attractive alternative for treating complex diseases such as metabolic syndrome. Previously, we identified a triamide derivative of 5-aminoanthranilic acid that exhibited hypoglycemic, hypolipemic, and antihypertensive activities simultaneously. In the present study, we report the synthesis and in combo evaluation of new derivatives of anthranilic acid, intending to identify the primary structural factors that improve the activity over metabolic syndrome-related parameters. We found that substitution on position 5, incorporation of 3,4-dimethoxyphenyl substituents, and having a free carboxylic acid group lead to the in vitro inhibition of HMG-CoA reductase, and simultaneously the diminution of the serum levels of glucose, triglycerides, and cholesterol in a diet-induced in vivo model.

The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach

Four compounds, hippacine, 4,2'-dihydroxy-4'-methoxychalcone, 2',5'-dihydroxy-4-methoxychalcone, and wighteone, were selected from 4924 African natural metabolites as potential inhibitors against SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT) of the PLpro through molecular fingerprints and structural similarity studies. Followingly, to examine the binding of the selected metabolites with the PLpro (molecular docking. Further, to confirm this binding through molecular dynamics simulations. Finally, in silico ADMET and toxicity studies were carried out to prefer the most convenient compounds and their drug-likeness. The obtained results could be a weapon in the battle against COVID-19 via more in vitro and in vivo studies.

Insights into performance evaluation of compound-protein interaction prediction methods

MOTIVATION

Machine-learning-based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent improvements in predictive accuracy, we have observed a number of fundamental issues in experiment design that produce overoptimistic estimates of model performance.

RESULTS

We systematically analyze the impact of several factors affecting generalization performance of CPI predictors that are overlooked in existing work: (i) similarity between training and test examples in cross-validation; (ii) synthesizing negative examples in absence of experimentally verified negative examples and (iii) alignment of evaluation protocol and performance metrics with real-world use of CPI predictors in screening large compound libraries. Using both state-of-the-art approaches by other researchers as well as a simple kernel-based baseline, we have found that effective assessment of generalization performance of CPI predictors requires careful control over similarity between training and test examples. We show that, under stringent performance assessment protocols, a simple kernel-based approach can exceed the predictive performance of existing state-of-the-art methods. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization in comparison to more sophisticated strategies used in existing studies. Our analyses indicate that using proposed experiment design strategies can offer significant improvements for CPI prediction leading to effective target compound screening for drug repurposing and discovery of putative chemical ligands of SARS-CoV-2-Spike and Human-ACE2 proteins.

AVAILABILITY AND IMPLEMENTATION

Code and supplementary material available at https://github.com/adibayaseen/HKRCPI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach

As an extension of our research against COVID-19, a multiphase in silico approach was applied in the selection of the three most common inhibitors (Glycyrrhizoflavone (76), Arctigenin (94), and Thiangazole (298)) against papain-like protease, PLpro (PDB ID: 4OW0), among 310 metabolites of natural origin. All compounds of the exam set were reported as antivirals. The structural similarity between the examined compound set and S88, the co-crystallized ligand of PLpro, was examined through structural similarity and fingerprint studies. The two experiments pointed to Brevicollin (28), Cryptopleurine (41), Columbamine (46), Palmatine (47), Glycyrrhizoflavone (76), Licochalcone A (87), Arctigenin (94), Termilignan (98), Anolignan B (99), 4,5-dihydroxy-6″-deoxybromotopsentin (192), Dercitin (193), Tryptanthrin (200), 6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole (211), Thiangazole (298), and Phenoxan (300). The binding ability against PLpro was screened through molecular docking, disclosing the favorable binding modes of six metabolites. ADMET studies expected molecules 28, 76, 94, 200, and 298 as the most favorable metabolites. Then, molecules 76, 94, and 298 were chosen through in silico toxicity studies. Finally, DFT studies were carried out on glycyrrhizoflavone (76) and indicated a high level of similarity in the molecular orbital analysis. The obtained data can be used in further in vitro and in vivo studies to examine and confirm the inhibitory effect of the filtered metabolites against PLpro and SARS-CoV-2.

Designing Dual Inhibitors of Autotaxin-LPAR GPCR Axis

The ATX-LPA-LPAR1 signaling pathway plays a universal role in stimulating diverse cellular responses, including cell proliferation, migration, survival, and invasion in almost every cell type. The ATX-LPAR1 axis is linked to several metabolic and inflammatory diseases including cancer, fibrosis, and rheumatoid arthritis. Numerous selective ATX or LPAR1 inhibitors have been developed and so far, their clinical efficacy has only been evaluated in idiopathic pulmonary fibrosis. None of the ATX and LPAR1 inhibitors have advanced to clinical trials for cancer and rheumatoid arthritis. Nonetheless, several research groups, including ours, have shown considerable benefit of simultaneous ATX and LPAR1 inhibition through combination therapy. Recent research suggests that dual-targeting therapies are superior to combination therapies that use two selective inhibitors. However, limited reports are available on ATX-LPAR1 dual inhibitors, potentially due to co-expression of multiple different LPARs with close structural similarities at the same target. In this review, we discuss rational design and future directions of dual ATX-LPAR1 inhibitors.

Potential Multifunctional Bioactive Compounds from Dysosma versipellis Explored by Bioaffinity Ultrafiltration-HPLC/MS with Topo I, Topo II, COX-2 and ACE2

Background

Dysosma versipellis (D. versipellis) has been traditionally used as a folk medicine for ages. However, the specific phytochemicals responsible for their correlated anti-inflammatory, anti-proliferative and antiviral activities remain unknown.

Purpose

This study aimed to explore the specific active components in D. versipellis responsible for its potential anti-inflammatory, anti-proliferative, and antiviral effects, and further elucidate the corresponding mechanisms of action.

Methods

Bioaffinity ultrafiltration coupled to liquid chromatography–mass spectrometry (UF-LC/MS) was firstly hired to fast screen for the anti-inflammatory, anti-proliferative and antiviral compounds from rhizomes of D. versipellis, and then further validation was conducted using in vitro inhibition assays and molecular docking.

Results

A total of 12, 12, 9 and 12 phytochemicals with considerable affinities to Topo I, Topo II, COX-2 and ACE2 were fished out, respectively. The anti-proliferative assay in vitro indicated that podophyllotoxin and quercetin exhibited comparably strong inhibitory rates on A549 and HT-29 cells compared with 5-FU and etoposide. Meanwhile, kaempferol displayed prominent dose-dependent inhibition against COX-2 with IC₅₀ value at 0.36 ± 0.02 μM lower than indomethacin at 0.73 ± 0.07 μM. Furthermore, quercetin exerted stronger inhibitory effect against ACE2 with IC₅₀ value at 104.79 ± 8.26 μM comparable to quercetin 3-O-glucoside at 135.25 ± 6.54 μM.

Conclusion

We firstly showcased an experimental investigation on the correlations between bioactive phytochemicals of D. versipellis and their multiple drug targets reflecting its potential pharmacological activities, and further constructed a multi-target and multi-component network to decipher its empirical traditional applications. It could not only offer a reliable and valuable experimental basis to better comprehend the curative effects of D. versipellis but also provide more new insights and strategies for other traditional medicinal plants.

Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine

Continuing our antecedent work against COVID-19, a set of 5956 compounds of traditional Chinese medicine have been virtually screened for their potential against SARS-CoV-2 helicase (PDB ID: 5RMM). Initially, a fingerprint study with VXG, the ligand of the target enzyme, disclosed the similarity of 187 compounds. Then, a molecular similarity study declared the most similar 40 compounds. Subsequently, molecular docking studies were carried out to examine the binding modes and energies. Then, the most appropriate 26 compounds were subjected to in silico ADMET and toxicity studies to select the most convenient inhibitors to be: (1R,2S)-ephedrine (57), (1R,2S)-norephedrine (59), 2-(4-(pyrrolidin-1-yl)phenyl)acetic acid (84), 1-phenylpropane-1,2-dione (195), 2-methoxycinnamic acid (246), 2-methoxybenzoic acid (364), (R)-2-((R)-5-oxopyrrolidin-3-yl)-2-phenylacetic acid (405), (Z)-6-(3-hydroxy-4-methoxystyryl)-4-methoxy-2H-pyran-2-one (533), 8-chloro-2-(2-phenylethyl)-5,6,7-trihydroxy-5,6,7,8-tetrahydrochromone (637), 3-((1R,2S)-2-(dimethylamino)-1-hydroxypropyl)phenol (818), (R)-2-ethyl-4-(1-hydroxy-2-(methylamino)ethyl)phenol (5159), and (R)-2-((1S,2S,5S)-2-benzyl-5-hydroxy-4-methylcyclohex-3-en-1-yl)propane-1,2-diol (5168). Among the selected 12 compounds, the metabolites, compound 533 showed the best docking scores. Interestingly, the MD simulation studies for compound 533, the one with the highest docking score, over 100 ns showed its correct binding to SARS-CoV-2 helicase with low energy and optimum dynamics. Finally, MM-PBSA studies showed that 533 bonded favorably to SARS-CoV-2 helicase with a free energy value of −83 kJ/mol. Further, the free energy decomposition study determined the essential amino acid residues that contributed favorably to the binding process. The obtained results give a huge hope to find a cure for COVID-19 through further in vitro and in vivo studies for the selected compounds.

From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples

Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products' drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.

Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease

In continuation of our antecedent work against COVID-19, three natural compounds, namely, Luteoside C (130), Kahalalide E (184), and Streptovaricin B (278) were determined as the most promising SARS-CoV-2 main protease (Mpro) inhibitors among 310 naturally originated antiviral compounds. This was performed via a multi-step in silico method. At first, a molecular structure similarity study was done with PRD_002214, the co-crystallized ligand of Mpro (PDB ID: 6LU7), and favored thirty compounds. Subsequently, the fingerprint study performed with respect to PRD_002214 resulted in the election of sixteen compounds (7, 128, 130, 156, 157, 158, 180, 184, 203, 204, 210, 237, 264, 276, 277, and 278). Then, results of molecular docking versus Mpro PDB ID: 6LU7 favored eight compounds (128, 130, 156, 180, 184, 203, 204, and 278) based on their binding affinities. Then, in silico toxicity studies were performed for the promising compounds and revealed that all of them have good toxicity profiles. Finally, molecular dynamic (MD) simulation experiments were carried out for compounds 130, 184, and 278, which exhibited the best binding modes against Mpro. MD tests revealed that luteoside C (130) has the greatest potential to inhibit SARS-CoV-2 main protease.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

Computational prediction of the molecular mechanism of statin group of drugs against SARS-CoV-2 pathogenesis

Recently published clinical data from COVID-19 patients indicated that statin therapy is associated with a better clinical outcome and a significant reduction in the risk of mortality. In this study by computational analysis, we have aimed to predict the possible mechanism of the statin group of drugs by which they can inhibit SARS-CoV-2 pathogenesis. Blind docking of the critical structural and functional proteins of SARS-CoV-2 like RNA-dependent RNA polymerase, M-protease of 3-CL-Pro, Helicase, and the Spike proteins ( wild type and mutants from different VOCs) were performed using the Schrodinger docking tool. We observed that fluvastatin and pitavastatin showed fair, binding affinities to RNA polymerase and 3-CL-Pro, whereas fluvastatin showed the strongest binding affinity to the helicase. Fluvastatin also showed the highest affinity for the SpikeDelta and a fair docking score for other spike variants. Additionally, molecular dynamics simulation confirmed the formation of a stable drug-protein complex between Fluvastatin and target proteins. Thus our study shows that of all the statins, fluvastatin can bind to multiple target proteins of SARS-CoV-2, including the spike-mutant proteins. This property might contribute to the potent antiviral efficacy of this drug.