Integrating structure-based and ligand-based approaches for computational drug design

Virtual screening: hope, hype, and the fine line in between

INTRODUCTION

Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.

AREAS COVERED

This perspective examines the current VS landscape, highlighting essential practices and identifying critical challenges, limitations, and common pitfalls. Using case studies and practices, this perspective aims to highlight strategies that can effectively mitigate or overcome these challenges. Furthermore, the perspective explores common approaches for addressing pharmacodynamic and pharmacokinetic issues in optimizing VS hits.

EXPERT OPINION

VS has become a tried-and-true technique of drug discovery due to the rapid advances in computational methods and machine learning (ML) over the past two decades. Although each VS workflow varies depending on the chosen approach and methodology, integrated strategies that combine biological and in silico data have consistently yielded higher success rates. Moreover, the widespread adoption of ML has enhanced the integration of VS into the drug discovery pipeline. However, the absence of standardized evaluation criteria hinders the objective assessment of VS studies' success and the identification of optimal adoption methods.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein-ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins.

Role of HSP90 in Type 2 Diabetes Mellitus and Its Association with Liver Diseases

Non-alcoholic fatty acid liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC) are the fatal liver diseases which encompass a spectrum of disease severity associated with increased risk of type 2 diabetes mellitus (T2DM), a metabolic disorder. Heat shock proteins serve as markers in early prognosis and diagnosis of early stages of liver diseases associated with metabolic disorder. This review aims to comprehensively investigate the significance of HSP90 isoforms in T2DM and liver diseases. Additionally, we explore the collective knowledge on plant-based drug compounds that regulate HSP90 isoform targets, highlighting their potential in treating T2DM-associated liver diseases. Furthermore, this review focuses on the computational systems' biology and next-generation sequencing technology approaches that are used to unravel the potential medicine for the treatment of pleiotropy of these 2 diseases and to further elucidate the mechanism.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Structure-Activity Relationship Studies on VEGFR2 Tyrosine Kinase Inhibitors for Identification of Potential Natural Anticancer Compounds

BACKGROUND

Over-expression of Vascular Endothelial Growth Factor Receptors (VEGFRs) leads to the hyperactivation of oncogenes. For inhibition of this hyperactivation, the USA Food Drug Administration (FDA) has approved many drugs that show adverse effects, such as hypertension, hypothyroidism, etc. There is a need to discover potent natural compounds that show minimal side effects. In the present study, we have taken structurally diverse known VEGFR2 inhibitors to develop a Quantitative Structure-Activity Relationship (QSAR) model and used this model to predict the inhibitory activity of natural compounds for VEGFR2.

METHODS

The QSAR model was developed through the forward stepwise Multiple Linear Regression (MLR) method. A developed QSAR model was used to predict the inhibitory activity of natural compounds. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) assessment and molecular docking studies were performed. The binding stability of the natural compounds with VEGFR2 was elucidated through Molecular Dynamics (MD) simulation.

RESULTS

The developed QSAR model against VEGFR2 showed the regression coefficient of the training dataset (r2) as 0.81 and the external regression coefficient of the test dataset (r2 test) 0.71. Descriptors, viz., electro-topological state of potential hydrogen bonds (maxHBint2, nHBint6), atom types (minssNH), maximum topological distance matrix (SpMAD_Dt), and 2D autocorrelation (ATSC7v), have been identified. Using this model, 14 natural compounds have been selected that have shown inhibitory activity for VEGFR2, of which six natural compounds have been found to possess a strong binding affinity with VEGFR2. In MD simulation, four complexes have shown binding stability up to 50ns.

CONCLUSION

The developed QSAR model has identified 5 conserved activity-inducing physiochemical properties, which have been found to be correlated with the anticancer activity of the nonidentical ligand molecules bound with the VEGFR2 kinase. Lavendustin_A, 3'-O-acetylhamaudol, and arctigenin have been obtained as possible lead natural compounds against the VEGFR2 kinase.

SARS-CoV-2 external structures interacting with nanospheres using docking and molecular dynamics

Coronavirus is caused by the SARS-CoV-2 virus has shown rapid proliferation and scarcity of treatments with proven effectiveness. In this way, we simulated the hospitalization of carbon nanospheres, with external active sites of the SARS-CoV-2 virus (M-Pro, S-Gly and E-Pro), which can be adsorbed or inactivated when interacting with the nanospheres. The computational procedures performed in this work were developed with the SwissDock server for molecular docking and the GROMACS software for molecular dynamics, making it possible to extract relevant data on affinity energy, distance between molecules, free Gibbs energy and mean square deviation of atomic positions, surface area accessible to solvents. Molecular docking indicates that all ligands have an affinity for the receptor's active sites. The nanospheres interact favorably with all proteins, showing promising results, especially C60, which presented the best affinity energy and RMSD values ​​for all protein macromolecules investigated. The C60 with E-Pro exhibited the highest affinity energy of -9.361 kcal/mol, demonstrating stability in both molecular docking and molecular dynamics simulations. Our RMSD calculations indicated that the nanospheres remained predominantly stable, fluctuating within a range of 2 to 3 Å. Additionally, the analysis of other structures yielded promising results that hold potential for application in other proteases.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors

FLT3 is considered a potential target of acute myeloid leukemia therapy. In this study, we applied a computer-aided methodology unifying molecular docking and pharmacophore screening to identify potent inhibitors against FLT3. To investigate the pharmacophore area and binding mechanism of FLT3, the reported co-crystallized Gilteritinib ligand was docked into the active site using Glide XP. Based on the docking results, we identified structure-based pharmacophore characteristics resistant to potent FLT3 inhibitors. The best hypothesis was corroborated using test and decoy sets, and the verified hypo was utilized to screen the chemical database. The hits from the pharmacophore-based screening were then screened again using a structure-based method that included molecular docking at various precisions; the selected molecules were further examined and refined using drug-like filters and ADMET analysis. Finally, two hits were picked out for molecular dynamic simulation. The results showed two hits were expected to have potent inhibitory activity and excellent ADMET characteristics, and they might be used as new leads in the development of FLT3 inhibitors.Communicated by Ramaswamy H. Sarma.

Fighting Antimicrobial Resistance: Insights on How the Staphylococcus aureus NorA Efflux Pump Recognizes 2-Phenylquinoline Inhibitors by Supervised Molecular Dynamics (SuMD) and Molecular Docking Simulations

The superbug Staphylococcus aureus (S. aureus) exhibits several resistance mechanisms, including efflux pumps, that strongly contribute to antimicrobial resistance. In particular, the NorA efflux pump activity is associated with S. aureus resistance to fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from cells. Thus, since efflux pump inhibitors (EPIs) are able to increase antibiotic concentrations in bacteria as well as restore their susceptibility to these agents, they represent a promising strategy to counteract bacterial resistance. Additionally, the very recent release of two NorA efflux pump cryo-electron microscopy (cryo-EM) structures in complex with synthetic antigen-binding fragments (Fabs) represents a real breakthrough in the study of S. aureus antibiotic resistance. In this scenario, supervised molecular dynamics (SuMD) and molecular docking experiments were combined to investigate for the first time the molecular mechanisms driving the interaction between NorA and efflux pump inhibitors (EPIs), with the ultimate goal of elucidating how the NorA efflux pump recognizes its inhibitors. The findings provide insights into the dynamic NorA-EPI intermolecular interactions and lay the groundwork for future drug discovery efforts aimed at the identification of novel molecules to fight antimicrobial resistance.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

5,6-Dihydrotetrazolo[1,5-c]quinazolines: Toxicity prediction, synthesis, antimicrobial activity, molecular docking, and perspectives

Antimicrobial resistance is a never-ending challenge, which should be considered seriously, especially when using unprescribed "over-the-counter" drugs. The synthesis and investigation of novel biologically active substances is among the directions to overcome this problem. Hence, 18 novel 5,6-dihydrotetrazolo[1,5-c]quinazolines were synthesized, their identity, purity, and structure were elucidated by elemental analysis, IR, LC-MS, ¹ Н, and ¹³ C NMR spectra. According to the computational estimation, 15 substances were found to be of toxicity Class V, two of Class IV, and only one of Class II. The in vitro serial dilution method of antimicrobial screening against Escherichia coli, Staphylococcus aureus, Klebsiella aerogenes, Pseudomonas aeruginosa, and Candida albicans determined b3, c1, c6, and c10 as the "lead-compounds" for further modifications to increase the level of activity. Substance b3 demonstrated antibacterial activity that can be related to the calculated high affinity toward all studied proteins: 50S ribosomal protein L19 (PDB ID: 6WQN), sterol 14-alpha demethylase (PDB ID: 5TZ1), and ras-related protein Rab-9A (PDB ID: 1WMS). The structure-activity and structure-target affinity relationships are discussed. The targets for further investigations and the anatomical therapeutic chemical codes of drug similarity are predicted.

Design, Synthesis, Kinetic Analysis and Pharmacophore-Directed Discovery of 3-Ethylaniline Hybrid Imino-Thiazolidinone as Potential Inhibitor of Carbonic Anhydrase II: An Emerging Biological Target for Treatment of Cancer

Carbonic anhydrases (CA), having Zn2+ metal atoms, are responsible for the catalysis of CO2 and water to bicarbonate and protons. Any abnormality in the functioning of these enzymes may lead to morbidities such as glaucoma and different types of cancers including brain, renal and pancreatic carcinomas. To cope with the lack of presence of a promising therapeutic agent against these cancers, searching for an efficient and suitable carbonic anhydrase inhibitor is crucial. In the current study, ten novel 3-ethylaniline hybrid imino-thiazolidinones were synthesized and characterized by FTIR, NMR (1H, 13C), and mass spectrometry. Synthesis was carried out by diethyl but-2-ynedioate cyclization and different acyl thiourea substitutions of 3-ethyl amine. The CA (II) enzyme inhibition profile for all synthesized derivatives was determined. It was observed that compound 6e demonstrated highest inhibition of CA-II with an IC50 value of 1.545 ± 0.016 µM. In order to explore the pharmacophoric properties and develop structure activity relationship, in silico screening was performed. In silico investigations included density functional theory (DFT) studies, pharmacophore-guided model development, molecular docking, molecular dynamic (MD) simulations, and prediction of drug likeness scores. DFT investigations provided insight into the electronic characteristics of compounds, while molecular docking determined the binding orientation of derivatives within the CA-II active site. Compounds 6a, 6e, and 6g had a reactive profile and generated stable protein-ligand interactions with respective docking scores of -6.12, -6.99, and -6.76 kcal/mol. MD simulations were used to evaluate the stability of the top-ranked complex. In addition, pharmacophore-guided modeling demonstrated that compound 6e produced the best pharmacophore model (HHAAARR) compared to standard brinzolamide. In vitro and in silico investigations anticipated that compound 6e would be an inhibitor of carbonic anhydrase II with high efficacy. Compound 6e may serve as a potential lead for future synthesis that can be investigated at the molecular level, and additional in vivo studies are strongly encouraged.

BoostSweet: Learning molecular perceptual representations of sweeteners

The development of safe artificial sweeteners has attracted considerable interest in the food industry. Previous machine learning (ML) studies based on quantitative structure-activity relationships have provided some molecular principles for predicting sweetness, but these models can be improved via the chemical recognition of sweetness active factors. Our ML model, a soft-vote ensemble model that has a light gradient boosting machine and uses both layered fingerprints and alvaDesc molecular descriptor features, demonstrates state-of-the-art performance, with an AUROC score of 0.961. Based on an analysis of feature importance and dataset, we identified that the number of nitrogen atoms that serve as hydrogen bond donors in molecules can play an essential role in determining sweetness. These results potentially provide an advanced understanding of the relationship between molecular structure and sweetness, which can be used to design new sweeteners based on molecular structural dependence.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Virtual screening and molecular dynamics simulation analysis of Forsythoside A as a plant-derived inhibitor of SARS-CoV-2 3CLpro

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a more severe strain of coronavirus (CoV) that was first emerged in China in 2019. Available antiviral drugs could be repurposed and natural compounds with antiviral activity could be safer and cheaper source of medicine for SARS-CoV-2. 78 natural antiviral compounds database was identified from literature and virtual screening technique was applied to identify potential 3-chymotrypsin-like protease (3CLpro) inhibitors. Molecular docking studies were conducted to analyze the main protease (3CLpro) and inhibitors interactions with key residues of active site of target protein (PDB ID: 6LU7), active site constitute the part of active domain I and II of 3CLpro. 10 compounds with highest dock score were subjected to calculate ADMET parameters to figure out drug-likeness. Molecular dynamic (MD) simulation of the selected lead was performed by Amber simulation package to understand the conformational changes in docked complex. MD simulations analysis (RMSD, RMSF, Rg, BF, HBs, and SASA plots) of lead bounded with 3CLpro, hence revealed the important structural turns and twists during MD simulations from 0 to 100 ns. MM-PBSA/GBSA methods has also been applied for the estimation binding free energy (BFE) of the selected lead-complex. The present study has identified lead compound “Forsythoside A” an active extract of Forsythia suspense as SARS-CoV-2 3CLpro inhibitor that can block the viral replication and translation. Structural analysis of target protein and lead compound performed in this study could contribute to the development of potential drug against SARS-CoV-2 infection.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.

Montelukast, cysteinyl leukotriene receptor 1 antagonist, inhibits cardiac fibrosis by activating APJ

Montelukast, cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is used clinically for patients with asthma, chronic obstructive pulmonary diseases (COPD), and allergic rhinitis. It has been reported that CysLT1R antagonists could reduce the risks of cardiovascular diseases in animal studies. Cardiac fibrosis is one of the major causes of heart failure. But little is known about the role of Montelukast in cardiac fibrosis and its underlying mechanism. In transverse aortic constriction (TAC) mice, Montelukast improved cardiac pumping function and inhibited cardiac fibrosis by down-regulation of the proteins related to the fibrosis, such as connective tissue growth factor (CTGF), Transforming Growth Factor β (TGF-β), and Alpha-smooth muscle actin (α-SMA). Montelukast reduced cell proliferation and collagen production in neonatal cardiac fibroblasts (CFs) with the pretreatment of 20% serum, while down-regulating the expression of TGF-β, CTGF and α-SMA. Molecules docking methods estimated a high affinity of Montelukast to Apelin receptor (APJ) and an effective chemical structure for Montelukast binding APJ. In Chinese hamster ovary (CHO) cells with stable overexpressing APJ, Montelukast inhibited forskolin (1 μM)-mediated cyclic adenosine monophosphate (cAMP) production and extracellular signal-regulated kinase1/2 (ERK1/2) phosphorylation, while these effects were reversed by pertussis toxin (PTX) pretreatment. APJ silence disrupted the effects of Montelukast in CFs pretreatment by serum 20%. So we concluded that Montelukast inhibited cardiac fibrosis due presumably to the coupling to the APJ-mediated Gi signaling pathway, which may be a promising therapeutic target for cardiac fibrosis.

Drug Design-Past, Present, Future

Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug-receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author's point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules' Special Issue "Drug Design-Science and Practice".

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds' databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method

Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis

Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fields. This paper employs a co-word analysis approach to map the intellectual structure of the coronavirus literature for a better understanding of how coronavirus research and the disease itself have developed during the target timeframe. A strategic diagram has been drawn to depict the coronavirus domain's structure and development. A detailed picture of coronavirus literature has been extracted from a huge number of papers to provide a quick overview of the coronavirus literature. The main themes of past coronavirus-related publications are (a) "Antibody-Virus Interactions," (b) "Emerging Infectious Diseases," (c) "Protein Structure-based Drug Design and Antiviral Drug Discovery," (d) "Coronavirus Detection Methods," (e) "Viral Pathogenesis and Immunity," and (f) "Animal Coronaviruses." The emerging infectious diseases are mostly related to fatal diseases (such as Middle East respiratory syndrome, severe acute respiratory syndrome, and COVID-19) and animal coronaviruses (including porcine, turkey, feline, canine, equine, and bovine coronaviruses and infectious bronchitis virus), which are capable of placing animal-dependent industries such as the swine and poultry industries under strong economic pressure. Although considerable research into coronavirus has been done, this unique field has not yet matured sufficiently. Therefore, "Antibody-virus Interactions," "Emerging Infectious Diseases," and "Coronavirus Detection Methods" hold interesting, promising research gaps to be both explored and filled in the future.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.

Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions

The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.

Peptides-based vaccine against SARS-nCoV-2 antigenic fragmented synthetic epitopes recognized by T cell and β-cell initiation of specific antibodies to fight the infection

The World Health Organization has declared the rapidly spreading coronavirus to be a global pandemic. The FDA is yet to approve a vaccine for human novel coronavirus. Here, we developed a peptide-based vaccine and used high-throughput screening by molecular dynamics simulation to identify T-cell- and β-cell-recognized epitopes for producing specific antibodies against SARS-nCoV-2. We construct ~ 12 P' antigenic epitope peptides to develop a more effective vaccine and identify specific antibodies. These epitope peptides selectively presented the best antigen presentation scores for both human pMHC class I and II alleles to develop a strong binding affinity. All antigens identified of SARS-nCoV-2 different proteins by each attached specific ~ 1-7 L linker adaptor were used to construct a broad single peripheral peptide vaccine. It is expected to be highly antigenic with a minimum allergic effect. As a result of these exciting outcomes, expressing a vaccine using the intimated peptide was highly promising and positive to be highly proposed as epitope-based peptide vaccine of specific antibody against SARS-nCoV-2 by initiating T cells and β-cells. An in vitro study for the proposed peptide-based vaccine is mostly recommended. Further clinical trials are required to check the efficacy of this vaccine.

Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration

The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Single Crystal X-Ray Structure for the Disordered Two Independent Molecules of Novel Isoflavone: Synthesis, Hirshfeld Surface Analysis, Inhibition and Docking Studies on IKKβ of 3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-6,7-dimethoxy-4H-chromen-4-one

The structure of the isoflavone compound, 3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-6,7-dimethoxy-4H-chromen-4-one (5), was elucidated by 2D-NMR spectra, mass spectrum and single crystal X-ray crystallography. Compound 5, C19H16O6, was crystallized in the monoclinic space group P21/c with the cell parameters; a = 12.0654(5) Å, b =11.0666(5) Å, c = 23.9550(11) Å, β = 101.3757(16)°, V = 3135.7(2) Å3, and Z = 8. The asymmetric unit of compound 5 consists of two independent molecules 5I and 5II. Both molecules exhibit the disorder of each methylene group present in their 1,4-dioxane rings with relative occupancies of 0.599(10) (5I) and 0.812(9) (5II) for the major component A, and 0.401(10) (5I) and 0.188(9) (5II) for the minor component B, respectively. Each independent molecule revealed remarkable discrepancies in bond lengths, bond angles and dihedral angles in the disordered regions of 1,4-dioxane rings. The common feature of the molecules 5I and 5II are a chromone ring and a benzodioxin ring, which are more tilted towards each other in 5I than in 5II. An additional difference between the molecules is seen in the relative disposition of two methoxy substituents. In the crystal, the molecule 5II forms inversion dimers which are linked into chains along an a-axis direction by intermolecular C–H⋯O interactions. Additional C–H⋯O hydrogen bonds connected the molecules 5I and 5II each other to form a three-dimensional network. Hirshfeld surface analysis evaluated the relative intermolecular interactions which contribute to each crystal structure 5I and 5II. Western blot analysis demonstrated that compound 5 inhibited the TNFα-induced phosphorylation of IKKα/β, resulting in attenuating further downstream NF-κB signaling. A molecular docking study predicted the possible binding of compound 5 to the active site of IKKβ. Compound 5 showed an inhibitory effect on the clonogenicity of HCT116 human colon cancer cells. These results suggest that compound 5 can be used as a platform for the development of an anti-cancer agent targeting IKKα/β.

In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line

BACKGROUND

Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods.

RESULTS

A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range -10.8 to -11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme.

CONCLUSION

The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.

Interfering with S100B-effector protein interactions for cancer therapy

S100 calcium-binding protein B (S100B) is overexpressed in various malignant tumors, where it regulates cancer cell proliferation and metabolism by physical interactions with other molecules. Interfering with S100B-effector protein interactions is a potential strategy to treat malignant tumors. Although some S100B inhibitors have been discovered by virtual screening (VS), most target the S100B-p53 interaction. Hence, there is scope for the discovery of other S100B-effector protein interaction modulators for malignant tumors. In this review, we provide an overview of S100B-effector protein interaction inhibitor discovery using VS and discuss promising S100B-effector protein interaction targets that permit in silico analysis for drug discovery.

Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening

The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

In silico strategies for probing novel DPP-IV inhibitors as anti-diabetic agents

Identification of new DPP-IV inhibitors by integrating validated in silico approach is being presented herein. Novel hits were identified by combining pharmacophore and structure based virtual screening of ZINC and Knowledge Base in house database followed by ADME profiling, consensus docking studies. Six potential hits were identified and analysed for their synthetic accessibility score, novelty analysis and pan assay interference compounds filtration. Out of six, three hits viz., ZINC25060187, ZINC53746227 and KB-10 were analysed for stability studies using Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Dynamics (MD) simulation. The simulation studies of the identified hits revealed that these hits have good selectivity and stability in DPP-IV binding pocket. Important interactions with amino acids viz., Tyr547, Glu205 and Glu206 similar to co-crystallized ligand were also observed. One of the hits viz., KB-10 was synthesized and evaluated for its biological potential. The compound KB-10 showed good DPP-IV inhibition in both in vitro and in vivo studies with IC₅₀: 22.69 µM. This study supports the fact that these techniques hold potential for efficient screening of compounds with unknown affinity for DPP-IV that could serve as candidates for therapeutic development.Communicated by Ramaswamy H. Sarma.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Mining of potential dipeptidyl peptidase-IV inhibitors as anti-diabetic agents using integrated in silico approaches

The dipeptidyl peptidase-IV (DPP-IV) family of receptors possesses a large binding cavity that imparts promiscuity for number of ligand binding which is not common to other receptors. This feature increases the challenge of using computational methods to identify DPP-IV inhibitors, therefore using both pharmacophore and structure-based screening seems to be a reliable approach. Mining of novel DPP-IV inhibitors by integrating both of these in silico techniques was reported. Pharmacophore model (Model_008) obtained from structurally diverse reported compounds was used as a template for screening of MolMall database followed by structure-based screening against PDB ID: 5T4E. After absorption, distribution, metabolism and excretion (ADME) analysis of shortlisted compounds, consensus docking and molecular mechanics/generalized born surface area studies were carried out. The results of the docking studies obtained were comparable to that of the reference ligand. Out of nine hits identified, only one hit (ID MolMall-20062) was available which was procured through exchange program. Molecular dynamic simulation studies of the procured hit revealed its good selectivity and stability in DPP-IV binding pocket and interactions observed with important amino acids viz., Trp629, Lys544 and Arg125. Biological testing of the compound MolMall-20062 showed promising DPP-IV inhibition activity with IC₅₀: 6.2 µM. Compound MolMall-20062 could be taken as a good lead for the development of DPP-IV inhibitors.AbbreviationsADMEabsorption, distribution, metabolism and excretionChEBIchemical entities of biological interestDPP-IVdipeptidyl peptidase IVDISCOtechdistance comparisonsHTVShigh throughput virtual screeningMDmolecular dynamicsMM-GBSAmolecular mechanics-generalized born surface areaOGTToral glucose tolerance testPBVSpharmacophore-based virtual screeningPDBprotein data bankRMSDroot mean square deviationROCreceiver operating characteristicsSPstandard precisionSBVSstructure-based virtual screeningVSvirtual screeningXPextra precisionCommunicated by Ramaswamy H. Sarma.