A Review on Applications of Computational Methods in Drug Screening and Design

Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors

Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.

Anticancer potential of mebendazole against chronic myeloid leukemia: in silico and in vitro studies revealed new insights about the mechanism of action

Chronic myeloid leukemia (CML) is caused by constitutively active fusion protein BCR-ABL1, and targeting ABL1 is a promising therapy option. Imatinib, dasatinib, and nilotinib have all been shown to work effectively in clinical trials. ABL1 mutations, particularly the T315I gate-keeper mutation, cause resistance in patients. As a result, broad-spectrum ABL1 medicines are desperately needed. In order to screen potential drugs targeting CML, mebendazole (MBZ) was subjected to the in vitro test against CML cell lines (K562 and FEPS) and computational assays. The antiproliferative effect of MBZ and the combination with tyrosine kinase inhibitors (TKIs) was tested using end-point viability assays, cell cycle distribution analysis, cell membrane, and mitochondrial dyes. By interrupting the cell cycle and causing cell death, MBZ and its combination with imatinib and dasatinib have a significant antiproliferative effect. We identified MBZ as a promising “new use” drug targeting wild-type and mutant ABL1 using molecular docking. Meanwhile, we determined which residues in the allosteric site are important in ABL1 drug development. These findings may not only serve as a model for repositioning current authorized medications but may also provide ABL1-targeted anti-CML treatments a fresh lease of life.

Molecular Modeling and Simulation of the Peptidoglycan Layer of Gram-Positive Bacteria Staphylococcus aureus

The peptidoglycan (PG) layer is a vital component of the bacterial cell wall that protects the cell from rupturing due to internal pressure. Its ubiquity across the bacterial kingdom but not animals has made it the target of drug discovery efforts. The PG layer composed of cross-linked PG strands is porous enough to allow the diffusion of molecules through the PG mesh and into the cell. The lack of an accurate atomistic model of the PG mesh has limited the computational investigations of drug diffusion in Gram-positive bacteria, which lack the outer membrane but consist of a much thicker PG layer compared to Gram-negative bacteria. In this work, we built an atomistic model of the Staphylococcus aureus PG layer architecture with horizontally aligned PG strands and performed molecular dynamics simulations of the diffusion of curcumin molecules through the PG mesh. An accurate model of the Gram-positive bacterial cell wall may aid in developing novel antibiotics to tackle the threat posed by antibiotic resistance.

Structural-Guided Identification of Small Molecule Inhibitor of UHRF1 Methyltransferase Activity

Ubiquitin-like containing plant homeodomain Ring Finger 1 (UHRF1) protein is recognized as a cell-cycle-regulated multidomain protein. UHRF1 importantly manifests the maintenance of DNA methylation mediated by the interaction between its SRA (SET and RING associated) domain and DNA methyltransferase-1 (DNMT1)-like epigenetic modulators. However, overexpression of UHRF1 epigenetically responds to the aberrant global methylation and promotes tumorigenesis. To date, no potential molecular inhibitor has been studied against the SRA domain. Therefore, this study focused on identifying the active natural drug-like candidates against the SRA domain. A comprehensive set of in silico approaches including molecular docking, molecular dynamics (MD) simulation, and toxicity analysis was performed to identify potential candidates. A dataset of 709 natural compounds was screened through molecular docking where chicoric acid and nystose have been found showing higher binding affinities to the SRA domain. The MD simulations also showed the protein ligand interaction stability of and in silico toxicity analysis has also showed chicoric acid as a safe and nontoxic drug. In addition, chicoric acid possessed a longer interaction time and higher LD50 of 5000 mg/kg. Moreover, the global methylation level (%5 mC) has been assessed after chicoric acid treatment was in the colorectal cancer cell line (HCT116) at different doses. The result showed that 7.5 µM chicoric acid treatment reduced methylation levels significantly. Thus, the study found chicoric acid can become a possible epidrug-like inhibitor against the SRA domain of UHRF1 protein.

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Discovery of New Inhibitors of eEF2K from Traditional Chinese Medicine Based on In Silico Screening and In Vitro Experimental Validation

Eukaryotic elongation factor 2 kinase (eEF2K) is a highly conserved α kinase and is increasingly considered as an attractive therapeutic target for cancer as well as other diseases. However, so far, no selective and potent inhibitors of eEF2K have been identified. In this study, pharmacophore screening, homology modeling, and molecular docking methods were adopted to screen novel inhibitor hits of eEF2K from the traditional Chinese medicine database (TCMD), and then cytotoxicity assay and western blotting were performed to verify the validity of the screen. Resultantly, after two steps of screening, a total of 1077 chemicals were obtained as inhibitor hits for eEF2K from all 23,034 compounds in TCMD. Then, to verify the validity, the top 10 purchasable chemicals were further analyzed. Afterward, Oleuropein and Rhoifolin, two reported antitumor chemicals, were found to have low cytotoxicity but potent inhibitory effects on eEF2K activity. Finally, molecular dynamics simulation, pharmacokinetic and toxicological analyses were conducted to evaluate the property and potential of Oleuropein and Rhoifolin to be drugs. Together, by integrating in silico screening and in vitro biochemical studies, Oleuropein and Rhoifolin were revealed as novel eEF2K inhibitors, which will shed new lights for eEF2K-targeting drug development and anticancer therapy.

A unique peptide-based pharmacophore identifies an inhibitory compound against the A-subunit of Shiga toxin

Shiga toxin (Stx), a major virulence factor of enterohemorrhagic Escherichia coli (EHEC), can cause fatal systemic complications. Recently, we identified a potent inhibitory peptide that binds to the catalytic A-subunit of Stx. Here, using biochemical structural analysis and X-ray crystallography, we determined a minimal essential peptide motif that occupies the catalytic cavity and is required for binding to the A-subunit of Stx2a, a highly virulent Stx subtype. Molecular dynamics simulations also identified the same motif and allowed determination of a unique pharmacophore for A-subunit binding. Notably, a series of synthetic peptides containing the motif efficiently inhibit Stx2a. In addition, pharmacophore screening and subsequent docking simulations ultimately identified nine Stx2a-interacting molecules out of a chemical compound database consisting of over 7,400,000 molecules. Critically, one of these molecules markedly inhibits Stx2a both in vitro and in vivo, clearly demonstrating the significance of the pharmacophore for identifying therapeutic agents against EHEC infection.

Boesenbergia Pandurata as an Anti-Breast Cancer Agent: Molecular Docking and ADMET Study

Background:

Boesenbergia pandurata or fingerroot is known to have various pharmacological activities, including anticancer properties. Extracts from these plants are known to inhibit the growth of cancer cells, including breast cancer. Anti-breast cancer activity is significantly influenced by the inhibition of two receptors: ER-α and HER2. However, it is unknown which metabolites of B. pandurata play the most crucial role in exerting anticancer activity.

Objective:

This study aimed to determine the metabolites of B. pandurata with the best potential as ER-α and HER2 inhibitors.

Method:

The method used was molecular docking of several B. pandurata metabolites to ER-α and HER2 receptors, followed by an ADMET study of several metabolites with the best docking results.

Results:

The docking results showed eight metabolites with the best docking results for the two receptors based on the docking score and ligand-receptor interactions. Of these eight compounds, compounds 11 ((2S)-7,8-dihydro-5-hydroxy-2-methyl-2-(4''-methyl-3''-pentenyl)-8-phenyl-2H,6H-benzo(1,2-b-5,4- b')dipyran-6-one) and 34 (geranyl-2,4-dihydroxy-6-phenethylbenzoate) showed the potential to inhibit both receptors. Both ADMET profiles also showed mixed results; however, there is a possibility of further development.

Conclusion:

In conclusion, the metabolites of B. pandurata, especially compounds 11 and 34, can be developed as anti-breast cancer agents by inhibiting ER-α and HER2.

The Role of Ingenane Diterpenes in Cancer Therapy: From Bioactive Secondary Compounds to Small Molecules

Diterpenes are a class of critical taxonomic markers of the Euphorbiaceae family, representing small compounds (eg, molecules) with a wide range of biological activities and multi-target therapeutic potential. Diterpenes can exert different activities, including antitumor and multi-drug resistance-reversing activities, and antiviral, immunomodulatory, and anti-inflammatory effects, mainly due to their great structural diversity. In particular, one polycyclic skeleton has been highlighted: ingenane. Besides this natural diterpene, promising polycyclic skeletons may be submitted to chemical modification—by in silico approaches, chemical reactions, or biotransformation—putatively providing more active analogs (eg, ingenol derivatives), which are currently under pre-clinical investigation. This review outlines the current mechanisms of action and potential therapeutic implications of ingenol diterpenes as small cancer molecules.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Mining therapeutic targets from the antibiotic-resistant Campylobacter coli and virtual screening of natural product inhibitors against its riboflavin synthase

Campylobacter coli resides in the intestine of several commonly consumed animals, as well as water and soil. It leads to campylobacteriosis when humans eat raw/undercooked meat or come into contact with infected animals. A common manifestation of the infection is fever, nausea, headache, and diarrhea. Increasing antibiotic resistance is being observed in this pathogen. The increased incidence of C. coli infection, and post-infection complications like Guillain-Barré syndrome, make it an important pathogen. It is essential to find novel therapeutic targets and drugs against it, especially with the emergence of antibiotic-resistant strains. In the current study, genomes of 89 antibiotic-resistant strains of C. coli were downloaded from the PATRIC database. Potent drug targets (n = 36) were prioritized from the core genome (n = 1,337 genes) of this species. Riboflavin synthase was selected as a drug target and pharmacophore-based virtual screening was performed to predict its inhibitors from the NPASS (n =  ~ 30,000 compounds) natural product library. The top three docked compounds (NPC115144, NPC307895, and NPC470462) were selected for dynamics simulation (for 50 ns) and ADMET profiling. These identified compounds appear safe for targeting this pathogen and can be further validated by experimental analysis before clinical trials.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Virtual screening based on pharmacophore model for developing novel HPPD inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an important target for herbicide design. A multilayered virtual screening workflow was constructed by combining two pharmacophore models based on ligand and crystal complexes, molecular docking, molecular dynamics (MD), and biological activity determination to identify novel small-molecule inhibitors of HPPD. About 110, 000 compounds of Bailingwei and traditional Chinese medicine databases were screened. Of these, 333 were analyzed through docking experiments. Five compounds were selected by analyzing the binding pattern of inhibitors with amino acid residues in the active pocket. All five compounds could produce stable coordination with cobalt ion, and form favorable π-π interactions. MD simulation demonstrated that Phe381 and Phe424 made large contributions to the strength of binding. The enzyme activity experiment verified that compound-139 displayed excellent potency against AtHPPD (IC₅₀ = 0.742 μM), however, compound-5222 had inhibitory effect on human HPPD (IC₅₀ = 6 nM). Compound-139 exhibited herbicidal activity to some extent on different gramineous weeds. This work provided a strong insight into the design and development of novel HPPD inhibitor using in silico techniques.

Improved drug-target interaction prediction with intermolecular graph transformer

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.

Toward the Discovery of a Novel Class of Leads for High Altitude Disorders by Virtual Screening and Molecular Dynamics Approaches Targeting Carbonic Anhydrase

For decades, carbonic anhydrase (CA) inhibitors, most notably the acetazolamide-bearing 1,3,4-thiadiazole moiety, have been exploited at high altitudes to alleviate acute mountain sickness, a syndrome of symptomatic sensitivity to the altitude characterized by nausea, lethargy, headache, anorexia, and inadequate sleep. Therefore, inhibition of CA may be a promising therapeutic strategy for high-altitude disorders. In this study, co-crystallized inhibitors with 1,3,4-thiadiazole, 1,3-benzothiazole, and 1,2,5-oxadiazole scaffolds were employed for pharmacophore-based virtual screening of the ZINC database, followed by molecular docking and molecular dynamics simulation studies against CA to find possible ligands that may emerge as promising inhibitors. Compared to the co-crystal ligands of PDB-1YDB, 6BCC, and 6IC2, ZINC12336992, ZINC24751284, and ZINC58324738 had the highest docking scores of -9.0, -9.0, and -8.9 kcal/mol, respectively. A molecular dynamics (MD) simulation analysis of 100 ns was conducted to verify the interactions of the top-scoring molecules with CA. The system's backbone revealed minor fluctuations, indicating that the CA-ligand complex was stable during the simulation period. Simulated trajectories were used for the MM-GBSA analysis, showing free binding energies of -16.00 ± 0.19, -21.04 ± 0.17, and -19.70 ± 0.18 kcal/mol, respectively. In addition, study of the frontier molecular orbitals of these compounds by DFT-based optimization at the level of B3LYP and the 6-311G(d,p) basis set showed negative values of the HOMO and LUMO, indicating that the ligands are energetically stable, which is essential for forming a stable ligand-protein complex. These molecules may prove to be a promising therapy for high-altitude disorders, necessitating further investigations.

Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes

To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.

Molecular Dynamics Simulations of Essential Oil Ingredients Associated with Hyperbranched Polymer Drug Carriers

Our work concerns the study of four candidate drug compounds of the terpenoid family, found as essential oil ingredients in species of the Greek endemic flora, namely carvacrol, p-cymene, γ-terpinene, and thymol, via the simulation method of molecular dynamics. Aquatic solutions of each compound, as well as a solution of all four together in realistic (experimental) proportions, are simulated at atmospheric pressure and 37 °C using an OPLS force field combined with TIP3P water. As verified, all four compounds exhibit a strong tendency to phase-separate, thereby calling for the use of carrier molecules as aids for the drug to circulate in the blood and enter the cells. Systems of two such carrier molecules, the hyperbranched poly(ethylene imine) (HBPEI) polyelectrolyte and hyperbranched polyglycerol (HPG), are examined in mixtures with carvacrol, the most abundant among the four compounds, at a range of concentrations, as well as with all four compounds present in natural proportions. Although a tendency of the terpenoids to cluster separately persists at high concentrations, promising association effects are observed for all drug–polymer ratios. HBPEI systems tend to form diffuse structures comprising small mixed clusters as well as freely floating polymer and essential oil molecules, a finding attributed to the polymer–polymer electrostatic repulsions, which here are only partially screened by the counterions. On the other hand, the electrically neutral HPG molecules cluster together with essential oil species to form a single nanodroplet. Currently, terpenoid–polymer clusters near lipid bilayer membranes are being studied to determine the propensity of the formed complexes to enter cell membranes.

In-silico elucidation reveals potential phytochemicals against angiotensin-converting enzyme 2 (ACE-2) receptor to fight coronavirus disease 2019 (COVID-19)

The coronavirus (SARS-CoV-2) pandemic is rapidly advancing and spreading worldwide, which poses an urgent need to develop anti-SARS-CoV-2 agents. A human receptor, namely, angiotensin-converting enzyme 2 (ACE-2), supports the SARS-CoV-2 entry, therefore, serves as a target for intervention via drug. In the current study, bioinformatic approaches were employed to screen potent bioactive compounds that might be ACE-2 receptor inhibitors. The employment of a docking study using ACE receptor protein with a ready-to-dock database of phytochemicals via MOE software revealed five compounds as potent molecules. Among them, astragaloside exhibited the highest binding affinity -21.8 kcal/mol and stable interactions within the active site of the ACE-2 receptor. Similarly, the phytochemicals such as pterocaryanin B, isoastragaloside II, and astraisoflavan glucoside followed by oleuropein showed a stronger binding affinity. We hypothesize these compounds as potential lead candidates for the development of anti- COVID-19 target-specific drugs.

Protein Folding Interdiction Strategy for Therapeutic Drug Development in Viral Diseases: Ebola VP40 and Influenza A M1

In a recent paper, we proposed the folding interdiction target region (FITR) strategy for therapeutic drug design in SARS-CoV-2. This paper expands the application of the FITR strategy by proposing therapeutic drug design approaches against Ebola virus disease and influenza A. We predict target regions for folding interdicting drugs on correspondingly relevant structural proteins of both pathogenic viruses: VP40 of Ebola, and matrix protein M1 of influenza A. Identification of the protein targets employs the sequential collapse model (SCM) for protein folding. It is explained that the model predicts natural peptide candidates in each case from which to start the search for therapeutic drugs. The paper also discusses how these predictions could be tested, as well as some challenges likely to be found when designing effective therapeutic drugs from the proposed peptide candidates. The FITR strategy opens a potential new avenue for the design of therapeutic drugs that promises to be effective against infectious diseases.

Establishment of models for reliability evaluation of 3CLpro ligand-receptor complexes with different binding sites

Aim: Recent research shows that 3CLpro enzyme of SARS-CoV-2 is a significant target against COVID-19. Drug modeling allows the design of inhibitors of 3CLpro, but the accuracy of those methods remains unclear. Therefore, it is important to determine the trustworthiness of the designed ligand-receptor complexes. Method & materials: The authors built models for the reliability evaluation of 3CLpro complexes with ligands using an in-house developed AlteQ approach and complementarity principles. The models were based on 145 experimentally found 3CLpro complexes with ligands for five different binding sites. Result & conclusion: The obtained models correspond to linear regression with high values of correlation coefficients and can be successfully used to determine the reliability of the docked 3CLpro complexes with ligands.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction

MOTIVATION

Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors. However, most of the previous methods do not consider the topological structures of multi-order neighbors. In addition, deep integration of the multi-modality similarities of drugs and proteins is also a challenging task.

RESULTS

We propose a model called ALDPI to adaptively learn the multi-scale topologies and multi-modality similarities with various significance levels. We first construct a drug-protein heterogeneous graph, which is composed of the interactions and the similarities with multiple modalities among drugs and proteins. An adaptive graph learning module is then designed to learn important kinds of connections in heterogeneous graph and generate new topology graphs. A module based on graph convolutional autoencoders is established to learn multiple representations, which imply the node attributes and multiple-scale topologies composed of one-order and multi-order neighbors, respectively. We also design an attention mechanism at neighbor topology level to distinguish the importance of these representations. Finally, since each similarity modality has its specific features, we construct a multi-layer convolutional neural network-based module to learn and fuse multi-modality features to obtain the attribute representation of each drug-protein node pair. Comprehensive experimental results show ALDPI's superior performance over six state-of-the-art methods. The results of recall rates of top-ranked candidates and case studies on five drugs further demonstrate the ability of ALDPI to discover potential drug-related protein candidates.

CONTACT

zhang@hlju.edu.cn.

The Brazilian compound library (BraCoLi) database: a repository of chemical and biological information for drug design

The Brazilian Compound Library (BraCoLi) is a novel open access and manually curated electronic library of compounds developed by Brazilian research groups to support further computer-aided drug design works, available on https://www.farmacia.ufmg.br/qf/downloads/ . Herein, the first version of the database is described comprising 1176 compounds. Also, the chemical diversity and drug-like profiles of BraCoLi were defined to analyze its chemical space. A significant amount of the compounds fitted Lipinski and Veber's rules, alongside other drug-likeness properties. A comparison using principal component analysis showed that BraCoLi is similar to other databases (FDA-approved drugs and NuBBE_DB) regarding structural and physicochemical patterns. Furthermore, a scaffold analysis showed that BraCoLi presents several privileged chemical skeletons with great diversity. Despite the similar distribution in the structural and physicochemical spaces, Tanimoto coefficient values indicated that compounds present in the BraCoLi are generally different from the two other databases, where they showed different kernel distributions and low similarity. These facts show an interesting innovative aspect, which is a desirable feature for novel drug design purposes.

Therapeutic strategies for identifying small molecules against prion diseases

Prion diseases are fatal neurodegenerative disorders, for which there are no effective therapeutic and diagnostic agents. The main pathological hallmark has been identified as conformational changes of the cellular isoform prion protein (PrP^C) to a misfolded isoform of the prion protein (PrP^Sc). Targeting PrP^C and its conversion to PrP^Sc is still the central dogma in prion drug discovery, particularly in in silico and in vitro screening endeavors, leading to the identification of many small molecules with therapeutic potential. Nonetheless, multiple pathological targets are critically involved in the intricate pathogenesis of prion diseases. In this context, multi-target-directed ligands (MTDLs) emerge as valuable therapeutic approach for their potential to effectively counteract the complex etiopathogenesis by simultaneously modulating multiple targets. In addition, diagnosis occurs late in the disease process, and consequently a successful therapeutic intervention cannot be provided. In this respect, small molecule theranostics, which combine imaging and therapeutic properties, showed tremendous potential to cure and diagnose in vivo prion diseases. Herein, we review the major advances in prion drug discovery, from anti-prion small molecules identified by means of in silico and in vitro screening approaches to two rational strategies, namely MTDLs and theranostics, that have led to the identification of novel compounds with an expanded anti-prion profile.

Recent advances in the design of antimicrobial peptide conjugates

Antimicrobial peptides (AMPs) are ubiquitous host defense peptides characterized by antibiotic activity and lower propensity for developing resistance compared to classic antibiotics. While several AMPs have shown activity against antibiotic-sensitive...

Databases, DrugBank, and virtual screening platforms for therapeutic development

The upsurge of the severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has turned into a global health disaster. Many remodeled medications were suggested for treatment in the early stages of this pandemic, but these dosages afterward came across with distinct offshoots. Thus, these consequences compelled the scientists to develop new drugs using various antiviral, antiinflammatory, antibacterial, and phytochemical compounds. A handful of drugs have been scrutinized in silico, in vitro, plus through human trials such as anti-SARS-CoV-2 agents and made available as various databases by various scientific communities. The SARS-CoV-2 pandemic databases are designed to allay difficulties associated with this scenario. Some of the popular databases are GESS (global evaluation of SARS-CoV-2/HCoV-19 sequences) which gives a thorough study of data based on tenfold of thousands of complete coverage and quality of SARS-CoV-2 genomes, CORona Drug InTERactions (CORDITE) database for SARS-CoV-2 which profoundly combines the understanding of potential drugs and make it available for scientists and medicos. SARSCOVIDB set one’s sights to merge all differential gene expression data, at mRNA and protein levels, helping to accelerate analysis and research on the molecular impact of covid-19. This chapter aims to provide a piece of complete information about the SARS-CoV-2 virus databases, potentially available drugs, and virtual screening methods. And also provides a different webserver to reach out for information related to the COVID-19 pandemic and its future.

Selectivity mechanism of BCL-XL/2 inhibition through in silico investigation

BCL-XL protein is among the most important members of the anti-apoptotic subfamily of BCL-2 protein family, as currently a promising new target for anti-tumor drug research, even though BCL-XL/2 proteins...

Calendulaglycoside A showing potential activity against SARS-CoV-2 main protease: Molecular docking, molecular dynamics, and SAR studies

BACKGROUND AND AIM

The discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, natural products can provide therapeutic alternatives that could be employed as an effective safe treatment for COVID-19.

EXPERIMENTAL PROCEDURE

Twelve compounds were isolated from the aerial parts of C. officinalis L. and investigated for their inhibitory activities against SARS-CoV-2 Mpro compared to its co-crystallized N3 inhibitor using molecular docking studies. Furthermore, a 100 ns MD simulation was performed for the most active two promising compounds, Calendulaglycoside A (SAP5) and Osteosaponin-I (SAP8).

RESULTS AND CONCLUSION

At first, molecular docking studies showed interesting binding scores as compared to the N3 inhibitor. Calendulaglycoside A (SAP5) achieved a superior binding than the co-crystallized inhibitor indicating promising affinity and intrinsic activity towards the Mpro of SARS-CoV-2 as well. Moreover, findings illustrated preferential stability for SAP5 within the Mpro pocket over that of N3 beyond the 40 ns MD simulation course. Structural preferentiality for triterpene-Mpro binding highlights the significant role of 17β-glucosyl and carboxylic 3α-galactosyl I moieties through high electrostatic interactions across the MD simulation trajectories. Furthermore, this study clarified a promising SAR responsible for the antiviral activity against the SARS-CoV-2 Mpro and the design of new drug candidates targeting it as well. The above findings could be promising for fast examining the previously isolated triterpenes both pre-clinically and clinically for the treatment of COVID-19.

FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction

The prediction of drug-target affinity (DTA) plays an increasingly important role in drug discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and proteins, but ignore the importance of feature aggregation. However, the increasingly complex encoder networks lead to the loss of implicit information and excessive model size. To this end, we propose a deep-learning-based approach namely FusionDTA. For the loss of implicit information, a novel muti-head linear attention mechanism was utilized to replace the rough pooling method. This allows FusionDTA aggregates global information based on attention weights, instead of selecting the largest one as max-pooling does. To solve the redundancy issue of parameters, we applied knowledge distillation in FusionDTA by transfering learnable information from teacher model to student. Results show that FusionDTA performs better than existing models for the test domain on all evaluation metrics. We obtained concordance index (CI) index of 0.913 and 0.906 in Davis and KIBA dataset respectively, compared with 0.893 and 0.891 of previous state-of-art model. Under the cold-start constrain, our model proved to be more robust and more effective with unseen inputs than baseline methods. In addition, the knowledge distillation did save half of the parameters of the model, with only 0.006 reduction in CI index. Even FusionDTA with half the parameters could easily exceed the baseline on all metrics. In general, our model has superior performance and improves the effect of drug-target interaction (DTI) prediction. The visualization of DTI can effectively help predict the binding region of proteins during structure-based drug design.

Subtractive genomics and molecular docking approach to identify drug targets against Stenotrophomonas maltophilia

Stenotrophomonas maltophilia is a multidrug resistant pathogen associated with high mortality and morbidity in patients having compromised immunity. The efflux systems of S. maltophilia include SmeABC and SmeDEF proteins, which assist in acquisition of multiple-drug-resistance. In this study, proteome based mapping was utilized to find out the potential drug targets for S. maltophilia strain k279a. Various tools of computational biology were applied to remove the human-specific homologous and pathogen-specific paralogous sequences from the bacterial proteome. The CD-HIT analysis selected 4315 proteins from total proteome count of 4365 proteins. Geptop identified 407 essential proteins, while the BlastP revealed approximately 85 non-homologous proteins in the human genome. Moreover, metabolic pathway and subcellular location analysis were performed for essential bacterial genes, to describe their role in various cellular processes. Only two essential proteins (Acyl-[acyl-carrier-protein]—UDP-N acetyl glucosamine O-acyltransferase and D-alanine-D-alanine ligase) as candidate for potent targets were found in proteome of the pathogen, in order to design new drugs. An online tool, Swiss model was employed to model the 3D structures of both target proteins. A library of 5000 phytochemicals was docked against those proteins through the molecular operating environment (MOE). That resulted in to eight inhibitors for both proteins i.e. enterodiol, aloin, ononin and rhinacanthinF for the Acyl-[acyl-carrier-protein]—UDP-N acetyl glucosamine O-acyltransferase, and rhazin, alkannin beta, aloesin and ancistrocladine for the D-alanine-D-alanine ligase. Finally the ADMET was done through ADMETsar. This study supported the development of natural as well as cost-effective drugs against S. maltophilia. These inhibitors displayed the effective binding interactions and safe drug profiles. However, further in vivo and in vitro validation experiment might be performed to check their drug effectiveness, biocompatibility and their role as effective inhibitors.

Decrypting a Cryptic Allosteric Pocket in H. pylori Glutamate Racemase

One of our greatest challenges in drug design is targeting cryptic allosteric pockets in enzyme targets. Drug leads that do bind to these cryptic pockets are often discovered during HTS campaigns, and the mechanisms of action are rarely understood. Nevertheless, it is often the case that the allosteric pocket provides the best option for drug development against a given target. In the current studies we present a successful way forward in rationally exploiting the cryptic allosteric pocket of H. pylori glutamate racemase, an essential enzyme in this pathogen's life cycle. A wide range of computational and experimental methods are employed in a workflow leading to the discovery of a series of natural product allosteric inhibitors which occupy the allosteric pocket of this essential racemase. The confluence of these studies reveals a fascinating source of the allosteric inhibition, which centers on the abolition of essential monomer-monomer coupled motion networks.

Discovery of Novel HCV NS5B polymerase inhibitor, 2-(3,4-dimethyl-5,5-dioxidobenzo[e]pyrazolo[4,3-c][1,2]thiazin-2(4H)-yl)-N-(2-fluorobenzyl)acetamide via molecular docking and experimental approach

Hepatitis C Virus (HCV) is a viral infection posing a severe global threat that left untreated progresses to end-stage liver disease, including cirrhosis and hepatocellular carcinoma (HCC). Moreover, no prophylactic approach exists so far enabling its prevention. The NS5B polymerase holds special significance as the target of intervention against HCV infection. The current study kindles benzothiazine derivatives against HCV NS5B polymerase through in silico and experimental approaches. Following docking, the compound 2-(3,4-dimethyl-5,5-dioxidobenzo[e]pyrazolo[4,3-c][1,2]thiazin-2(4H)-yl)-N-(2-fluorobenzyl)acetamide was revealed to form effective binding interaction in the proposed site of HCV NS5B with a score of -10 kcal/mol and subsequently was deciphered through molecular dynamics (MD) simulation study which indicated interaction of residues TYR_382, VAL_381 and HIS_467 through hydrophobic interaction and two residues such as GLU_202 and LYS_209 contributed in the formation of water bridges. The subsequent in silico pharmacological analysis revealed its safe drug profile. The cytotoxicity activity of compound 6c indicated to be non-toxic in HepG2 cells at concentration ranges from 0.001-1.0 µmol/L with >80% cell viability and diminished expression of the HCV NS5B to 98% at the dose of 1.0 µmol/L and 90% at 0.5µmol/L. Thus the hit compound 6c might be a potent NS5B polymerase inhibitor required to be validated further through in vivo and preclinical studies.

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing

We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Characteristics and Research Techniques Associated with the Journal Impact Factor and Other Key Metrics in Pharmacology Journals

In the present age, there is intense pressure on researchers to publish their research in ‘high-impact factor’ journals. It would be interesting to understand the trend of research publications in the field of pharmacology by exploring the characteristics of research articles, including research techniques, in relation to the journal’s key bibliometrics, particularly journal impact factor (JIF), the seemingly most mentioned metric. This study aimed to determine the characteristics and research techniques in relation to research articles in pharmacology journals with higher or lower JIF values. A cross-sectional study was conducted on primary research journals under the ‘Pharmacology and Pharmacy’ category. Analysis of 768 original research articles across 32 journals (with an average JIF of 2.565 ± 0.887) demonstrated that research studies involving molecular techniques, in vivo experiments on animals, and bioinformatics and computational modeling were significantly associated with a higher JIF value of the journal in which such contributions were published. Our analysis suggests that research studies involving such techniques/approaches are more likely to be published in higher-ranked pharmacology journals.

The steroidal lactone withaferin A impedes T-cell motility by inhibiting the kinase ZAP70 and subsequent kinome signaling

The steroidal lactone withaferin A (WFA) is a dietary phytochemical, derived from Withania somnifera. It exhibits a wide range of biological properties, including immunomodulatory, anti-inflammatory, antistress, and anticancer activities. Here we investigated the effect of WFA on T-cell motility, which is crucial for adaptive immune responses as well as autoimmune reactions. We found that WFA dose-dependently (within the concentration range of 0.3–1.25 μM) inhibited the ability of human T-cells to migrate via cross-linking of the lymphocyte function-associated antigen-1 (LFA-1) integrin with its ligand, intercellular adhesion molecule 1 (ICAM-1). Coimmunoprecipitation of WFA interacting proteins and subsequent tandem mass spectrometry identified a WFA-interactome consisting of 273 proteins in motile T-cells. In particular, our data revealed significant enrichment of the zeta-chain-associated protein kinase 70 (ZAP70) and cytoskeletal actin protein interaction networks upon stimulation. Phospho-peptide mapping and kinome analysis substantiated kinase signaling downstream of ZAP70 as a key WFA target, which was further confirmed by bait-pulldown and Western immunoblotting assays. The WFA-ZAP70 interaction was disrupted by a disulfide reducing agent dithiothreitol, suggesting an involvement of cysteine covalent binding interface. In silico docking predicted WFA binding to ZAP70 at cystine 560 and 564 residues. These findings provide a mechanistic insight whereby WFA binds to and inhibits the ZAP70 kinase and impedes T-cell motility. We therefore conclude that WFA may be exploited to pharmacologically control host immune responses and potentially prevent autoimmune-mediated pathologies.

Computational screening of phytochemicals from three medicinal plants as inhibitors of transmembrane protease serine 2 implicated in SARS-CoV-2 infection

Background

SARS-CoV-2 infection or COVID-19 is a major global public health issue that requires urgent attention in terms of drug development. Transmembrane Protease Serine 2 (TMPRSS2) is a good drug target against SARS-CoV-2 because of the role it plays during the viral entry into the cell. Virtual screening of phytochemicals as potential inhibitors of TMPRSS2 can lead to the discovery of drug candidates for the treatment of COVID-19.

Purpose

The study was designed to screen 132 phytochemicals from three medicinal plants traditionally used as antivirals; Zingiber officinalis Roscoe (Zingiberaceae), Artemisia annua L. (Asteraceae), and Moringa oleifera Lam. (Moringaceae), as potential inhibitors of TMPRSS2 for the purpose of finding therapeutic options to treat COVID-19.

Methods

Homology model of TMPRSS2 was built using the ProMod3 3.1.1 program of the SWISS-MODEL. Binding affinities and interaction between compounds and TMPRSS2 model was examined using molecular docking and molecular dynamics simulation. The drug-likeness and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of potential inhibitors of TMPRSS2 were also assessed using admetSAR web tool.

Results

Three compounds, namely, niazirin, quercetin, and moringyne from M. oleifera demonstrated better molecular interactions with binding affinities ranging from -7.1 to -8.0 kcal/mol compared to -7.0 kcal/mol obtained for camostat mesylate (a known TMPRSS2 inhibitor), which served as a control. All the three compounds exhibited good drug-like properties by not violating the Lipinski rule of 5. Niazirin and moringyne possessed good ADMET properties and were stable in their interactions with the TMPRSS2 based on the molecular dynamics simulation. However, the ADMET tool predicted the potential hepatotoxic and mutagenic effects of quercetin.

Conclusion

This study demonstrated the potentials of niazirin, quercetin, and moringyne from M. oleifera, to inhibit the activities of human TMPRSS2, thus probably being good candidates for further development as new drugs for the treatment or management of COVID-19.

Protein domain-based prediction of drug/compound-target interactions and experimental validation on LIM kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins' structure/function, and bias in system training datasets. Here, we propose a new method "DRUIDom" (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound-target pairs (~2.9M data points), and used as training data for calculating parameters of compound-domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

A Comparative Study of the Lipophilicity of Metformin and Phenformin

The results presented in this paper confirm the beneficial role of an easy-to-use and low-cost thin-layer chromatography (TLC) technique for describing the retention behavior and the experimental lipophilicity parameter of two biguanide derivatives, metformin and phenformin, in both normal-phase (NP) and reversed-phase (RP) TLC systems. The retention parameters (R_F, R_M) obtained under different chromatographic conditions, i.e., various stationary and mobile phases in the NP-TLC and RP-TLC systems, were used to determine the lipophilicity parameter (R_MW) of metformin and phenformin. This study confirms the poor lipophilicity of both metformin and phenformin. It can be stated that the optimization of chromatographic conditions, i.e., the kind of stationary phase and the composition of mobile phase, was needed to obtain the reliable value of the chromatographic lipophilicity parameter (R_MW) in this study. The fewer differences in the R_MW values of both biguanide derivatives were ensured by the RP-TLC system composed of RP2, RP18, and RP18W plates and the mixture composed of methanol, propan-1-ol, and acetonitrile as an organic modifier compared to the NP-TLC analysis. The new calculation procedures for logP of drugs based on topological indices ⁰χ^ν, ⁰χ, ¹χ^ν, M, and M^ν may be a certain alternative to other algorithms as well as the TLC procedure performed under optimized chromatographic conditions. The knowledge of different lipophilicity parameters of the studied biguanides can be useful in the future design of novel and more therapeutically effective metformin and phenformin formulations for antidiabetic and possible anticancer treatment. Moreover, the topological indices presented in this work may be further used in the QSAR study of the examined biguanides.