Computational methods in drug discovery

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.

Leveraging-Induced Polarization for Drug Discovery: Efficient IC50 Prediction Using Minimal Features

Here, we use the frequency of the atomic hybridizations (s, sp, sp2, and sp3) of each atom type (H, C, N, O, S, etc.) within a molecule to predict the IC50s of drug-like molecules, focusing on compounds targeting the Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A proteins. The Neural Network and Random Forest models yield high correlation coefficients (R2) and low mean square error (MSE) using only 19 features. The atomic hybridizations have been used previously to calculate the molecular polarizability using a simple empirical model (Miller et al. JACS 1979). We show that the atomic hybridizations may also be used to accurately predict the molecular polarizabilities of these molecules. The results show the importance of the induced polarization in protein-ligand binding. Furthermore, the variation in R2 and MSE for the different target proteins indicates that the contribution of the induced polarization to the binding energies is different for different target proteins.

Forsythoside B activates Siglec-14 to inhibit HIV-1 replication via the JAK1/STAT1 pathway

The HIV/AIDS epidemic poses a severe global health challenge. While antiretroviral therapy is crucial, it has limitations, including high costs and resistance, and requires long-term use. Consequently, novel antiviral agents with unique structures and innovative mechanisms are needed for better management of HIV/AIDS. We previously discovered that Siglec-14 inhibits HIV-1 replication. In this study, we employed homology modeling and AlphaFold 2 to predict the structure of Siglec-14, followed by molecular dynamics simulations to explore its conformational landscape. The MM/GBSA method was used to calculate the binding free energy of selected small molecules. Among them, Forsythoside B (FTS·B) exhibited the highest binding free energy and enhanced Siglec-14's conformational stability. SPR analysis further confirmed a strong binding affinity between FTS·B and Siglec-14. In vitro experiments demonstrated that FTS·B upregulates Siglec-14 expression and suppresses HIV-1 replication in macrophages. Mechanistically, FTS·B suppresses pro-inflammatory cytokines, increases the expression of interferon-stimulated genes and chemokines, and activates the JAK1/STAT1 pathway in Siglec-14 knockdown macrophages. Our results confirm that FTS·B, as an agonist of Siglec-14, effectively inhibits HIV-1 replication by upregulating Siglec-14 expression and modulating the JAK1/STAT1 signaling pathway, highlighting the potential clinical application value of FTS·B in HIV treatment.

Current Approaches and Strategies Applied in First-in-class Drug Discovery

First-in-class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target-based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit-to-lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

In silico docking and ADMET studies on clinical targets for type 2 diabetes correlated to in vitro inhibition of pancreatic alpha-amylase and alpha-glucosidase by rutin, caffeic acid, p-coumaric acid, and vanillin

Inhibition of pancreatic alpha-amylase and alpha-glucosidase is a common strategy to manage type 2 diabetes. This study focuses on the ability of compounds present in commercially available herbs and spices to inhibit pancreatic alpha-amylase and alpha-glucosidase. In silico molecular docking was performed to evaluate the binding affinity of the compounds present in herbs and spices. Molecular dynamics was performed with acarbose and rutin which had the best docking scores for pancreatic alpha-amylase and alpha-glucosidase. Six compounds (rutin, caffeic acid, p-coumaric acid, vanillin, ethyl gallate, and oxalic acid) with a range of docking scores were subjected to in vitro enzyme kinetic studies using pancreatic alpha-amylase and alpha-glucosidase biochemical assays. Acarbose, a prescribed alpha-amylase and alpha-glucosidase inhibitor, was used as a positive control. Ligands that interacted strongly with the amino acids at a particular site, were conformationally stable and had good docking scores. There was a correlation between the in silico and in vitro binding affinity. Caffeic acid, vanillin, ethyl gallate, and p-coumaric acid had inhibition constant (Ki) values that were not significantly different (p > 0.05) from the Ki of acarbose for pancreatic alpha-amylase. Rutin, caffeic acid, vanillin, and p-coumaric acid had Ki values that were not significantly different (p ˃ 0.05) from the Ki of acarbose for alpha-glucosidase. The cell viability of these compounds was assessed with the sulforhodamine B (SRB) assay in Caco2 cells. Caffeic acid, p-coumaric acid, rutin, and vanillin had Caco2 IC50 values that were not significantly different (p ˃ 0.05) from that of acarbose. The evaluated compounds present in herbs and spices can potentially reduce hyperglycemia associated with type 2 diabetes. Herbs and spices with high levels of these compounds were identified and these were common verbena, sweet basil, tarragon, pepper, parsley, sorrel, and vanilla. These herbs and spices may reduce the required dose of prescription drugs, such as acarbose, thereby reducing costs and drug-associated side effects.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00324-6.

ABIET: An explainable transformer for identifying functional groups in biological active molecules

Recent advancements in deep learning have revolutionized the field of drug discovery, with Transformer-based models emerging as powerful tools for molecular design and property prediction. However, the lack of explainability in such models remains a significant challenge. In this study, we introduce ABIET (Attention-Based Importance Estimation Tool), an explainable Transformer model designed to identify the most critical regions for drug-target interactions - functional groups (FGs) - in biologically active molecules. Functional groups play a pivotal role in determining chemical behavior and biological interactions. Our approach leverages attention weights from Transformer-encoder architectures trained on SMILES representations to assess the relative importance of molecular subregions. By processing attention scores using a specific strategy - considering bidirectional interactions, layer-based extraction, and activation transformations - we effectively distinguish FGs from non-FG atoms. Experimental validation on diverse datasets targeting pharmacological receptors, including VEGFR2, AA2A, GSK3, JNK3, and DRD2, demonstrates the model's robustness and interpretability. Comparative analysis with state-of-the-art gradient-based and perturbation-based methods confirms ABIET's superior performance, with functional groups receiving statistically higher importance scores. This work enhances the transparency of Transformer predictions, providing critical insights for molecular design, structure-activity analysis, and targeted drug development.

GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity

BACKGROUND

Drug-target binding affinity (DTA) prediction is vital in drug discovery and repositioning, more and more researchers are beginning to focus on this. Many effective methods have been proposed. However, some current methods have certain shortcomings in focusing on important nodes in drug molecular graphs and dealing with complex structural molecules. In particular, when considering important nodes and complex substructures in molecules, they may not be able to fully explore the potential relationships between different parts. In addition, when dealing with protein structures, some methods ignore the connections between amino acid fragments that are far apart in sequence but may work synergistically in function.

RESULTS

In this paper, we propose a new method, called GS-DTA, for predicting DTA based on graph and sequence models. GS-DTA takes simplified molecular input line input system (SMILES) of the drug and the protein amino acid sequence as input. First, each drug is modeled as a graph, in which a vertex is an atom and an edge represents interaction between atoms. Then GATv2-GCN and the three-layer GCN networks are used to extract the features of the drug. GATv2-GCN enhances the model's ability to focus on important nodes by assigning dynamic attention scores, which improves the learning of the graph structure's intricate patterns. Besides, The three-layer GCN can captures hierarchical features of the drug through deeper propagation and feature transformation. Meanwhile, for each protein, a framework combining CNN, Bi-LSTM, and Transformer is used to extract the contextual and structural information of the protein amino acid sequences, and this combination can help to understand a comprehensive and detailed features of the protein. Finally, the obtained drug and protein feature vectors are combined to predict DTA through the fully connected layer. The source code can be downloaded from https://github.com/zhuziguang/GS-DTA .

CONCLUSIONS

The results show that GS-DTA achieves good performance in terms of MSE, CI, and r2m on the Davis and KIBA datasets, improving the accuracy of DTA prediction.

The latest progress in assay development in leishmaniasis drug discovery: a review of the available papers on PubMed from the past year

INTRODUCTION

Leishmaniasis is a significant neglected tropical disease with limited treatment options that urgently requires ongoing efforts in drug discovery. Recent advances have focused on the development of new assays and methods to identify effective therapeutic candidates.

AREAS COVERED

This review explores recent trends and methodologies in leishmaniasis drug discovery, with a particular focus on in silico and in vitro studies, as well as in vivo validation, using animal models. A detailed analysis of recent studies was provided, discussing the methodologies employed, such as manual and automated parasite quantification, and the use of fluorescence and luminescence-based techniques. Additionally, global research trends were analyzed, highlighting the leading countries in scientific output and the collaborative efforts driving advancements in this field.

EXPERT OPINION

The field of leishmaniasis drug discovery has rapidly progressed in the last years, but the lack of standardized methodologies and limited in vivo validation remain significant hurdles. To advance promising treatments to clinical trials, cross-validation of preclinical findings and interdisciplinary collaboration are essential. Increased funding and global partnerships are also crucial to accelerate the discovery and development of alternative and effective therapies.

Targeting necroptosis in MCF-7 breast cancer cells: In Silico insights into 8,12-dimethoxysanguinarine from Eomecon Chionantha through molecular docking, dynamics, DFT, and MEP studies

Breast cancer remains a significant challenge in oncology, highlighting the need for alternative therapeutic strategies that target necroptosis to overcome resistance to conventional therapies. Recent investigations into natural compounds have identified 8,12-dimethoxysanguinarine (SG-A) from Eomecon chionantha as a potential necroptosis inducer. This study presents the first computational exploration of SG-A interactions with key necroptotic proteins-RIPK1, RIPK3, and MLKL-through molecular docking, molecular dynamics (MD), density functional theory (DFT), and molecular electrostatic potential (MEP) analyses. Molecular docking revealed that SG-A exhibited a stronger affinity for MLKL (-9.40 kcal/mol) compared to the co-crystallized ligand (-6.29 kcal/mol), while its affinity for RIPK1 (-6.37 kcal/mol) and RIPK3 (-7.01 kcal/mol) was lower. MD simulations further demonstrated the stability of SG-A within the MLKL site, with RMSD values stabilizing between 1.4 and 3.3 Å over 300 ns, indicating a consistent interaction pattern. RMSF analysis indicated the preservation of protein backbone flexibility, with average fluctuations under 1.7 Å. The radius of gyration (Rg) results indicated a consistent value of ~15.3 Å across systems, confirming the role of SG-A in maintaining protein integrity. Notably, SG-A maintains two critical H-bonds within the active site of MLKL, reinforcing the stability of the interaction. Principal component analysis (PCA) indicated a significant reduction in MLKL's conformational space upon SG-A binding, implying enhanced stabilization. Dynamic cross-correlation map (DCCM) analysis further revealed that SG-A induced highly correlated motions, reducing internal fluctuations within MLKL compared to the co-crystallized ligand. MM-PBSA revealed the enhanced binding efficacy of SG-A, with a significant binding free energy of -31.03 ± 0.16 kcal/mol against MLKL, surpassing that of the control (23.96 ± 0.11 kcal/mol). In addition, the individual residue contribution analysis highlighted key interactions, with ARG149 showing a significant contribution (-176.24 kcal/mol) in the MLKL-SG-A complex. DFT and MEP studies corroborated these findings, revealing that the electronic structure of SG-A is conducive to stable binding interactions, characterized by a narrow band gap (~0.16 units) and distinct electrostatic potential favourable for necroptosis induction. In conclusion, SG-A has emerged as a compelling inducer of necroptosis for breast cancer therapy, warranting further experimental validation to fully realize its therapeutic potential.

Exploring Inhibition Mechanisms in Wildtype and T315I BCR-ABL1: An In Silico Approach Integrating Virtual Screening, MD Simulations, and MM-GBSA Analysis

The BCR-ABL tyrosine kinase which is responsible for the pathogenesis of chronic myeloid leukemia (CML), has emerged as a promising therapeutic target. To address this issue, we employed a comprehensive computational approach integrating virtual screening, molecular dynamics (MD) simulations, and MM-GBSA (Molecular Mechanics/Generalized Born Surface Area) analysis to identify potential inhibitors and elucidate their binding mechanisms. Initially, virtual screening was conducted on 994 compounds from the ZINC database and, these compounds were docked against wildtype and T315I mutant ABL1 for the Type I and Type II ABL1 kinase inhibition mechanisms. In our molecular docking analysis for Type I inhibition, compound 911 demonstrated notable affinity towards the wildtype ABL1, with a binding energy of -14.91 kcal/mol, while compound 972 showed significant binding affinity towards the mutant ABL1, with a binding energy of -14.27 kcal/mol. In the Type II inhibition mechanism, the compounds with the highest binding affinity were compound 261 in wildtype ABL1 with -17.05 kcal/mol binding energy and compound 966 to the mutant ABL1 with a binding energy of -16.29 kcal/mol. Furthermore, analyses of MD simulations and MM/GBSA binding free energy (ΔG) were performed for target proteins with compounds, that exhibited the most favorable binding affinities with target proteins. The selected hit compounds showed ΔG scores ranging from -118.09 to -74.85 kJ/mol in both wildtype and mutant ABL1. Considering all in silico studies performed, it can be inferred that the identified molecules hold promise as potential candidates for drug design aimed at targeting CML.

Virtual screening combined with molecular docking for the !identification of new anti-adipogenic compounds

Obesity is an important risk factor for diabetes, cardiovascular diseases, and cancer, reducing the quality of life and expectancy of millions of people. Consequently, obesity has turned into one of the most health public problems worldwide, which highlights the urgent need for new and safe treatments. Obesity is mainly related to excessive fat accumulation; therefore, proteins participating in white adipose tissue increase and dysfunction are considered pertinent and attractive targets for developing new methods that can help with body weight control. In this context, virtual screening of libraries containing a large number of molecules represents a valuable strategy for the identification of potential anti-adipogenic compounds with reduced costs and time production. Here, we review the scientific literature about the prediction of new ligands of specific proteins through molecular docking and virtual screening of chemical libraries, with the aim of proposing new potential anti-adipogenic molecules. First, we present the targets related to adipogenesis and adipocyte functions that were selected for the following studies: PPARγ, Crif1, SIRT1, ERβ, PC1, FTO, Mss51, and FABP4. Then, we describe the obtention of new ligands according to the characteristics of the virtual screening approach, i.e. a structure-based drug design (SBDD) or a ligand-based drug design (LBDD). Finally, the critical analysis of these computational strategies and the corresponding results points out the necessity of combining computational and in vitro or in vivo assays for the identification of effective new anti-adipogenic molecules for obesity control. It also evidences that translating molecular docking and virtual screening results into successful drug candidates for adipogenesis and obesity control remains a huge challenge.

In Silico Conotoxin Studies: Progress and Prospects

Cone snails of the genus Conus have evolved to produce structurally distinct and functionally diverse venom peptides for defensive and predatory purposes. This nature-devised delicacy enlightened drug discovery and for decades, the bioactive cone snail venom peptides, known as conotoxins, have been widely explored for their therapeutic potential, yet we know very little about them. With the augmentation of computational algorithms from the realms of bioinformatics and machine learning, in silico strategies have made substantial contributions to facilitate conotoxin studies although still with certain limitations. In this review, we made a bibliometric analysis of in silico conotoxin studies from 2004 to 2024 and then discussed in silico strategies to not only efficiently classify conotoxin superfamilies but also speed up drug discovery from conotoxins, reveal binding modes of known conotoxin-ion channel interactions at a microscopic level and relate the mechanisms of ion channel modulation to its underlying molecular structure. We summarized the current progress of studies in this field and gave an outlook on prospects.

Kinetics-Based State Definitions for Discrete Binding Conformations of T4 L99A in MD via Markov State Modeling

As a model system, the binding pocket of the L99A mutant of T4 lysozyme has been the subject of numerous computational free energy studies. However, previous studies have failed to fully sample and account for the observed changes in the binding pocket of T4 L99A upon binding of a congeneric ligand series, limiting the accuracy of results. In this work, we resolve the closed, intermediate, and open states for T4 L99A previously reported in experiment in MD and establish definitions for these states based on the dynamics of the system. From this analysis, we arrive at two primary conclusions. First, assignment of simulation trajectories into discrete states should not be done simply based on RMSD to crystal structures as this can result in misassignment of states. Second, the different metastable conformations studied here need to be carefully treated, as we estimate the time scales for conformational interconversion to be on the order of 102 to 103 ns─far longer than time scales for typical binding calculations. We conclude with a discussion on the need to develop enhanced sampling methods to generally account for significant changes in protein conformation due to relatively small ligand perturbations.

Input Pose is Key to Performance of Free Energy Perturbation: Benchmarking with Monoacylglycerol Lipase

Free energy perturbation (FEP) methodologies have become commonplace methods for modeling potency in hit-to-lead and lead optimization stages of drug discovery. The conformational states of the initial poses of compounds for FEP+ calculations are often set up by alignment to a cocrystal structure ligand, but it is not clear if this method provides the best result for all proteins or all ligands. Not only are ligand conformational states potential variables in modeling compound potency in FEP but also the selection of crystallographic water molecules for inclusion in the FEP input structures can impact FEP models. Here, we report the results of FEP calculations using FEP+ from Schrödinger and starting from maximum common substructure alignment and docked poses generated with an array of docking methodologies. As a benchmark data set, we use monoacylglycerol lipase (MAGL), an important clinical drug target in cancer malignancy, neurological diseases, and metabolic disorders, and a set of 17 MAGL inhibitors. We found a large variation among FEP+ correlations to experimental IC50 values depending on the method used to generate the input pose and that the inclusion of ligand-based information in the docking process, with some methods, increases the correlation between FEP+ free energies and IC50 values. Upon analysis of the initial poses, we found that the differences in FEP+ correlations stemmed from rotation around a tertiary amide bond as well as translation of the compound toward the more hydrophobic side of the MAGL pocket. FEP+ estimation improved across all pose modeling methods when hydrogen bond constraint information was added. However, simple maximum common substructure alignment in the presence of all crystallographic water molecules outperformed all other methods in correlation between estimated and experimental IC50 values. Taken together, these findings suggest that pose selection and crystallographic water inclusion greatly impact how well FEP+ estimated IC50 values align with experimental IC50 values and that modelers should benchmark a few different pose generation methodologies and different water inclusion strategies for their hit-to-lead and lead optimization drug discovery projects.

In Silico Exploration of Isoxazole Derivatives of Usnic Acid: Novel Therapeutic Prospects Against α-Amylase for Diabetes Treatment

Diabetes mellitus (DM) a metabolic disorder characterized by high blood sugar levels causing damage to various organs over time. Current anti-diabetic drugs have limitations and side effects, prompting a search for new inhibitors targeting the α-amylase enzyme. This study aims to discover such inhibitors from thirty isoxazole derivatives of usnic acid using in silico approaches. The potential inhibitory effects of compounds were investigated using ADMET, molecular docking, molecular dynamic simulation, principal component analysis and density functional theory studies. ADMET analysis exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities with no significant side effects which were then investigated using molecular docking experiment to determine the lead compound with the best binding affinity for the α-amylase enzyme. All compounds showed good binding affinity against α-amylase enzyme (-7.9 to -9.2 kcal/mol) where compound-13 showed the best binding affinity of -9.2 kcal/mol forming hydrogen bonds with Leu162, Tyr62, Glu233 and Asp300 amino acids. Furthermore, the binding posture and the stability of the compound-13-α-amylase enzyme complex was confirmed by molecular dynamic simulation experiment. Moreover, compound-13 showed binding energy value of -27.92 ± 5.61 kcal/mol, which indicated it could be an α-amylase inhibitor. Additionally, the reactivity of compound-13 was further confirmed by density functional theory analysis. The above findings suggest compound-13 to be a potential α-amylase inhibitor in DM. And setting the stage for further in vitro and in vivo experimental validation.

WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate Dataset Collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets; WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening; Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at WelQrate.org.

Discovery of novel MLK4 inhibitors against colorectal cancer through computational approaches

Colorectal cancer (CRC) is a significant health issue globally, affecting approximately 10 % of the world's population. The prevalence of CRC highlights the need for effective treatments and prevention strategies. The current therapeutic option, such as chemotherapy, has significant side effects. Thus, this study investigated the anticancer properties of Sanguinarine derivatives, an alkaloid found in traditional herbs via chemoinformatic approaches. Six Sanguinarine derivatives were discovered through virtual screening and molecular docking to determine their binding affinities against the mixed lineage kinase (MLK4) protein which is responsible for CRC. All the compounds were found to be more effective than standard drug used for colorectal cancer treatment, with Sanguinarine derivative 11 showing the highest affinity. The stability of the drug was confirmed through molecular dynamics simulations at 500 ns. This suggests that compound 11 has a higher chance of replacing 5-Fluorouracil, which is currently a widely used chemotherapy drug. Before molecular dynamics simulations, the pharmacokinetic and chemical properties of Sanguinarine derivatives were determined using pkCSM server and DFT method, respectively. The results support that compound 11 is a good drug candidate, as evidenced by Lipinski's Rule of Five. Therefore, compound 11 is recommended for further analysis via in vivo and in vitro studies to confirm its efficacy and safety.

QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition

BACKGROUND/OBJECTIVES

HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases.

METHODS

We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets.

RESULTS

We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥ 8 (IC50 values ≤ 10 nM). Our svm-based ensemble model predicted IC50 values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome.

CONCLUSIONS

Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

A Comprehensive Review on Potential In Silico Screened Herbal Bioactive Compounds and Host Targets in the Cardiovascular Disease Therapy

Herbal medicines (HMs) have deciphered indispensable therapeutic effects against cardiovascular disease (CVD) (the predominant cause of death worldwide). The conventional CVD therapy approaches have not been efficient and need alternative medicines. The objective of this study was a review of herbal bioactive compound efficacy for CVD therapy based on computational and in silico studies. HM bioactive compounds with potential anti-CVD traits include campesterol, naringenin, quercetin, stigmasterol, tanshinaldehyde, Bryophyllin A, Bryophyllin B, beta-sitosterol, punicalagin, butein, eriodyctiol, butin, luteolin, and kaempferol discovered using computational studies. Some of the bioactive compounds have exhibited therapeutic effects, as followed by in vitro (tanshinaldehyde, punicalagin, butein, eriodyctiol, and butin), in vivo (gallogen, luteolin, chebulic acid, butein, eriodyctiol, and butin), and clinical trials (quercetin, campesterol, and naringenin). The main mechanisms of action of bioactive compounds for CVD healing include cell signaling and inhibition of inflammation and oxidative stress, decrease of lipid accumulation, and regulation of metabolism and immune cells. Further experimental studies are required to verify the anti-CVD effects of herbal bioactive compounds and their pharmacokinetic/pharmacodynamic features.

Updating the Pharmacological Effects of α-Mangostin Compound and Unraveling Its Mechanism of Action: A Computational Study Review

α-Mangostin, initially identified in 1855, is a xanthone derivative compound predominantly located in the pericarp of the mangosteen fruit (Garcinia mangostana L). This compound is known for its beneficial properties as an antioxidant and anti-inflammatory agent, still holding promise for potential benefits in other related pathologies. In the investigative process, computational studies have proven highly valuable in providing evidence and initial screening before progressing to preclinical and clinical studies. This review aims to present the pharmacological findings and mechanisms of action of α-mangostin based on computational studies. The compilation of this review is founded on the analysis of relevant articles obtained from PubMed, Scopus, and ScienceDirect databases. The study commences with an elucidation of the physicochemical characteristics, drug-likeness, pharmacokinetics, and toxicity profile of α-mangostin, which demonstrates that α-mangostin complies with the Lipinski's Rule of Five, exhibits favorable profiles of absorption, distribution, metabolism, and excretion, and presents low toxicity. Subsequent investigations have revealed that computational studies employing various software tools including ArgusLab, AutoDock, AutoDock Vina, Glide, HEX, and MOE, have been pivotal to comprehend the pharmacology of α-mangostin. Beyond its well established roles as an antioxidant and anti-inflammatory agent, α-mangostin is now recognized for its pharmacological effects in Alzheimer's disease, diabetes, cancer, chronic kidney disease, chronic periodontitis, infectious diseases, and rheumatoid arthritis. Moreover, α-mangostin is projected to have applications in pain management and as a potent mosquito larvicide. All of these findings are based on the attainment of adequate binding affinity to specific target receptors associated with each respective pathological condition. Consequently, it is anticipated that these findings will serve as a foundation for future scientific endeavours, encompassing both in vitro and in vivo studies, as well as clinical investigations, to better understand the pharmacological effects of α-mangostin.

Recent advances from computer-aided drug design to artificial intelligence drug design

Computer-aided drug design (CADD), a cornerstone of modern drug discovery, can predict how a molecular structure relates to its activity and interacts with its target using structure-based and ligand-based methods. Fueled by ever-increasing data availability and continuous model optimization, artificial intelligence drug design (AIDD), as an enhanced iteration of CADD, has thrived in the past decade. AIDD demonstrates unprecedented opportunities in protein folding, property prediction, and molecular generation. It can also facilitate target identification, high-throughput screening (HTS), and synthetic route prediction. With AIDD involved, the process of drug discovery is greatly accelerated. Notably, AIDD offers the potential to explore uncharted territories of chemical space beyond current knowledge. In this perspective, we began by briefly outlining the main workflows and components of CADD. Then through showcasing exemplary cases driven by AIDD in recent years, we describe the evolving role of artificial intelligence (AI) in drug discovery from three distinct stages, that is, chemical library screening, linker generation, and de novo molecular generation. In this process, we attempted to draw comparisons between the features of CADD and AIDD.

SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CLpro and RdRp viral proteins

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy.

Identification of small molecule inhibitors of the Chloracidobacterium thermophilum type IV pilus protein PilB by ensemble virtual screening

Antivirulence strategy has been explored as an alternative to traditional antibiotic development. The bacterial type IV pilus is a virulence factor involved in host invasion and colonization in many antibiotic resistant pathogens. The PilB ATPase hydrolyzes ATP to drive the assembly of the pilus filament from pilin subunits. We evaluated Chloracidobacterium thermophilum PilB (CtPilB) as a model for structure-based virtual screening by molecular docking and molecular dynamics (MD) simulations. A hexameric structure of CtPilB was generated through homology modeling based on an existing crystal structure of a PilB from Geobacter metallireducens. Four representative structures were obtained from molecular dynamics simulations to examine the conformational plasticity of PilB and improve docking analyses by ensemble docking. Structural analyses after 1 μs of simulation revealed conformational changes in individual PilB subunits are dependent on ligand presence. Further, ensemble virtual screening of a library of 4234 compounds retrieved from the ZINC15 database identified five promising PilB inhibitors. Molecular docking and binding analyses using the four representative structures from MD simulations revealed that top-ranked compounds interact with multiple Walker A residues, one Asp-box residue, and one arginine finger, indicating these are key residues in inhibitor binding within the ATP binding pocket. The use of multiple conformations in molecular screening can provide greater insight into compound flexibility within receptor sites and better inform future drug development for therapeutics targeting the type IV pilus assembly ATPase.

New combined Inverse-QSAR and molecular docking method for scaffold-based drug discovery

Computer-aided drug discovery plays a vital role in developing novel medications for various diseases. The COVID-19 pandemic has heightened the need for innovative approaches to design lead compounds with the potential to become effective drugs. Specifically, designing promising inhibitors of the SARS-CoV-2 main protease (Mpro) is crucial, as it plays a key role in viral replication. Phytochemicals, primarily flavonoids and flavonols from medicinal plants, were screened. Fifty small molecules were selected for molecular docking analysis against SARS-CoV-2 Mpro (PDB ID: 6LU7). Binding energies and interactions were analyzed and compared to those of the anti-SARS-CoV-2 inhibitor Nirmatrelvir. Using these 50 structures as a training set, a QSAR model was built employing simple, reversible topological descriptors. An inverse-QSAR analysis was then performed on 2⁹ = 512 hydroxyl combinations at nine possible positions on the flavone and flavonol scaffold. The model predicted three novel, promising compounds exhibiting the most favorable binding energies (-8.5 kcal/mol) among the 512 possible hydroxyl combinations: 3,6,7,2',4'-pentahydroxyflavone (PF9), 6,7,2',4'-tetrahydroxyflavone (PF11), and 3,6,7,4'-tetrahydroxyflavone (PF15). Molecular dynamics (MD) simulations demonstrated the stability of the PF9/Mpro complex over 300 ns of simulation. These predicted structures, reported here for the first time, warrant synthesis and further evaluation of their biological activity through in vitro and in vivo studies.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

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

Combining the Fragment Molecular Orbital and GRID Approaches for the Prediction of Ligand-Metalloenzyme Binding Affinity: The Case Study of hCA II Inhibitors

Polarization and charge-transfer interactions play an important role in ligand-receptor complexes containing metals, and only quantum mechanics methods can adequately describe their contribution to the binding energy. In this work, we selected a set of benzenesulfonamide ligands of human Carbonic Anhydrase II (hCA II)-an important druggable target containing a Zn2+ ion in the active site-as a case study to predict the binding free energy in metalloprotein-ligand complexes and designed specialized computational methods that combine the ab initio fragment molecular orbital (FMO) method and GRID approach. To reproduce the experimental binding free energy in these systems, we adopted a machine-learning approach, here named formula generator (FG), considering different FMO energy terms, the hydrophobic interaction energy (computed by GRID) and logP. The main advantage of the FG approach is that it can find nonlinear relations between the energy terms used to predict the binding free energy, explicitly showing their mathematical relation. This work showed the effectiveness of the FG approach, and therefore, it might represent an important tool for the development of new scoring functions. Indeed, our scoring function showed a high correlation with the experimental binding free energy (R2 = 0.76-0.95, RMSE = 0.34-0.18), revealing a nonlinear relation between energy terms and highlighting the relevant role played by hydrophobic contacts. These results, along with the FMO characterization of ligand-receptor interactions, represent important information to support the design of new and potent hCA II inhibitors.

Quantum Molecular Docking with a Quantum-Inspired Algorithm

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.

Data set of fraction unbound values in the in vitro incubations for metabolic studies for better prediction of human clearance

In vitro-in vivo extrapolation is a commonly applied technique for liver clearance prediction. Various in vitro models are available such as hepatocytes, human liver microsomes, or recombinant cytochromes P450. According to the free drug theory, only the unbound fraction (fu) of a chemical can undergo metabolic changes. Therefore, to ensure the reliability of predictions, both specific and nonspecific binding in the model should be accounted. However, the fraction unbound in the experiment is often not reported. The study aimed to provide a detailed repository of the literature data on the compound's fu value in various in vitro systems used for drug metabolism evaluation and corresponding human plasma binding levels. Data on the free fraction in plasma and different in vitro models were supplemented with the following information: the experimental method used for the assessment of the degree of drug binding, protein or cell concentration in the incubation, and other experimental conditions, if different from the standard ones, species, reference to the source publication, and the author's name and date of publication. In total, we collected 129 literature studies on 1425 different compounds. The provided data set can be used as a reference for scientists involved in pharmacokinetic/physiologically based pharmacokinetic modelling as well as researchers interested in Quantitative Structure-Activity Relationship models for the prediction of fraction unbound based on compound structure. Database URL: https://data.mendeley.com/datasets/3bs5526htd/1.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Identification of ligands binding to MB327-PAM-1, a binding pocket relevant for resensitization of nAChRs

Desensitization of nicotinic acetylcholine receptors (nAChRs) can be induced by overstimulation with acetylcholine (ACh) caused by an insufficient degradation of ACh after poisoning with organophosphorus compounds (OPCs). Currently, there is no generally applicable treatment for OPC poisoning that directly targets the desensitized nAChR. The bispyridinium compound MB327, an allosteric modulator of nAChR, has been shown to act as a resensitizer of nAChRs, indicating that drugs binding directly to nAChRs can have beneficial effects after OPC poisoning. However, MB327 also acts as an inhibitor of nAChRs at higher concentrations and can thus not be used for OPC poisoning treatment. Consequently, novel, more potent resensitizers are required. To successfully design novel ligands, the knowledge of the binding site is of utmost importance. Recently, we performed in silico studies to identify a new potential binding site of MB327, MB327-PAM-1, for which a more affine ligand, UNC0646, has been described. In this work, we performed ligand-based screening approaches to identify novel analogs of UNC0646 to help further understand the structure-affinity relationship of this compound class. Furthermore, we used structure-based screenings and identified compounds representing four new chemotypes binding to MB327-PAM-1. One of these compounds, cycloguanil, is the active metabolite of the antimalaria drug proguanil and shows a higher affinity towards MB327-PAM-1 than MB327. Furthermore, cycloguanil can reestablish the muscle force in soman-inhibited rat muscles. These results can act as a starting point to develop more potent resensitizers of nAChR and to close the gap in the treatment after OPC poisoning.

Efficient molecular conformation generation with quantum-inspired algorithm

CONTEXT

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature.

METHODS

The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

AI-based prediction of protein-ligand binding affinity and discovery of potential natural product inhibitors against ERK2

Determination of protein-ligand binding affinity (PLA) is a key technological tool in hit discovery and lead optimization, which is critical to the drug development process. PLA can be determined directly by experimental methods, but it is time-consuming and costly. In recent years, deep learning has been widely applied to PLA prediction, the key of which lies in the comprehensive and accurate representation of proteins and ligands. In this study, we proposed a multi-modal deep learning model based on the early fusion strategy, called DeepLIP, to improve PLA prediction by integrating multi-level information, and further used it for virtual screening of extracellular signal-regulated protein kinase 2 (ERK2), an ideal target for cancer treatment. Experimental results from model evaluation showed that DeepLIP achieved superior performance compared to state-of-the-art methods on the widely used benchmark dataset. In addition, by combining previously developed machine learning models and molecular dynamics simulation, we screened three novel hits from a drug-like natural product library. These compounds not only had favorable physicochemical properties, but also bound stably to the target protein. We believe they have the potential to serve as starting molecules for the development of ERK2 inhibitors.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

An In Vivo and In Silico Approach Reveals Possible Sodium Channel Nav1.2 Inhibitors from Ficus religiosa as a Novel Treatment for Epilepsy

Epilepsy is a neurological disease that affects approximately 50 million people worldwide. Despite an existing abundance of antiepileptic drugs, lifelong disease treatment is often required but could be improved with alternative drugs that have fewer side effects. Given that epileptic seizures stem from abnormal neuronal discharges predominately modulated by the human sodium channel Nav1.2, the quest for novel and potent Nav1.2 blockers holds promise for epilepsy management. Herein, an in vivo approach was used to detect new antiepileptic compounds using the maximum electroshock test on mice. Pre-treatment of mice with extracts from the Ficus religiosa plant ameliorated the tonic hind limb extensor phase of induced convulsions. Subsequently, an in silico approach identified potential Nav1.2 blocking compounds from F. religiosa using a combination of computational techniques, including molecular docking, prime molecular mechanics/generalized Born surface area (MM/GBSA) analysis, and molecular dynamics (MD) simulation studies. The molecular docking and MM/GBSA analysis indicated that out of 82 compounds known to be present in F. religiosa, seven exhibited relatively strong binding affinities to Nav1.2 that ranged from -6.555 to -13.476 kcal/mol; similar or with higher affinity than phenytoin (-6.660 kcal/mol), a known Na+-channel blocking antiepileptic drug. Furthermore, MD simulations revealed that two compounds: 6-C-glucosyl-8-C-arabinosyl apigenin and pelargonidin-3-rhamnoside could form stable complexes with Nav1.2 at 300 K, indicating their potential as lead antiepileptic agents. In summary, the combination of in vivo and in silico approaches supports the potential of F. religiosa phytochemicals as natural antiepileptic therapeutic agents.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Describing the ligandin properties of Plasmodium falciparum and vivax glutathione transferase towards bromosulfophthalein from empirical and computational modelling viewpoints

Research has spotlighted glutathione transferase (GST) as a promising target for antimalarial drug development due to its pivotal role in cellular processes, including metabolizing toxins and managing oxidative stress. This interest arises from GST's potential to combat multidrug resistance in existing antimalarial drugs. Plasmodium falciparum GST (PfGST) and Plasmodium vivax GST (PvGST) are key targets; inhibiting them not only disrupt detoxification but also reduce their antioxidant capacity, a critical feature for potent antimalarials. Bromosulfophthalein (BSP), a clinical liver function dye, emerged as a potent cytosolic GST inhibitor. This study explored BSP's inhibitory properties on PfGST and PvGST, showcasing its binding capabilities through empirical and computational analyses. The study revealed BSP's ability to significantly inhibit GST activity, altering the proteins' structures and stability. Specifically, BSP binding induced spectral changes and impacted the proteins' thermal stability, reducing their melting temperatures. Computational simulations highlighted BSP's strong binding to PfGST and PvGST at their dimer interface, stabilized by various interactions, including hydrogen bonds and van der Waals forces. Notably, BSP's binding altered the proteins' compactness and conformational dynamics, suggesting a potential non-competitive, allosteric inhibition mechanism. This study provided novel insights into BSP's candidacy as an antimalarial drug by targeting PfGST and PvGST. Its ability to disrupt crucial functions of these enzymes' positions BSP as a promising candidate for further drug development in combating malaria.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Application of Deep Learning for Studying NMDA Receptors

Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, such as ChatGPT, has even begun to demonstrate human-level performance in certain tasks.However, the difficulties of deploying and utilizing AI and pretrained model for nonexpert limited its practical use. To overcome this challenge, here we presented three highly accessible online tools based on a large pretrained model for chemistry, the Uni-Mol, for drug development against CNS diseases, including those targeting NMDA receptor: the blood-brain barrier (BBB) permeability prediction, the quantitative structure-activity relationship (QSAR) analysis system, and a versatile interface of the AI-based molecule generation model named VD-gen. We believe that these resources will effectively bridge the gap between cutting-edge AI technology and NMDAR experts, facilitating rapid and rational drug development.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Data insights from a Moroccan phytochemical database (MPDB) derived from aromatic & medicinal plants

The geographical location of Morocco and the diversity of its topography ensure a high variability of climate conditions, ranging from humid to Saharan, and extending through subhumid, arid, and semi-arid stages. This variability offers a high floristic diversity, while the medical use of these phytochemicals has not been fully explored. Advanced computer-aided drug discovery utilizes chemical biology to accelerate the study of phytochemicals at the molecular level and discover novel therapeutic pathways. Currently, there is no online resource for phytochemicals in Morocco. Therefore, it is of interest to describe the Moroccan Phytochemicals Database (MPDB), accessible, featuring over 600 phytochemicals derived from journal articles and other reports. The web interface of the database, which is simple and easy to use, provides each phytochemical's reference, plant sources, 3D structures, and all related information. Furthermore, we provide direct links to commercially available analogs from Mcule. In addition, we provide the results of the first virtual screening against cardiovascular targets. We present these data to facilitate further exploration and exploitation of Morocco's rich phytochemical resources, and to contribute to the global understanding and application of these compounds in the medical and scientific communities.

Scipion-Chem: An Open Platform for Virtual Drug Screening

Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.

Identification of paeoniflorin from Paeonia lactiflora pall. As an inhibitor of tryptophan 2,3-dioxygenase and assessment of its pharmacological effects on depressive mice

ETHNOPHARMACOLOGICAL RELEVANCE

The radix of Paeonia lactiflora Pall. (PaeR) is a traditional Chinese medicine (TCM) clinically used for treating depression. Although it has been established that PaeR can protect the liver and alleviate depressive-like behaviors, its bioactive chemicals and antidepressant mechanism remain unclear. Our pilot study showed that PaeR reduced the expression of the L-tryptophan- catabolizing enzyme tryptophan 2,3-dioxygenase (TDO) in the livers of stress-induced depression-like mice.

AIM OF THE STUDY

This study aimed to screen potential TDO inhibitors from PaeR and investigate the potential therapeutic use of TDO inhibition for treating depression.

MATERIALS AND METHODS

Molecular docking, magnetic ligand fishing, and secrete-pair dual luminescence assay were conducted for in vitro ligand discovery and high-throughput screening of TDO inhibitors. Stable TDO overexpression was achieved in HepG2 cell lines to evaluate the TDO inhibitory activities of drugs in vitro by RT-PCR and Western blot analyses of TDO at mRNA and protein levels. In vivo validation of TDO inhibitory potency and evaluation of TDO inhibition as a potential therapeutic strategy for major depressive disorder (MDD) were performed using mice subjected to "3 + 1″ combined stresses for at least 30 days to induce depression-like behaviors. A well-known TDO inhibitor, LM10, was evaluated in parallel.

RESULTS

The PaeR extract significantly ameliorated depressive-like behaviors of stressed mice, attributed to inhibition of TDO expression and tryptophan modulation metabolism. After a comprehensive analysis of molecular docking, ligand fishing, and luciferase assay, paeoniflorin was screened as a TDO inhibitor from the PaeR extract. This compound, structurally different from LM10, potently inhibited human and mouse TDO in cell- and animal-based assays. The effects of TDO inhibitors on MDD symptoms were evaluated in a stress-induced depression-like mouse model. In mice, both inhibitors had beneficial effects on stress-induced depressive-like behavioral despair and unhealthy physical status. Moreover, both inhibitors increased the liver serotonin/tryptophan ratio and decreased the kynurenine/tryptophan ratio after oral administration, demonstrating in vivo inhibition of TDO activity. Our data substantiated the potential of TDO inhibition as a therapeutic strategy to improve behavioral activity and decrease despair symptoms in major depressive disorder.

CONCLUSIONS

This study introduced a hitherto undocumented comprehensive screening strategy to identify TDO inhibitors in PaeR extract. Our findings also highlighted the potential of PaeR as a source of antidepressant constituents and pinpointed the inhibition of TDO as a promising therapeutic approach for managing major depressive disorder.