MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold

TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.

SAEDC: Development of a technological solution for exploratory data analysis and statistics in cytotoxicity

INTRODUCTION

The intergovernmental organizations Organisation for Economic Co-operation and Development (OECD) and Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) have developed guidelines for the use of in vitro models for toxicological evaluation of chemicals. However, the presence of manual steps and the requirement of multiple tools for data analysis, apart from being costly and time-consuming, can inadvertently introduce errors by researchers.

OBJECTIVES

We have developed the SAEDC platform (Technological Solution for Exploratory Data Analysis and Statistics for Cytotoxicity, in Portuguese), which enables analysis of cytotoxicity data from assays following OECD Guideline No. 129.

METHODOLOGY

In vitro experimental data were used to compare with the analysis methodology suggested in the Guideline. We analyzed 117 data sets covering chemicals from Category I to Unclassified according to GHS classification.

RESULTS

The four-parameters of non-linear regression (4PL) calculated by the SAEDC platform showed no significant differences compared to standard methodology in any of the data sets (p > 0.05). The coefficient of determination (R-squared) also demonstrated not only a good fit of the 4PL model to the data but also significant similarity to values obtained by the conventional methodology. Finally, the SAEDC platform predicted LD50 values for the chemicals from IC50, using the Registry of Cytotoxicity (RC) regression models.

CONCLUSION

The comparison with the standard data analysis methodology revealed that SAEDC platform fulfills the requirements for cytotoxicity data analysis, generating reliable and accurate results with fewer steps performed by researchers. The use of SAEDC platform for obtaining toxicity values can reduce analysis time compared to the standard methodology proposed by regulatory agencies. Thus, automation of the analysis using the SAEDC platform has the potential to save time and resources for cytotoxicity researchers and laboratories while generating reliable results.

Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

The application of brain organoid for drug discovery in mitochondrial diseases

Mitochondrial diseases are difficult to treat due to the complexity and multifaceted nature of mitochondrial dysfunction. Brain organoids are three-dimensional (3D) structures derived from human pluripotent stem cells designed to mimic brain-like development and function. Brain organoids have received a lot of attention in recent years as powerful tools for modeling human diseases, brain development, and drug screening. Screening compounds for mitochondrial diseases using brain organoids could provide a more physiologically relevant platform for drug discovery. Brain organoids offer the possibility of personalized medicine because they can be derived from patient-specific cells, allowing testing of drugs tailored to specific genetic mutations. In this article, we highlight how brain organoids offer a promising avenue for screening compounds for mitochondrial diseases and address the challenges and limitations associated with their use. We hope this review will provide new insights into the further progress of brain organoids for mitochondrial screening studies.

Development of dietary small molecules as multi-targeting treatment strategies for Alzheimer's disease

Cognitive dysfunction can occur both in normal aging and age-related neurological disorders, such as mild cognitive impairment and Alzheimer's disease (AD). These disorders have few treatment options due to side effects and limited efficacy. New approaches to slow cognitive decline are urgently needed. Dietary interventions (nutraceuticals) have received considerable attention because they exhibit strong neuroprotective properties and may help prevent or minimize AD symptoms. Biological aging is driven by a series of interrelated mechanisms, including oxidative stress, neuroinflammation, neuronal apoptosis, and autophagy, which function through various signaling pathways. Recent clinical and preclinical studies have shown that dietary small molecules derived from natural sources, including flavonoids, carotenoids, and polyphenolic acids, can modulate oxidative damage, cognitive impairments, mitochondrial dysfunction, neuroinflammation, neuronal apoptosis, autophagy dysregulation, and gut microbiota dysbiosis. This paper reviews research on different dietary small molecules and their bioactive constituents in the treatment of AD. Additionally, the chemical structure, effective dose, and specific molecular mechanisms of action are comprehensively explored. This paper also discusses the advantages of using nanotechnology-based drug delivery, which significantly enhances oral bioavailability, safety, and therapeutic effect, and lowers the risk of adverse effects. These agents have considerable potential as novel and safe therapeutic agents that can prevent and combat age-related AD.

Discovery and in vitro Evaluation of Novel Serotonin 5-HT2A Receptor Ligands Identified Through Virtual Screening

The 5-HT2A receptor is a molecular target of high pharmacological importance. Ligands of this protein, particularly atypical antipsychotics, are useful in the treatment of numerous mental disorders, including schizophrenia and major depressive disorder. Structure-based virtual screening using a 5-HT2A receptor complex was performed to identify novel ligands for the 5-HT2A receptor, serving as potential antidepressants. From the Enamine screening library, containing over 4 million compounds, 48 molecules were selected for subsequent experimental validation. These compounds were tested against the 5-HT2A receptor in radioligand binding assays. From the tested batch, six molecules were identified as ligands of the main molecular target and were forwarded to a more detailed in vitro profiling. This included radioligand binding assays at 5-HT1A, 5-HT7, and D2 receptors and functional studies at 5-HT2A receptors. These compounds were confirmed to show a binding affinity for at least one of the targets tested in vitro. Thus, the success rate for the inactive template-based screening reached 17%, while it was 9% for the active template-based screening. Similarity and fragment analysis indicated the structural novelty of the identified compounds. Pharmacokinetics for these molecules was determined using in silico approaches.

Data-oriented protein kinase drug discovery

The continued growth of data from biological screening and medicinal chemistry provides opportunities for data-driven experimental design and decision making in early-phase drug discovery. Approaches adopted from data science help to integrate internal and public domain data and extract knowledge from historical in-house data. Protein kinase (PK) drug discovery is an exemplary area where large amounts of data are accumulating, providing a valuable knowledge base for discovery projects. Herein, the evolution of PK drug discovery and development of small molecular PK inhibitors (PKIs) is reviewed, highlighting milestone developments in the field and discussing exemplary studies providing a basis for increasing data orientation of PK discovery efforts.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.

An update on the development on tubulin inhibitors for the treatment of solid tumors

INTRODUCTION

Microtubules play a vital role in cancer therapeutics. They are implicated in tumorigenesis, thus inhibiting tubulin polymerization in cancer cells, and have now become a significant target for anticancer drug development. A plethora of drug molecules has been crafted to influence microtubule dynamics and presently, numerous tubulin inhibitors are being investigated. This review discusses the recently developed inhibitors including natural products, and also examines the preclinical and clinical data of some potential molecules.

AREA COVERED

The current review article summarizes the development of tubulin inhibitors while detailing their specific binding sites. It also discusses the newly designed inhibitors that may be useful in the treatment of solid tumors.

EXPERT OPINION

Microtubules play a crucial role in cellular processes, especially in cancer therapy where inhibiting tubulin polymerization holds promise. Ongoing trials signify a commitment to revolutionizing cancer treatment and exploring targeted therapies. Challenges in microtubule modulation, like resistance and off-target effects, demand focused efforts, emphasizing combination therapies and personalized treatments. Beyond microtubules, promising avenues in cancer research include immunotherapy, genomic medicine, CRISPR gene editing, liquid biopsies, AI diagnostics, and stem cell therapy, showcasing a holistic approach for future advancements.

Selective impact of ALK and MELK inhibition on ERα stability and cell proliferation in cell lines representing distinct molecular phenotypes of breast cancer

Breast cancer (BC) is a leading cause of global cancer-related mortality in women, necessitating accurate tumor classification for timely intervention. Molecular and histological factors, including PAM50 classification, estrogen receptor α (ERα), breast cancer type 1 susceptibility protein (BRCA1), progesterone receptor (PR), and HER2 expression, contribute to intricate BC subtyping. In this work, through a combination of bioinformatic and wet lab screenings, followed by classical signal transduction and cell proliferation methods, and employing multiple BC cell lines, we identified enhanced sensitivity of ERα-positive BC cell lines to ALK and MELK inhibitors, inducing ERα degradation and diminishing proliferation in specific BC subtypes. MELK inhibition attenuated ERα transcriptional activity, impeding E2-induced gene expression, and hampering proliferation in MCF-7 cells. Synergies between MELK inhibition with 4OH-tamoxifen (Tam) and ALK inhibition with HER2 inhibitors revealed potential therapeutic avenues for ERα-positive/PR-positive/HER2-negative and ERα-positive/PR-negative/HER2-positive tumors, respectively. Our findings propose MELK as a promising target for ERα-positive/PR-positive/HER2-negative BC and highlight ALK as a potential focus for ERα-positive/PR-negative/HER2-positive BC. The synergistic anti-proliferative effects of MELK with Tam and ALK with HER2 inhibitors underscore kinase inhibitors' potential for selective treatment in diverse BC subtypes, paving the way for personalized and effective therapeutic strategies in BC management.

Prediction of drug-target binding affinity based on deep learning models

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.

Quinoxaline derivatives: Recent discoveries and development strategies towards anticancer agents

Cancer is a leading cause of death and a major health problem worldwide. While many effective anticancer agents are available, most drugs currently on the market are not specific, raising issues like the common side effects of chemotherapy. However, recent research hold promises for the development of more efficient and safer anticancer drugs. Quinoxaline and its derivatives are becoming recognized as a novel class of chemotherapeutic agents with activity against different tumors. The present review compiles and discusses studies concerning the therapeutic potential of the anticancer activity of quinoxaline derivatives, covering articles published between January 2018 and January 2023.

Antimicrobial and anti-virulence activities of 4-shogaol from grains of paradise against gram-negative bacteria: Integration of experimental and computational methods

ETHNOPHARMACOLOGICAL RELEVANCE

Bacterial resistance to antibiotics is a growing global concern, highlighting the urgent need for new antimicrobial candidates. Aframomum melegueta was traditionally used for combating urinary tract and soft tissue infections, which implies its potential as an antimicrobial agent.

AIM OF STUDY

This study was designed to explore the antibacterial and anti-virulence capabilities of 4-shogaol isolated from A. melegueta seeds versus gram-negative bacteria: Serratia marcescens, Klebsiella pneumoniae, Acinetobacter baumannii, and the clinically important pathogen Pseudomonas aeruginosa.

MATERIALS AND METHODS

4-Shogeol was isolated from A. melegueta seeds and its MICs were determined for Acinetobacter baumannii (ATCC-17978), Pseudomonas aeruginosa (ATCC-27853), Klebsiella pneumoniae (ATCC-700603), and Serratia marcescens clinical isolate. The anti-efflux activity and effect on the bacterial cell membrane for the compound were evaluated. Furthermore, the anti-virulence activities of the compound were evaluated. The effects of 4-shogeol at sub-MIC on bacterial motility, biofilm formation, and production of virulent enzymes and pigments were assessed. The anti-quorum sensing activities of 4-shogeol were evaluated virtually and by quantification its effect on the expression of quorum sensing encoding genes. The in vivo protection assay was conducted to evaluate the effect of 4-shogaol on the P. aeruginosa capacity to induce pathogenesis in mice. Finally, the effect of shogaol-antibiotics combination was assessed.

RESULTS

The research revealed that 4-shogaol's antibacterial action primarily involves disrupting the bacterial cell membrane and efflux pumps. It also exhibited significant anti-virulence effects by reducing biofilm development and repressing virulence factors production, effectively protecting mice against P. aeruginosa infection. Furthermore, when combined with antibiotics, 4-shogaol demonstrated synergistic effects, leading to reduced minimum inhibitory concentrations (MICs) against P. aeruginosa. Its anti-virulence properties were linked to its ability to disrupt bacterial quorum sensing (QS) mechanisms, as evidenced by its interaction with QS receptors and downregulation of QS-related genes. Notably, in silico analysis indicated that 4-shogaol exhibited strong binding affinity to different P. aeruginosa QS targets.

CONCLUSION

These findings suggest that 4-shogaol holds promise as an effective anti-virulence agent that can be utilized in combination with antibiotics for treating severe infections caused by gram-positive bacteria.

Bio synthesis, comprehensive characterization, and multifaceted therapeutic applications of BSA-Resveratrol coated platinum nanoparticles

This study examines the manufacturing, characterization, and biological evaluation of platinum nanoparticles, which were synthesized by Enterobacter cloacae and coated with Bovine Serum Albumin (BSA) and Resveratrol (RSV). The formation of PtNPs was confirmed with the change of color from dark yellow to black, which was due to the bioreduction of platinum chloride by E. cloacae. BSA and RSV functionalization enhanced these nanoparticles' biocompatibility and therapeutic potential. TGA, SEM, XRD, and FTIR were employed for characterization, where PtNPs and drug conjugation-related functional groups were studied by FTIR. XRD confirmed the crystalline nature of PtNPs and Pt-BSA-RSV NPs, while TGA and SEM showed thermal stability and post-drug coating morphological changes. Designed composite was also found to be biocompatible in nature in hemolytic testing, indicating their potential in Biomedical applications. After confirmation of PtNPs based nanocaompsite synthesis, they were examined for anti-bacterial, anti-oxidant, anti-inflammatory, and anti-cancer properties. Pt-BSA-RSV NPs showed higher concentration-dependent DPPH scavenging activity, which measured antioxidant capability. Enzyme inhibition tests demonstrated considerable anti-inflammatory activity against COX-2 and 15-LOX enzymes. In in vitro anticancer studies, Pt-BSA-RSV NPs effectively killed human ovarian cancer cells. This phenomenon was demonstrated to be facilitated by the acidic environment of cancer, as the drug release assay confirmed the release of RSV from the NP formulation in the acidic environment. Finally, Molecular docking also demonstrated that RSV has strong potential as an anti-oxidant, antibacterial, anti-inflammatory, and anticancer agent. Overall, in silico and in vitro investigations in the current study showed good medicinal applications for designed nanocomposites, however, further in-vivo experiments must be conducted to validate our findings.

Vesicular monoamine transporter-2 inhibitor JPC-141 prevents methamphetamine-induced dopamine toxicity and blocks methamphetamine self-administration in rats

Previous research has demonstrated therapeutic potential for VMAT2 inhibitors in rat models of methamphetamine use disorder. Here, we report on the neurochemical and behavioral effects of 1-(2-methoxyphenethyl)-4-phenethypiperazine (JPC-141), a novel analog of lobelane. JPC-141 potently inhibited (Ki = 52 nM) [3H]dopamine uptake by VMAT2 in striatal vesicles with 50 to 250-fold greater selectivity for VMAT2 over dopamine, norepinephrine and serotonin plasmalemma transporters. Also, JPC-141 was 57-fold more selective for inhibiting VMAT2 over [3H]dofetilide binding to hERG channels expressed by HEK293, suggesting relatively low potential for cardiotoxicity. When administered in vivo to rats, JPC-141 prevented the METH-induced reduction in striatal dopamine content when given either prior to or after a high dose of METH, suggesting a reduction in METH-induced dopaminergic neurotoxicity. In behavioral assays, JPC-141 decreased METH-stimulated locomotor activity in METH-sensitized rats at doses of JPC-141 which did not alter locomotor activity in the saline control group. Moreover, JPC-141 specifically decreased iv METH self-administration at doses that had no effect on food-maintained responding. These findings support the further development of VMAT2 inhibitors as pharmacotherapies for individuals with methamphetamine use disorder.

Is there a biochemical basis for purinergic P2X3 and P2X4 receptor antagonists to be considered as anti-seizure medications?

Patients with epilepsy require improved medications. Purinergic receptors were identified as late as 1976 and are slowly emerging as potential drug targets for the discovery of antiseizure medications. While compounds interacting with these receptors have been approved for use as medicines (e.g., gefapixant for cough) and continue to be explored for a number of diseases (e.g., pain, cancer), there have been no purinergic receptor antagonists that have been advanced for epilepsy. There are very few studies on the channel conducting receptors, P2X3 and P2X4, that suggest their possible role in seizure generation or control. However, the limited data available provides some compelling reasons to believe that they could be valuable antiseizure medication drug targets. The data implicating P2X3 and P2X4 receptors in epilepsy includes the role played by ATP in neuronal excitability and seizures, receptor localization, increased receptor expression in epileptic brain, the involvement of these receptors in seizure-associated inflammation, crosstalk between these purinergic receptors and neuronal processes involved in seizures (GABAergic and glutamatergic neurotransmission), and the significant attenuation of seizures and seizure-like activity with P2X receptor blockade. The discovery of new and selective antagonists for P2X3 and P2X4 receptors is ongoing, armed with new structural data to guide rational design. The availability of safe, brain-penetrant compounds will likely encourage the clinical exploration of epilepsy as a disease entity.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

N- and s-substituted Pyrazolopyrimidines: A promising new class of potent c-Src kinase inhibitors with prominent antitumor activity

In this work, readily achievable synthetic pathways were utilized for construction of a library of N/S analogues based on the pyrazolopyrimidine scaffold with terminal alkyl or aryl fragments. Subsequently, we evaluated the anticancer effects of these novel analogs against the proliferation of various cancer cell lines, including breast, colon, and liver lines. The results were striking, most of the tested molecules exhibited strong and selective cytotoxic activity against the MDA-MB-231 cancer cell line; IC50 1.13 µM. Structure-activity relationship (SAR) analysis revealed that N-substituted derivatives generally enhanced the cytotoxic effect, particularly with aliphatic side chains that facilitated favorable target interactions. We also investigated apoptosis, DNA fragmentation, invasion assay, and anti-migration effects, and discussed their underlying molecular mechanisms for the most active compound 7c. We demonstrated that 7c N-propyl analogue could inhibit MDA-MB-231 TNBC cell proliferation by inducing apoptosis through the regulation of vital proteins, namely c-Src, p53, and Bax. In addition, our results also revealed the potential of these compounds against tumor metastasis by downregulating the invasion and migration modes. Moreover, the in vitro inhibitory effect of active analogs against c-Src kinase was studied and proved that might be the main cause of their antiproliferative effect. Overall, these compelling results point towards the therapeutic potential of these derivatives, particularly those with N-substitution as promising candidates for the treatment of TNBC type of breast cancer.

Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery

Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.

Discovery of bioactive natural products of microbial origin as inhibitors of the PD-1/PD-L1 protein-protein interaction

The PD-1/PD-L1 protein-protein interaction (PPI) controls an adaptive immune resistance mechanism exerted by tumor cells to evade immune responses. The large-molecule nature of current commercial monoclonal antibodies against this PPI hampers their effectiveness by limiting tumor penetration and inducing severe immune-related side effects. Synthetic small-molecule inhibitors may overcome such limitations and have demonstrated promising clinical translation, but their design is challenging. Microbial natural products (NPs) are a source of small molecules with vast chemical diversity that have proved anti-tumoral activities, but which immunotherapeutic properties as PD-1/PD-L1 inhibitors had remained uncharacterized so far. Here, we have developed the first cell-based PD-1/PD-L1 blockade reporter assay to screen NPs libraries. In this study, 6000 microbial extracts of maximum biosynthetic diversity were screened. A secondary metabolite called alpha-cyclopiazonic acid (α-CPA) of a bioactive fungal extract was confirmed as a new PD-1/PD-L1 inhibitor with low micromolar range in the cellular assay and in an additional cell-free competitive assay. Thermal denaturation experiments with PD-1 confirmed that the mechanism of inhibition is based on its stabilization upon binding to α-CPA. The identification of α-CPA as a novel PD-1 stabilizer proves the unprecedented resolution of this methodology at capturing specific PD-1/PD-L1 PPI inhibitors from chemically diverse NP libraries.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

Structure-activity relationship and target investigation of 2-aryl quinolines with nematocidal activity

Within the context of our anthelmintic discovery program, we recently identified and evaluated a quinoline derivative, called ABX464 or obefazimod, as a nematocidal candidate; synthesised a series of analogues which were assessed for activity against the free-living nematode Caenorhabditis elegans; and predicted compound-target relationships by thermal proteome profiling (TPP) and in silico docking. Here, we logically extended this work and critically evaluated the anthelmintic activity of ABX464 analogues on Haemonchus contortus (barber's pole worm) - a highly pathogenic nematode of ruminant livestock. First, we tested a series of 44 analogues on H. contortus (larvae and adults) to investigate the nematocidal pharmacophore of ABX464, and identified one compound with greater potency than the parent compound and showed moderate activity against a select number of other parasitic nematodes (including Ancylostoma, Heligmosomoides and Strongyloides species). Using TPP and in silico modelling studies, we predicted protein HCON_00074590 (a predicted aldo-keto reductase) as a target candidate for ABX464 in H. contortus. Future work aims to optimise this compound as a nematocidal candidate and investigate its pharmacokinetic properties. Overall, this study presents a first step toward the development of a new nematocide.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.

Mining channel-regulated peptides from animal venom by integrating sequence semantics and structural information

Channel-regulated peptides (CRPs) derived from animal venom hold great promise as potential drug candidates for numerous diseases associated with channel proteins. However, discovering and identifying CRPs using traditional bio-experimental methods is a time-consuming and laborious process. While there were a few computational studies on CRPs, they were limited to specific channel proteins, relied heavily on complex feature engineering, and lacked the incorporation of multi-source information. To address these problems, we proposed a novel deep learning model, called DeepCRPs, based on graph neural networks for systematically mining CRPs from animal venom. By combining the sequence semantic and structural information, the classification performance of four CRPs was significantly enhanced, reaching an accuracy of 0.92. This performance surpassed baseline models with accuracies ranging from 0.77 to 0.89. Furthermore, we employed advanced interpretable techniques to explore sequence and structural determinants relevant to the classification of CRPs, yielding potentially valuable bio-function interpretations. Comprehensive experimental results demonstrated the precision and interpretive capability of DeepCRPs, making it an accurate and bio-explainable suit for the identification and categorization of CRPs. Our research will contribute to the discovery and development of toxin peptides targeting channel proteins. The source data and code are freely available at https://github.com/liyigerry/DeepCRPs.

Neutralizing antibodies to block viral entry and for identification of entry inhibitors

Neutralizing antibodies (NAbs) are naturally produced by our immune system to combat viral infections. Clinically, neutralizing antibodies with potent efficacy and high specificity have been extensively used to prevent and treat a wide variety of viral infections, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Human Immunodeficiency Virus (HIV), Dengue Virus (DENV) and Hepatitis B Virus (HBV). An overwhelmingly large subset of clinically effective NAbs operates by targeting viral envelope proteins to inhibit viral entry into the host cell. Binding of viral envelope protein to the host receptor is a critical rate limiting step triggering a cascade of downstream events, including endocytosis, membrane fusion and pore formation to allow viral entry. In recent years, improved structural knowledge on these processes have allowed researchers to also leverage NAbs as an indispensable tool in guiding discovery of novel antiviral entry inhibitors, providing drug candidates with high efficacy and pan-genus specificity. This review will summarize the latest progresses on the applications of NAbs as effective entry inhibitors and as important tools to develop antiviral therapeutics by high-throughput drug screenings, rational design of peptidic entry inhibitor mimicking NAbs and in silico computational modeling approaches.

Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis

BACKGROUND

Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies.

METHODS

Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining.

RESULTS

Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle.

CONCLUSIONS

The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

A domain-label-guided translation model for molecular optimization

Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched molecular pairs, which are prone to be limited by the datasets. Although some models do not require matched molecular pairs, their performance is usually sacrificed due to the lack of useful supervising information. To address this issue, a domain-label-guided translation model is proposed in this paper, namely DLTM. In the model, the domain label information of molecules is exploited as a control condition to obtain different embedding representations, enabling the model to generate diverse molecules. Besides, the model adopts a classifier network to identify the property categories of transformed molecules, guiding the model to generate molecules with desired properties. The performance of DLTM is verified on two optimization tasks, namely the quantitative estimation of drug-likeness and penalized logP. Experimental results show that the proposed DLTM is superior to the compared baseline models.

Exploring MTH1 inhibitory potential of Thymoquinone and Baicalin for therapeutic targeting of breast cancer

Cancers frequently have increased ROS levels due to disrupted redox balance, leading to oxidative DNA and protein damage, mutations, and apoptosis. The MTH1 protein plays a crucial role by sanitizing the oxidized dNTP pools. Hence, cancer cells rely on MTH1 to prevent the integration of oxidized dNTPs into DNA, preventing DNA damage and allowing cancer cell proliferation. We have discovered Thymoquinone (TQ) and Baicalin (BC) as inhibitors of MTH1 using combined docking and MD simulation approaches complemented by experimental validations via assessing binding affinity and enzyme inhibition. Docking and MD simulations studies revealed an efficient binding of TQ and BC to the active site pocket of the MTH1, and the resultant complexes are appreciably stable. Fluorescence measurements estimated a strong binding affinity of TQ and BC with Ka 3.4 ×106 and 1.0 ×105, respectively. Treating breast cancer cells with TQ and BC significantly inhibited the growth and proliferation (IC50 values 28.3 µM and 34.8 µM) and induced apoptosis. TQ and BC increased the ROS production in MCF7 cells, imposing substantial oxidative stress on cancer cells and leading to cell death. Finally, TQ and BC are proven strong MTH1 inhibitors, offering promising prospects for anti-cancer therapy.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

BACKGROUND

Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

METHODS

This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

RESULTS

Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

LIMITATIONS

Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

CONCLUSIONS

Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Comprehensive insights into potential roles of purinergic P2 receptors on diseases: Signaling pathways involved and potential therapeutics

BACKGROUND

Purinergic P2 receptors, which can be divided into ionotropic P2X receptors and metabotropic P2Y receptors, mediate cellular signal transduction of purine or pyrimidine nucleoside triphosphates and diphosphate. Based on the wide expression of purinergic P2 receptors in tissues and organs, their significance in homeostatic maintenance, metabolism, nociceptive transmission, and other physiological processes is becoming increasingly evident, suggesting that targeting purinergic P2 receptors to regulate biological functions and signal transmission holds significant promise for disease treatment.

AIM OF REVIEW

This review highlights the detailed mechanisms by which purinergic P2 receptors engage in physiological and pathological progress, as well as providing prospective strategies for discovering clinical drug candidates.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purinergic P2 receptors regulate complex signaling and molecular mechanisms in nervous system, digestive system, immune system and as a result, controlling physical health states and disease progression. There has been a significant rise in research and development focused on purinergic P2 receptors, contributing to an increased number of drug candidates in clinical trials. A few influential pioneers have laid the foundation for advancements in the evaluation, development, and of novel purinergic P2 receptors modulators, including agonists, antagonists, pharmaceutical compositions and combination strategies, despite the different scaffolds of these drug candidates. These advancements hold great potential for improving therapeutic outcomes by specifically targeting purinergic P2 receptors.

Marine Natural Products as Novel Treatments for Parasitic Diseases

Parasitic diseases including malaria, leishmaniasis, and trypanosomiasis have received significant attention due to their severe health implications, especially in developing countries. Marine natural products from a vast and diverse range of marine organisms such as sponges, corals, molluscs, and algae have been found to produce unique bioactive compounds that exhibit promising potent properties, including antiparasitic, anti-Plasmodial, anti-Leishmanial, and anti-Trypanosomal activities, providing hope for the development of effective treatments. Furthermore, various techniques and methodologies have been used to investigate the mechanisms of these antiparasitic compounds. Continued efforts in the discovery and development of marine natural products hold significant promise for the future of novel treatments against parasitic diseases.

Fragment-based drug discovery and biological evaluation of novel cannabinol-based inhibitors of oxytosis/ferroptosis for neurological disorders

The oxytosis/ferroptosis regulated cell death pathway is an emerging field of research owing to its pathophysiological relevance to a wide range of neurological disorders, including Alzheimer's and Parkinson's diseases and traumatic brain injury. Developing novel neurotherapeutics to inhibit oxytosis/ferroptosis offers exciting opportunities for the treatment of these and other neurological diseases. Previously, we discovered cannabinol (CBN) as a unique, potent inhibitor of oxytosis/ferroptosis by targeting mitochondria and modulating their function in neuronal cells. To further elucidate which key pharmacophores and chemical space are essential to the beneficial effects of CBN, we herein introduce a fragment-based drug discovery strategy in conjunction with cell-based phenotypic screens using oxytosis/ferroptosis to determine the structure-activity relationship of CBN. The resulting information led to the development of four new CBN analogs, CP1-CP4, that not only preserve the sub-micromolar potency of neuroprotection and mitochondria-modulating activities seen with CBN in neuronal cell models but also have better druglike properties. Moreover, compared to CBN, the analog CP1 shows improved in vivo efficacy in the Drosophila model of mild traumatic brain injury. Together these studies identify the key molecular scaffolds of cannabinoids that contribute to neuroprotection against oxytosis/ferroptosis. They also highlight the advantageous approach of combining in vitro cell-based assays and rapid in vivo studies using Drosophila models for evaluating new therapeutic compounds.

Cryosphere: a frozen home of microbes and a potential source for drug discovery

The world is concerned about the emergence of pathogens and the occurrence and spread of antibiotic resistance among pathogens. Drug development requires time to combat these issues. Consequently, drug development from natural sources is unavoidable. Cryosphere represents a gigantic source of microbes that could be the bioprospecting source of natural products with unique scaffolds as molecules or drug templates. This review focuses on the novel source of drug discovery and cryospheric environments as a potential source for microbial metabolites having potential medicinal applications. Furthermore, the problems encountered in discovering metabolites from cold-adapted microbes and their resolutions are discussed. By adopting modern practical approaches, the discovery of bioactive compounds might fulfill the demand for new drug development.

Discovery of a potent inhibitor, D-132, targeting AsfvPolX, via protein-DNA complex-guided pharmacophore screening and in vitro molecular characterizations

The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.

Generation of fibroblast-derived induced pluripotent stem cell (iPSC) lines from two paediatric patients with phenylketonuria

Phenylketonuria is a rare autosomal recessive metabolic disorder mainly due to a significant reduction in the enzyme phenylalanine hydroxylase, resulting in elevation of phenylalanine in the blood. Here, we have established two fibroblast-derived induced pluripotent stem cell lines using Sendai virus-based reprogramming. The established induced pluripotent stem cell lines exhibited a normal karyotype and expressed markers of pluripotency assessed through quantitative PCR, flow cytometry and immunocytochemistry. These cell lines also demonstrated the ability to differentiate into the three primary germ layers of the human body, including ectoderm, endoderm, and mesoderm.

Machine learning-aided search for ligands of P2Y6 and other P2Y receptors

The P2Y6 receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet few potent and selective antagonists are known to date. This prompted us to use machine learning as a novel approach to aid ligand discovery, with pharmacological evaluation at three P2YR subtypes: initially P2Y6 and subsequently P2Y1 and P2Y14. Relying on extensive published data for P2Y6R agonists, we generated and validated an array of classification machine learning model using the algorithms deep learning (DL), adaboost classifier (ada), Bernoulli NB (bnb), k-nearest neighbors (kNN) classifier, logistic regression (lreg), random forest classifier (rf), support vector classification (SVC), and XGBoost (XGB) classifier models, and the common consensus was applied to molecular selection of 21 diverse structures. Compounds were screened using human P2Y6R-induced functional calcium transients in transfected 1321N1 astrocytoma cells and fluorescent binding inhibition at closely related hP2Y14R expressed in CHO cells. The hit compound ABBV-744, an experimental anticancer drug with a 6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridine scaffold, had multifaceted interactions with the P2YR family: hP2Y6R inhibition in a non-surmountable fashion, suggesting that noncompetitive antagonism, and hP2Y1R enhancement, but not hP2Y14R binding inhibition. Other machine learning-selected compounds were either weak (experimental anti-asthmatic drug AZD5423 with a phenyl-1H-indazole scaffold) or inactive in inhibiting the hP2Y6R. Experimental drugs TAK-593 and GSK1070916 (100 µM) inhibited P2Y14R fluorescent binding by 50% and 38%, respectively, and all other compounds by < 20%. Thus, machine learning has led the way toward revealing previously unknown modulators of several P2YR subtypes that have varied effects.

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.

Compounds with potentialities as novel chemotherapeutic agents in leishmaniasis at preclinical level

Leishmaniasis are neglected infectious diseases caused by kinetoplastid protozoan parasites from the genus Leishmania. These sicknesses are present mainly in tropical regions and almost 1 million new cases are reported each year. The absence of vaccines, as well as the high cost, toxicity or resistance to the current drugs determines the necessity of new treatments against these pathologies. In this review, several compounds with potentialities as new antileishmanial drugs are presented. The discussion is restricted to the preclinical level and molecules are organized according to their chemical nature, source and molecular targets. In this manner, we present antimicrobial peptides, flavonoids, withanolides, 8-aminoquinolines, compounds from Leish-Box, pyrazolopyrimidines, and inhibitors of tubulin polymerization/depolymerization, topoisomerase IB, proteases, pteridine reductase, N-myristoyltransferase, as well as enzymes involved in polyamine metabolism, response against oxidative stress, signaling pathways, and sterol biosynthesis. This work is a contribution to the general knowledge of these compounds as antileishmanial agents.

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.

Current advances and development strategies of targeting son of sevenless 1 (SOS1) in drug discovery

The Son of Sevenless 1 (SOS1) guanine nucleotide exchange factor, prevalent across eukaryotic species, plays a pivotal role in facilitating the attachment of RAS protein to GTP, thereby regulating the activation of intracellular RAS proteins. This regulation is part of a feedback mechanism involving SOS1, which allows both activators and inhibitors of SOS1 to exert control over downstream signaling pathways, demonstrating potential anti-tumor effects. Predominantly, small molecule modulators that target SOS1 focus on a hydrophobic pocket within the CDC25 protein domain. The effectiveness of these modulators largely depends on their ability to interact with specific amino acids, notably Phe890 and Tyr884. This interaction is crucial for influencing the protein-protein interaction (PPI) between RAS and the catalytic domain of SOS1. Currently, most small molecule modulators targeting SOS1 are in the preclinical research phase, with a few advancing to clinical trials. This progression raises safety concerns, making the assurance of drug safety a primary consideration alongside the enhancement of efficacy in the development of SOS1 modulators. This review encapsulates recent advancements in the chemical categorization of SOS1 inhibitors and activators. It delves into the evolution of small molecule modulation targeting SOS1 and offers perspectives on the design of future generations of selective SOS1 small molecule modulators.

Small-molecule agents for treating skin diseases

Skin diseases are a class of common and frequently occurring diseases that significantly impact daily lives. Currently, the limited effective therapeutic drugs are far from meeting the clinical needs; most drugs typically only provide symptomatic relief rather than a cure. Developing small-molecule drugs with improved efficacy holds paramount importance for treating skin diseases. This review aimed to systematically introduce the pathogenesis of common skin diseases in daily life, list related drugs applied in the clinic, and summarize the clinical research status of candidate drugs and the latest research progress of candidate compounds in the drug discovery stage. Also, it statistically analyzed the number of publications and global attention trends for the involved skin diseases. This review might provide practical information for researchers engaged in dermatological drugs and further increase research attention to this disease area.

A phenotypic screen of the Global Health Priority Box identifies an insecticide with anthelmintic activity

BACKGROUND

Infection with parasitic nematodes (helminths), particularly those of the order Strongylida (such as Haemonchus contortus), can cause significant and burdensome diseases in humans and animals. Widespread drug (anthelmintic) resistance in livestock parasites, the absence of vaccines against most of these nematodes, and a lack of new and effective chemical entities on the commercial market demands the discovery of new anthelmintics. In the present study, we searched the Global Health Priority Box (Medicines for Malaria Venture) for new candidates for anthelmintic development.

METHODS

We employed a whole-organism, motility-based phenotypic screening assay to identify compounds from the Global Health Priority Box with activity against larvae of the model parasite H. contortus, and the free-living comparator nematode Caenorhabditis elegans. Hit compounds were further validated via dose-response assays, with lead candidates then assessed for nematocidal activity against H. contortus adult worms, and additionally, for cytotoxic and mitotoxic effects on human hepatoma (HepG2) cells.

RESULTS

The primary screen against H. contortus and C. elegans revealed or reidentified 16 hit compounds; further validation established MMV1794206, otherwise known as 'flufenerim', as a significant inhibitor of H. contortus larval motility (half-maximal inhibitory concentration [IC50] = 18 μM) and development (IC50 = 1.2 μM), H. contortus adult female motility (100% after 12 h of incubation) and C. elegans larval motility (IC50 = 0.22 μM). Further testing on a mammalian cell line (human hepatoma HepG2 cells), however, identified flufenerim to be both cytotoxic (half-maximal cytotoxic concentration [CC50] < 0.7 μM) and mitotoxic (half-maximal mitotoxic concentration [MC50] < 0.7 μM).

CONCLUSIONS

The in vitro efficacy of MMV1794206 against the most pathogenic stages of H. contortus, as well as the free-living C. elegans, suggests the potential for development as a broad-spectrum anthelmintic compound; however, the high toxicity towards mammalian cells presents a significant hindrance. Further work should seek to establish the protein-drug interactions of MMV1794206 in a nematode model, to unravel the mechanism of action, in addition to an advanced structure-activity relationship investigation to optimise anthelmintic activity and eliminate mammalian cell toxicity.

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.