Virtual ligand screening: strategies, perspectives and limitations

Integration of virtual screening and proteomics reveals potential targets and pathways for ginsenoside Rg1 against myocardial ischemia

Background

Ginsenoside Rg1 (Rg1) is one of the main active components in Chinese medicines, Panax ginseng and Panax notoginseng. Research has shown that Rg1 has a protective effect on the cardiovascular system, including anti-myocardial ischemia-reperfusion injury, anti-apoptosis, and promotion of myocardial angiogenesis, suggesting it a potential cardiovascular agent. However, the protective mechanism involved is still not fully understood.

Methods

Based on network pharmacology, ligand-based protein docking, proteomics, Western blot, protein recombination and spectroscopic analysis (UV-Vis and fluorescence spectra) techniques, potential targets and pathways for Rg1 against myocardial ischemia (MI) were screened and explored.

Results

An important target set containing 19 proteins was constructed. Two target proteins with more favorable binding activity for Rg1 against MI were further identified by molecular docking, including mitogen-activated protein kinase 1 (MAPK1) and adenosine kinase (ADK). Meanwhile, Rg1 intervention on H9c2 cells injured by H2O2 showed an inhibitory oxidative phosphorylation (OXPHOS) pathway. The inhibition of Rg1 on MAPK1 and OXPHOS pathway was confirmed by Western blot assay. By protein recombination and spectroscopic analysis, the binding reaction between ADK and Rg1 was also evaluated.

Conclusion

Rg1 can effectively alleviate cardiomyocytes oxidative stress injury via targeting MAPK1 and ADK, and inhibiting oxidative phosphorylation (OXPHOS) pathway. The present study provides scientific basis for the clinical application of the natural active ingredient, Rg1, and also gives rise to a methodological reference to the searching of action targets and pathways of other natural active ingredients.

Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions

Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

Molecular modelling approaches can reveal the Molecular interactions established between antimalarial targets of hemozoin pathway and the organic phytochemicals of Artocarpus species

Ayurveda, the traditional Indian medical system, has potential applications in early malaria treatment. In an in silico docking study, 50 phytochemicals from two plants Artocarpus lakoocha Roxb. (AL) And Artocarpus heterophyllus Lam. (AH), were examined for their interactions with anti-malarial proteins (PDB IDs: 3BWK, 3BPF, 1LF3). The nucleotide analogue Artemisinin, a current malaria treatment, served as a positive control. Result showed that phytochemicals from AL and AH exhibited binding affinities as high as -9.6 kcal/mol, respectively. Additionally, molecular dynamics simulation for Artocarpin: 3BPF demonstrated stable complexes over 100 ns. Notably, Artocarpin and Quercetin displayed higher binding affinities (up to -9.6 as well as -9.5 kcal/mol, respectively) compared to Artemisinin (-7.5 up to kcal/mol), have shown. Pharmacokinetic predictions indicated the compounds were likely non-carcinogenic, water-soluble and biologically safe. In-vitro analysis using β-Hematin assay supported these findings, suggesting the phytochemicals as Hemozoin pathway inhibitors.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

Discovery and development of small-molecule heparanase inhibitors

Heparanase-1 (HPSE) is a promising yet challenging therapeutic target. It is the only known enzyme that is responsible for cleavage of heparan sulfate (HS) side chains from heparan sulfate proteoglycans (HSPGs), and is the key enzyme involved in the remodeling and degradation of the extracellular matrix (ECM). Overexpression of HPSE is found in various types of diseases, including cancers, inflammations, diabetes, and viral infections. Inhibiting HPSE can restore ECM functions and integrity, making the development of HPSE inhibitors a highly sought-after topic. So far, all HPSE inhibitors that have entered clinical trials belong to the category of HS mimetics, and no small-molecule or drug-like HPSE inhibitors have made similar progress. None of the HS mimetics have been approved as drugs, with some clinical trials discontinued due to poor bioavailability, side effects, and unfavorable pharmacokinetics characteristics. Small-molecule HPSE inhibitors are, therefore, particularly appealing due to their drug-like characteristics. Advances in the chemical spaces and drug design technologies, including the increasing use of in vitro and in silico screening methods, have provided new opportunities in drug discovery. This article aims to review the discovery and development of small-molecule HPSE inhibitors via screening strategies to shed light on the future endeavors in the development of novel HPSE inhibitors.

Research advances in identification procedures of endocrine disrupting chemicals

Endocrine disrupting chemicals (EDCs) are increasingly concerned substance endangering human health and environment. However, there is no unified standard for identifying chemicals as EDCs, which is also controversial internationally. In this review, the procedures for EDC identification in different organizations/countries were described. Importantly, three aspects to be considered in identifying chemical substances as EDCs were summarized, which were mechanistic data, animal experiments, and epidemiological information. The relationships between them were also discussed. To elaborate more clearly on these three aspects of evidence, scientific data on some chemicals including bisphenol A, 1,2-dibromo-4-(1,2 dibromoethyl) cyclohexane and perchlorate were collected and evaluated. Altogether, the above three chemicals were assessed for interfering with hormones and elaborated their health hazards from macroscopic to microscopic. This review is helpful for standardizing the identification procedure of EDCs.

In silico evaluation of binding interaction and ADME study of new 1,3-diazetidin-2-one derivatives with high antiproliferative activity

A series of eight novels' 1,3-diazetidin-2-ones have been proposed to assess their potential activities. They are intended to examine antiproliferative effects through inhibition of epidermal growth factor receptor (EGFR) expression. These eight compounds strongly interact with the EGFR protein, responsible for the activity. As part of a present study, these compounds were docked to the crystal structure of the EGFR (Protein Data Bank code: 1 M17) to determine their binding affinity at the active site. Based on computer predictions, two compounds were demonstrated high scores of 80.80 and 85.89. After analyzing ADME properties, these compounds were found to have significant potential for binding. Consequently, the abilities of gefitinib, erlotinib, imatinib, and sorafenib were selected for comparison as controls. Computational methods were performed to predict the critical disposition of eight novels' 1,3-diazetidin-2-one derivatives to the EGFR. Moreover, a docking technique employing the Genetic Optimization for Ligand Docking program was conducted. Compounds 2 and 7 demonstrate a high docking peace-wise scoring function (PLP) fitness of 85.89 and 80.80, respectively. They fulfilled the Lipinski's rule, topological descriptors, and fingerprints of drug-like molecular structure keys. These compounds can be used as lead compounds to develop novel antiproliferative agents. The outcome of applying this study is novel series of 1,3-diazetidin-2-one compounds as new analogs were designed and evaluated for their antiproliferative activity with a higher potency profile and binding affinity within the active sites of EGFR.

PLBD: protein-ligand binding database of thermodynamic and kinetic intrinsic parameters

We introduce a protein-ligand binding database (PLBD) that presents thermodynamic and kinetic data of reversible protein interactions with small molecule compounds. The manually curated binding data are linked to protein-ligand crystal structures, enabling structure-thermodynamics correlations to be determined. The database contains over 5500 binding datasets of 556 sulfonamide compound interactions with the 12 catalytically active human carbonic anhydrase isozymes defined by fluorescent thermal shift assay, isothermal titration calorimetry, inhibition of enzymatic activity and surface plasmon resonance. In the PLBD, the intrinsic thermodynamic parameters of interactions are provided, which account for the binding-linked protonation reactions. In addition to the protein-ligand binding affinities, the database provides calorimetrically measured binding enthalpies, providing additional mechanistic understanding. The PLBD can be applied to investigations of protein-ligand recognition and could be integrated into small molecule drug design. Database URL https://plbd.org/.

Identification of Potential Phytochemical/Antimicrobial Agents against Pseudoperonospora cubensis Causing Downy Mildew in Cucumber through In-Silico Docking

Compatibility interactions between the host and the fungal proteins are necessary to successfully establish a disease in plants by fungi or other diseases. Photochemical and antimicrobial substances are generally known to increase plant resilience, which is essential for eradicating fungus infections. Through homology modeling and in silico docking analysis, we assessed 50 phytochemicals from cucumber (Cucumis sativus), 15 antimicrobial compounds from botanical sources, and six compounds from chemical sources against two proteins of Pseudoperonospora cubensis linked to cucumber downy mildew. Alpha and beta sheets made up the 3D structures of the two protein models. According to Ramachandran plot analysis, the QNE 4 effector protein model was considered high quality because it had 86.8% of its residues in the preferred region. The results of the molecular docking analysis showed that the QNE4 and cytochrome oxidase subunit 1 proteins of P. cubensis showed good binding affinities with glucosyl flavones, terpenoids and flavonoids from phytochemicals, antimicrobial compounds from botanicals (garlic and clove), and chemically synthesized compounds, indicating the potential for antifungal activity.

Discovery of potential SARS-CoV 3CL protease inhibitors from approved antiviral drugs using: virtual screening, molecular docking, pharmacophore mapping evaluation and dynamics simulation

The spread of corona-virus disease 2019 (COVID-19) has been faster than any other corona-viruses that have succeeded in crossing the animal-human barrier. This disease, caused by the severe acute respiratory syndrome corona-virus 2 (SARS-CoV-2/2019-nCoV) posing a serious threat to global public health and local economies. There are three responsible for this disease; SARS-CoV-2, SARS-CoV and MERS-CoV. Whereas our goal is to test the affinity for a new class of compounds obtained from a hybridization of Chloroquine, Amodiaquine and Mefloquine with three targets SARS-CoV-2, SARS-CoV and MERS-CoV, in order to find new compounds as new inhibitors against Covid-19. In this work, we first used: the molecular docking/dynamics methods and ADME properties to study interaction and affinity between eight new compounds against three targets involved in the Covid-19. The results of the docking simulations and dynamics revealed that inhibitor of the malaria (Ligand 87) has an affinity to interact with SARS-CoV-2, SARS-CoV and MERS-CoV targets and they can be good inhibitors for treatment of Covid-19. Moreover, they give best affinity compared to the Remdesivir and Chloroquine and other clinical tests. The Pharmacokinetics was justified by means of lipophilicity and high coefficient of skin permeability. The in silico evaluation of ADME and drug-likeness revealed that L87 has higher absorption in the intestines with good bioavailability. However, an additional in vitro and/or in vivo experimental study should make it possible to verify the theoretical results obtained in silico.Communicated by Ramaswamy H. Sarma.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Simulation of ligand dissociation kinetics from the protein kinase PYK2

Early-stage drug discovery projects often focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. The kinetics of drug binding are ignored but can have significant influence on drug efficacy. Therefore, increasing attention has been paid on evaluating drug-binding kinetics early in a drug discovery process. Simulating drug-binding kinetics at the atomic level is challenging for the long time scale involved. Here, we used the transition-based reweighting analysis method (TRAM) with the Markov state model to study the dissociation of a ligand from the protein kinase PYK2. TRAM combines biased and unbiased simulations to reduce computational costs. This work used the umbrella sampling technique for the biased simulations. Although using the potential of mean force from umbrella sampling simulations with the transition-state theory over-estimated the dissociation rate by three orders of magnitude, TRAM gave a dissociation rate within an order of magnitude of the experimental value.

High throughput virtual screening (HTVS) of peptide library: Technological advancement in ligand discovery

High-throughput virtual screening (HTVS) is a leading biopharmaceutical technology that employs computational algorithms to uncover biologically active compounds from large-scale collections of chemical compound libraries. In addition, this method often leverages the precedence of screening focused libraries for assessing their binding affinities and improving physicochemical properties. Usually, developing a drug sometimes takes ages, and lessons are learnt from FDA-approved drugs. This screening strategy saves resources and time compared to laboratory testing in certain stages of drug discovery. Yet in-silico investigations remain challenging in some cases of drug discovery. For the last few decades, peptide-based drug discoveries have received remarkable momentum for several advantages over small molecules. Therefore, developing a high-fidelity HTVS platform for chemically versatile peptide libraries is highly desired. This review summarises the modern and frequently appreciated HTVS strategies for peptide libraries from 2011 to 2021. In addition, we focus on the software used for preparing peptide libraries, their screening techniques and shortcomings. An index of various HTVS methods reported here should assist researchers in identifying tools that could be beneficial for their peptide library screening projects.

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

Commonly used surfactants sodium dodecyl sulphate, cetylpyridinium chloride and sodium laureth sulphate and their effects on antioxidant defence system and oxidative stress indices in Cyprinus carpio L.: an integrated in silico and in vivo approach

The present study evaluated the homology modelling, in silico prediction and characterization of Cyprinus carpio cytochrome P450, as well as molecular docking experiments between the modelled protein and the surfactants sodium dodecyl sulphate (SDS), sodium laureth sulphate (SLES) and cetylpyridinium chloride (CPC). Homology modelling of cytochrome P450 was performed using the best fit template structure. The structure was optimized with 3D refine, and the ultimate 3D structure was checked with PROCHEK and ERRATA. ExPASy's ProtParam was likewise used to analyse the modelled protein's physiochemical and stereochemical attributes. To establish the binding pattern of each ligand to the targeted protein and its effect on the overall protein conformation, molecular docking calculations and protein-ligand interactions were performed. Our in silico analysis revealed that hydrophobic interactions with the active site amino acid residues of cytochrome p450 were more prevalent than hydrogen bonds and salt bridges. The in vivo analysis exhibited that exposure of fish to sublethal concentrations (10% and 30% of 96 h LC₅₀) of SDS (0.34 and 1.02 mg/l), CPC (0.002 and 0.006 mg/l) and SLES (0.69 and 2.07 mg/l) at 15d, 30d and 45d adversely affected the oxidative stress and antioxidant enzymes (CAT, SOD, GST, GPx and MDA) in the liver of Cyprinus carpio. As a result, the study suggests that elicited oxidative stress, prompted by the induction of antioxidant enzymes activity, could be attributable to the stable binding of cytochrome P450 with SDS, CPC and SLES which ultimately leads to the evolution of antioxidant enzymes for its neutralization.

Benchmarking ensemble docking methods in D3R Grand Challenge 4

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

Sequence-based prediction of protein binding regions and drug-target interactions

Identifying drug-target interactions (DTIs) is important for drug discovery. However, searching all drug-target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model's performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein-ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence-ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub ( https://github.com/GIST-CSBL/HoTS ).

wSDTNBI: a novel network-based inference method for virtual screening

In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS), namely, network-based VS. Herein, we reported a novel network-based inference method named wSDTNBI. Compared with previous network-based methods that use unweighted DTI networks, wSDTNBI uses weighted DTI networks whose edge weights are correlated with binding affinities. A two-pronged approach based on weighted DTI and drug-substructure association networks was employed to calculate prediction scores. To show the practical value of wSDTNBI, we performed network-based VS on retinoid-related orphan receptor γt (RORγt), and purchased 72 compounds for experimental validation. Seven of the purchased compounds were confirmed to be novel RORγt inverse agonists by in vitro experiments, including ursonic acid and oleanonic acid with IC50 values of 10 nM and 0.28 μM, respectively. Moreover, the direct contact between ursonic acid and RORγt was confirmed using the X-ray crystal structure, and in vivo experiments demonstrated that ursonic acid and oleanonic acid have therapeutic effects on multiple sclerosis. These results indicate that wSDTNBI might be a powerful tool for network-based VS in drug discovery.

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods

Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.

Virtual screening and in vitro assays of novel hits as promising DPP-4 inhibitors

Diabetes is a metabolic disorder that presents hyperglycemia and vascular complications due to the non-production of insulin or its inappropriate use by the body. One of the strategies to treat diabetes is the inhibition of dipeptidyl peptidase-4 (DPP-4) and it is interesting to conduct virtual screening studies to search for new inhibitors of the DPP-4 enzyme. This study involves a virtual screening using the crystallographic structure of DPP-4 and a compound subset from the ZINC database. To filter this compound subset, we used some physicochemical properties, positioning at the three DPP-4 binding sites, molecular interactions, and ADME-Tox properties. The conformations of ligands obtained from AutoDock Vina were analyzed using a consensus with other algorithms (AutoDock and GOLD). The compounds selected from virtual screening were submitted to biological assays using the "DPPIV-Glo™ protease assay". Cytotoxicity tests were also performed. One promising compound (ZINC1572309) established interactions with important residues at the binding site. The results of the ADME-Tox prediction for ZINC1572309 were compared with a reference drug (sitagliptin). The cytotoxicity of sitagliptin and ZINC1572309 were evaluated using the XTT short-term cytotoxic assay, including normal and tumor cell lines to observe the cellular response to inhibitor treatment at different genetic bases. Both compounds (ZINC1572309 and the reference drug - sitagliptin) also inhibited DPP-4 activity, suggesting interesting biological effects of the selected compound at non-cytotoxic concentrations. Therefore, from in silico and in vitro studies, a potential hit as DPP-4 inhibitor was discovered and it can be structurally optimized to achieve suitable activity and pharmacokinetic profiles.

Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα

In this study, we utilized human DNA topoisomerase IIα as a model target to outline a dynophore-based approach to catalytic inhibitor design. Based on MD simulations of a known catalytic inhibitor and the native ATP ligand analog, AMP-PNP, we derived a joint dynophore model that supplements the static structure-based-pharmacophore information with a dynamic component. Subsequently, derived pharmacophore models were employed in a virtual screening campaign of a library of natural compounds. Experimental evaluation identified flavonoid compounds with promising topoisomerase IIα catalytic inhibition and binding studies confirmed interaction with the ATPase domain. We constructed a binding model through docking and extensively investigated it with molecular dynamics MD simulations, essential dynamics, and MM-GBSA free energy calculations, thus reconnecting the new results to the initial dynophore-based screening model. We not only demonstrate a new design strategy that incorporates a dynamic component of molecular recognition, but also highlight new derivates in the established flavonoid class of topoisomerase II inhibitors.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

TRPM2 in ischemic stroke: Structure, molecular mechanisms, and drug intervention

Ischemic stroke has a high lethality rate worldwide, and novel treatments are limited. Calcium overload is considered to be one of the mechanisms of cerebral ischemia. Transient receptor potential melastatin 2 (TRPM2) is a reactive oxygen species (ROS)-sensitive calcium channel. Cerebral ischemia-induced TRPM2 activation triggers abnormal intracellular Ca2+ accumulation and cell death, which in turn causes irreversible brain damage. Thus, TRPM2 has emerged as a new therapeutic target for ischemic stroke. This review provides data on the expression, structure, and function of TRPM2 and illustrates its cellular and molecular mechanisms in ischemic stroke. Natural and synthetic TRPM2 inhibitors (both specific and nonspecific) are also summarized. The three-dimensional protein structure of TRPM2 has been identified, and we speculate that molecular simulation techniques will be essential for developing new drugs that block TRPM2 channels. These insights about TRPM2 may be the key to find potent therapeutic approaches for the treatment of ischemic stroke.

Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2

The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensin-converting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and [SARS-CoV-2/ACE2] complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor and [SARS-CoV-2/ACE2] complex better than chloroquine and hydroxychloroquine. This new understanding should help to improve predictions of the impact of such alternatives on COVID-19.Communicated by Ramaswamy H. Sarma.

A review on network pharmacology based phytotherapy in treating diabetes- An environmental perspective

Diabetes has become common lifestyle disorder associated with obesity and cardiovascular diseases. Environmental factors like physical inactivity, polluted surroundings and unhealthy dieting also plays a vital role in diabetes pathogenesis. As the current anti-diabetic drugs possess unprecedented side effects, traditional herbal medicine can be used an alternative therapy. The paramount challenge with the herbal formulation usage is the lack of standardized procedure, entangled with little knowledge on drug safety and mechanism of drug action. Heavy metal contamination is a major environmental hazard where plants tend to accumulate toxic metals like nickel, chromium and lead through industrial and agricultural activities. It becomes inappropriate to use these plants for phytotherapy as it may affect the human health on long term consumption. This review discuss about the environmental risk factors related to diabetes and better implication of medicinal plants in anti-diabetic therapy using network pharmacology. It is an in silico analytical tool that helps to unravel the multi-targeted action of herbal formulations rich in secondary metabolites. Also, a special focus is attempted to pool the databases regarding the medicinal plants for diabetes and associated diseases, their bioactive compounds, possible diabetic targets, drug-target interaction and toxicology reports that may open an aisle in safer, effective and toxicity-free drug discovery.

Application of Reverse Docking in the Research of Small Molecule Drugs and Traditional Chinese Medicine

With the development of structural biology and data mining, computer-aided drug design (CADD) has been playing an important role in all aspects of new drug development. Reverse docking, a method of virtual screening based on molecular docking in CADD, is widely used in drug repositioning, drug rescue, and traditional Chinese medicine (TCM) research, for it can search for macromolecular targets that can bind to a given ligand molecule. This review revealed the principle of reverse docking, summarized common target protein databases and docking procedures, and enumerated the applications of reverse docking in drug repositioning, adverse drug reactions, traditional Chinese medicine, and COVID-19 treatment. Hope our work can give some inspiration to researchers engaged in drug development.

Diversity-Oriented Synthesis of Spiropyrrolo[1,2-a]isoquinoline Derivatives via Diastereoselective and Regiodivergent Three-Component 1,3-Dipolar Cycloaddition Reactions: In Vitro and in Vivo Evaluation of the Antidiabetic Activity of Rhodanine Analogues

An efficient diastereoselective route is developed to get access to novel spiropyrrolo[1,2-a]isoquinoline-oxindole skeletons by a one-pot three-component [3 + 2] cycloaddition reaction of (Z)-5-arylidene-1,3-thiazolidine-2,4-diones, isatin derivatives, and 1,2,3,4-tetrahydroisoquinoline (THIQ). Interestingly, the regioselectivity of the reaction is both temperature- and solvent-dependent, allowing the synthesis of two regioisomeric endo-dispiropyrrolo[2,1-a]isoquinolineoxindoles in excellent yield. Unprecedentedly, each isomeric dispiropyrrolo[2,1-a]isoquinolineoxindole endured retro-1,3-dipolar cycloaddition/recycloaddition reactions under thermal or catalytic conditions to regenerate the corresponding regioisomeric counterpart. In addition, DFT calculations were performed at the M062X/6-31++g(d,p) level of theory to unravel the origin of the reversal of regioselectivity and endo-stereoselectivity of the title 1,3-dipolar cycloaddition reactions. Upon treatment of Isatin, THIQ with (Z)-4-arylidene-5-thioxo-thiazolidin-2-ones as dipolarophiles, unusual rhodanine analogues were formed, along with smaller amounts of a dispirooxindole-piperazine. The structure and the relative configuration of these N-heterocycles were unambiguously assigned by spectroscopic techniques and confirmed by four single-crystal structures. In vitro and in vivo studies reveal that the novel rhodanine derivatives exert antidiabetic activity. The binding affinity with the active site of the enzyme α-amylase was studied by molecular docking. Furthermore, the bioavailability assessed through virtual ADME parameters (Absorption, Distribution, Metabolism, Elimination pharmacokinetics) and the excellent fit with the Lipinski and Veber rules predict good drug-likeness properties for a bromo-substituted 2-sulfanylidene-1,3-thiazolidin-4-one.

Acid-catalyzed three-component addition of carbonyl compounds with 1,2,3-triazoles and indoles

A facile and efficient acid-catalyzed three-component reaction of indoles, 1-tosyl-1,2,3-triazoles and carbonyl compounds has been developed. The use of TsOH with a small amount of water significantly promoted the reaction yield. This method provided a general and one-pot approach for the synthesis of structurally diverse C3-alkylated indole derivatives. The alkylation exclusively occurred at the N² position of triazoles. Various functional groups were tolerated under the optimized simple reaction conditions.

An integrated in silico and in vivo approach to determine the effects of three commonly used surfactants sodium dodecyl sulphate, cetylpyridinium chloride and sodium laureth sulphate on growth rate and hematology in Cyprinus carpio L

The purpose of this work is to evaluate the homology modeling, in silico prediction, and characterization of somatotropin and erythropoietin from Cyprinus carpio as well as molecular docking and simulation experiments between the modeled proteins and surfactants sodium dodecyl sulfate (SDS), sodium laureth sulfate (SLES) and cetylpyridinium chloride (CPC). Using the best fit template structure, homology modeling of somatotropin and erythropoietin of Cyprinus carpio respectively was conducted. The model structures were improved further with 3Drefine, and the final 3D structures were verified with PROCHEK, ERRATA and ProQ. The physiochemical, as well as the stereochemical parameters of the modeled proteins, were evaluated using ExPASy's ProtParam. Molecular docking calculations, protein-ligand interactions, and protein flexibility analysis were carried out to determine the binding pattern of each ligand to the targeted proteins and their effect on the overall proteins' conformation. Our in silico analysis showed that hydrophobic interactions with the active site amino acid residues of the modeled proteins (somatotropin and erythropoietin) were more prevalent than hydrogen bonds and salt bridges that affect the flexibility and stability of the somatotropin and erythropoietin as revealed from our protein flexibility analysis. The in vivo analysis showed that sublethal concentrations of SDS, SLES, and CPC negatively affected the growth and hematological parameters of Cyprinus carpio. Hence, it may be inferred from the study that the alterations in the flexibility of somatotropin and erythropoietin of Cyprinus carpio upon addition of SDS, CPC and SLES might be attributable to the reduction in growth and hematological parameters.

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening

The hepatic bile acid transporter Na⁺/taurocholate co-transporting polypeptide (NTCP) was identified in 2012 as the high-affinity hepatic receptor for the hepatitis B and D viruses (HBV/HDV). Since then, this carrier has emerged as promising drug target for HBV/HDV virus entry inhibitors, but the synthetic peptide Hepcludex^® of high molecular weight is the only approved HDV entry inhibitor so far. The present study aimed to identify small molecules as novel NTCP inhibitors with anti-viral activity. A ligand-based bioinformatic approach was used to generate and validate appropriate pharmacophore and QSAR (quantitative structure–activity relationship) models. Half-maximal inhibitory concentrations (IC₅₀) for binding inhibition of the HBV/HDV-derived preS1 peptide (as surrogate parameter for virus binding to NTCP) were determined in NTCP-expressing HEK293 cells for 150 compounds of different chemical classes. IC₅₀ values ranged from 2 µM up to >1000 µM. The generated pharmacophore and QSAR models were used for virtual screening of drug-like chemicals from the ZINC¹⁵ database (~11 million compounds). The 20 best-performing compounds were then experimentally tested for preS1-peptide binding inhibition in NTCP-HEK293 cells. Among them, four compounds were active and revealed experimental IC₅₀ values for preS1-peptide binding inhibition of 9, 19, 20, and 35 µM, which were comparable to the QSAR-based predictions. All these compounds also significantly inhibited in vitro HDV infection of NTCP-HepG2 cells, without showing any cytotoxicity. The best-performing compound in all assays was ZINC000253533654. In conclusion, the present study demonstrates that virtual compound screening based on NTCP-specific pharmacophore and QSAR models can predict novel active hit compounds for the development of HBV/HDV entry inhibitors.

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening

Three-dimensional (3D) molecular similarity, one major ligand-based virtual screening (VS) method, has been widely used in the drug discovery process. A variety of 3D molecular similarity tools have been developed in recent decades. In this study, we assessed a panel of 15 3D molecular similarity programs against the DUD-E and LIT-PCBA datasets, including commercial ROCS and Phase, in terms of screening power and scaffold-hopping power. The results revealed that (1) SHAFTS, LS-align, Phase Shape_Pharm and LIGSIFT showed the best VS capability in terms of screening power. Some 3D similarity tools available to academia can yield relatively better VS performance than commercial ROCS and Phase software. (2) Current 3D similarity VS tools exhibit a considerable ability to capture actives with new chemotypes in terms of scaffold hopping. (3) Multiple conformers relative to single conformations will generally improve VS performance for most 3D similarity tools, with marginal improvement observed in area under the receiving operator characteristic curve values, enrichment factor in the top 1% and hit rate in the top 1% values showed larger improvement. Moreover, redundancy and complementarity analyses of hit lists from different query seeds and different 3D similarity VS tools showed that the combination of different query seeds and/or different 3D similarity tools in VS campaigns retrieved more (and more diverse) active molecules. These findings provide useful information for guiding choices of the optimal 3D molecular similarity tools for VS practices and designing possible combination strategies to discover more diverse active compounds.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Deep Learning in Virtual Screening: Recent Applications and Developments

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.

Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein-ligand interactions and quantitative structure-activity relationship studies

It is frequently mentioned that QSARs have not generally lived up to expectations, especially in cases where high predictability is expected yet failed to deliver satisfactory results. Even though outliers can provide an increased opportunity in drug discovery research, outliers in SAR and QSAR can contort predictions and affect the accuracy if proper attention is not given. The percentages of outliers in QSARs have not changed appreciably over the last decade. In our previous studies, we suggested two possible sources of outliers in SAR and QSAR. In this paper, we suggest an additional possible source of outliers in QSAR. We presented several literature examples that show one or more water molecules that play a critical role in protein-ligand binding interactions as observed in their crystal structures. These examples illustrate that failing to account for the effects of water molecules in protein-ligand interactions could mislead interpretation and possibly yield outliers in SAR and QSAR. Examples include cases where QSAR, considering the role of water molecules in protein-ligand crystal structures, provided deeper insight into the understanding and interpretation of the developed QSAR.

In silico docking analysis revealed the potential of phytochemicals present in Phyllanthus amarus and Andrographis paniculata, used in Ayurveda medicine in inhibiting SARS-CoV-2

The Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in outbreak of global pandemic, fatal pneumonia in human referred as Coronavirus Disease-2019 (Covid-19). Ayurveda, the age old practice of treating human ailments in India, can be considered against SARS-CoV-2. Attempt was made to provide preliminary evidences for interaction of 35 phytochemicals from two plants (Phyllanthus amarus and Andrographis paniculata used in Ayurveda) with SARS-CoV-2 proteins (open & closed state S protein, 3CLpro, PLpro and RdRp) through in silico docking analysis. The nucleotide analogue remdesivir, being used in treatment of SARS-CoV-2, was used as a positive control. The results revealed that 18 phytochemicals from P. amarus and 14 phytochemicals from A. paniculata shown binding energy affinity/dock score < - 6.0 kcal/mol, which is considered as minimum threshold for any compound to be used for drug development. Phytochemicals used for docking studies in the current study from P. amarus and A. paniculata showed binding affinity up to - 9.10 kcal/mol and - 10.60 kcal/mol, respectively. There was no significant difference in the binding affinities of these compounds with closed and open state S protein. Further, flavonoids (astragalin, kaempferol, quercetin, quercetin-3-O-glucoside and quercetin) and tannins (corilagin, furosin and geraniin) present in P. amarus have shown more binding affinity (up to - 10.60 kcal/mol) than remdesivir (up to - 9.50 kcal/mol). The pharmacokinetic predictions suggest that compounds from the two plants species studied in the current study are found to be non-carcinogenic, water soluble and biologically safe. The phytochemicals present in the extracts of P. amarus and A. paniculata might have synergistic effect with action on multiple target sites of SARS-CoV-2. The information generated here might serve as preliminary evidence for anti SARS-CoV-2 activity of phytochemicals present from P. amarus and A. paniculata and the potential of Ayurveda medicine in combating the virus.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-020-02578-7.