Structure-Based Virtual Screening: From Classical to Artificial Intelligence

Cheminformatics and biomolecular dynamics studies towards the discovery of anti-staphylococcal nuclease domain-containing 1 (SND1) inhibitors to treat metastatic breast cancer

Metastatic breast cancer is a prime health concern and leading health burden across the globe. Previous efforts have shown that protein-protein interaction between Metadherin and Staphylococcal nuclease domaincontaining 1 (SND1) promotes initiation of breast cancer, progression, therapy resistance and metastasis. Therefore, small drug molecules that can interrupt the Metadherin and SND1 interaction may be ideal to suppress tumor growth, metastasis and increases chemotherapy sensitivity of triple negative breast cancer. Here, in this study, structure based virtual screening was conducted against the reported active site of SND1 enzyme, which revealed three promising lead molecules from Asinex library. These compounds were; BAS_00381028, BAS_00327287, and BAS_01293454 with binding energy score -10.25 kcal/mol, -9.65 kcal/mol and -9.32 kcal/mol, respectively. Compared to control (5-chloro-2-methoxy-N-([1,2,4]triazolo[1,5-a]pyridin-8-yl)benzene-1-sulfonamide) the lead molecules showed robust hydrophilic and hydrophobic interactions with the enzyme and revealed stable docked conformation in molecular dynamics simulation. During the simulation time, the compounds reported stable dynamics with no obvious fluctuation in binding mode and interactions noticed. The mean root mean square deviation (RMSD) of BAS_00381028, BAS_00327287, and BAS_01293454 complexes were 1.87 Å, 1.75 Å, 1.34 Å, respectively. Furthermore, the MM/GBSA analysis was conduction on the simulation trajectories of complexes that unveiled binding energy score of -19.25 kcal/mol, -27.03 kcal/mol, -34.6 kcal/mol and -29.61 kcal/mol for control, BAS_00381028, BAS_00327287, and BAS_01293454, respectively. In MM/PBSA, the binding energy value of for control, BAS_00381028, BAS_00327287, and BAS_01293454 was -20.45 kcal/mol, -27.89 kcal/mol, -36.41 kcal/mol and -32.01 kcal/mol, respectively. Additionally, the compounds were classified as druglike and have favorable pharmacokinetic properties. The compounds were predicted as promising leads and might be used in experimental investigation to study their anti-SND1 activity.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations

Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.

Advances in non-hormonal male contraception targeting sperm motility

BACKGROUND

The high rates of unintended pregnancy and the ever-growing world population impose health, economic, social, and environmental threats to countries. Expanding contraceptive options, including male methods, are urgently needed to tackle these global challenges. Male contraception is limited to condoms and vasectomy, which are unsuitable for many couples. Thus, novel male contraceptive methods may reduce unintended pregnancies, meet the contraceptive needs of couples, and foster gender equality in carrying the contraceptive burden. In this regard, the spermatozoon emerges as a source of druggable targets for on-demand, non-hormonal male contraception based on disrupting sperm motility or fertilization.

OBJECTIVE AND RATIONALE

A better understanding of the molecules governing sperm motility can lead to innovative approaches toward safe and effective male contraceptives. This review discusses cutting-edge knowledge on sperm-specific targets for male contraception, focusing on those with crucial roles in sperm motility. We also highlight challenges and opportunities in male contraceptive drug development targeting spermatozoa.

SEARCH METHODS

We conducted a literature search in the PubMed database using the following keywords: 'spermatozoa', 'sperm motility', 'male contraception', and 'drug targets' in combination with other related terms to the field. Publications until January 2023 written in English were considered.

OUTCOMES

Efforts for developing non-hormonal strategies for male contraception resulted in the identification of candidates specifically expressed or enriched in spermatozoa, including enzymes (PP1γ2, GAPDHS, and sAC), ion channels (CatSper and KSper), transmembrane transporters (sNHE, SLC26A8, and ATP1A4), and surface proteins (EPPIN). These targets are usually located in the sperm flagellum. Their indispensable roles in sperm motility and male fertility were confirmed by genetic or immunological approaches using animal models and gene mutations associated with male infertility due to sperm defects in humans. Their druggability was demonstrated by the identification of drug-like small organic ligands displaying spermiostatic activity in preclinical trials.

WIDER IMPLICATIONS

A wide range of sperm-associated proteins has arisen as key regulators of sperm motility, providing compelling druggable candidates for male contraception. Nevertheless, no pharmacological agent has reached clinical developmental stages. One reason is the slow progress in translating the preclinical and drug discovery findings into a drug-like candidate adequate for clinical development. Thus, intense collaboration among academia, private sectors, governments, and regulatory agencies will be crucial to combine expertise for the development of male contraceptives targeting sperm function by (i) improving target structural characterization and the design of highly selective ligands, (ii) conducting long-term preclinical safety, efficacy, and reversibility evaluation, and (iii) establishing rigorous guidelines and endpoints for clinical trials and regulatory evaluation, thus allowing their testing in humans.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Discovery of new Schistosoma mansoni aspartyl protease inhibitors by structure-based virtual screening

BACKGROUND

Schistosomiasis is a neglected tropical disease caused by trematodes of the genus Schistosoma, with a limited treatment, mainly based on the use of praziquantel (PZQ). Currently, several aspartic proteases genes have already been identified within the genome of Schistosoma species. At least one enzyme encoded from this gene family (SmAP), named SmCD1, has been validated for the development of schistosomicidal drugs, since it has a key role in haemoglobin digestion by worms.

OBJECTIVE

In this work, we integrated a structure-based virtual screening campaign, enzymatic assays and adult worms ex vivo experiments aiming to discover the first classes of SmCD1 inhibitors.

METHODS

Initially, the 3D-structures of SmCD1, SmCD2 and SmCD3 were generated using homology modelling approach. Using these models, we prioritised 50 compounds from 20,000 compounds from ChemBridge database for further testing in adult worm aqueous extract (AWAE) and recombinant SmCD1 using enzymatic assays.

FINDINGS

Seven compounds were confirmed as hits and among them, two compounds representing new chemical scaffolds, named 5 and 19, had IC50 values against SmCD1 close to 100 μM while presenting binding efficiency indexes comparable to or even higher than pepstatin, a classical tight-binding peptide inhibitor of aspartyl proteases. Upon activity comparison against mammalian enzymes, compound 50 was selective and the most potent against the AWAE aspartic protease activity (IC50 = 77.7 μM). Combination of computational and experimental results indicate that compound 50 is a selective inhibitor of SmCD2. Compounds 5, 19 and 50 tested at low concentrations (10 uM) were neither cytotoxic against WSS-1 cells (48 h) nor could kill adult worms ex-vivo, although compounds 5 and 50 presented a slight decrease on female worms motility on late incubations times (48 or 72 h).

MAIN CONCLUSION

Overall, the inhibitors identified in this work represent promising hits for further hit-to-lead optimisation.

Flavonoids as P-glycoprotein inhibitors for multidrug resistance in cancer: an in-silico approach

Cancer has become a leading cause of mortality due to non-communicable diseases after cardiovascular disease worldwide and is increasing day by day at a daunting pace. According to an estimate by 2040 there will be 28.4 million cancer cases. Occurrence of multidrug resistance has further worsened the scenario of available cancer treatment. Among different mechanisms of multidrug resistance efflux of xenobiotics by ABC transporter is of prime importance. P-glycoprotein (P-gp) is the major factor behind occurrence of multidrug resistance due to its wide distribution and invariably big binding cavity. Various generations of chemical inhibitors for P-gp have been designed and tested are not devoid of major side effects. Thus, in present study flavonoids a major class of natural compounds was virtually screened in order to find molecules which can be used as selective P-gp inhibitors to be used along with chemotherapeutics. After screening 4275 molecules from different classes of flavonoids i.e. flavan, flavanol, flavonone, flavone, anthocyanins, and isoflavone, through Glide docking top ten hit molecules were selected based on their binding affinity, binding energy calculation and pharmacokinetic properties. All the hit molecules were found to have docking score within the range of -11.202 to -9.699 kcal/mol showing very strong interaction with the amino acid residues of binding pocket. Whereas, dock score of standard P-gp inhibitor verapamil was -4.984 kcal/mol. The ligand and protein complex were found to be quite stable while run through molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Design, synthesis, biological evaluation and molecular dynamics of some novel 3-phenylpyrazolo[1,5-a]pyrimidine-2,7(1H,4H)-dione based compounds as anti-tubercular agents

Decaprenylphosphoryl-β-d-ribose-2'-epimerase (DprE1) is a druggable target which is being exploited for the development of new anti-TB agents. In the present work, we report developing a pharmacophore model and performing virtual screening of Asinex database using the developed pharmacophore model to get eight hits as potential DprE1 inhibitors. The hits were used as leads to design new 3-phenylpyrazolo[1,5-a]pyrimidine-2,7(1H,4H)-dione based potential anti-TB agents. On the basis of the identified lead molecules, a total of 18 compounds were synthesized and evaluated for their anti-TB activity by using MABA. ADMET predictions for all the compounds revealed that these compounds have drug-like and lead-like properties. One of the final compounds was found to exhibit potent anti-TB activity against Mycobacterium bovis.Communicated by Ramaswamy H. Sarma.

In silico approach identified benzoylguanidines as SARS-CoV-2 main protease (Mpro) potential inhibitors

The coronavirus disease-2019 (COVID-19) pandemic, caused by the novel coronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), became the highest public health crisis nowadays. Although the use of approved vaccines for emergency immunization and the reuse of FDA-approved drugs remains at the forefront, the search for new, more selective, and potent drug candidates from synthetic compounds is also a viable alternative to combat this viral disease. In this context, the present study employed a computational virtual screening approach based on molecular docking and molecular dynamics (MD) simulation to identify possible inhibitors for SARS-CoV-2 Mpro (main protease), an important molecular target required for the maturation of the various polyproteins involved in viral replication. The virtual screening approach selected four potential inhibitors against SARS-CoV-2 Mpro. In addition, MD simulation studies revealed changes in the positions of the ligands during the simulations compared to the complex obtained in the molecular docking studies, showing the benzoylguanidines LMed-110 and LMed-136 have a higher affinity for the active site compared to the other structures that tended to leave the active site. Besides, there was a better understanding of the formation and stability of the existing H-bonds in the formed complexes and the energetic contributions to the stability of the target-ligand molecular complexes. Finally, the in silico prediction of the ADME profile suggested that LMed-136 has drug-like characteristics and good pharmacokinetic properties. Therefore, from the present study, it can be suggested that these structures can inhibit SARS-CoV-2 Mpro. Nevertheless, further studies are needed in vitro assays to investigate the antiviral properties of these structures against SARS-CoV-2.

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions

Motivation

Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information.

Results

In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life.

Availability and implementation

The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.

Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED's ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC50 value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

Deciphering Molecular Aspects of Potential α-Glucosidase Inhibitors within Aspergillus terreus: A Computational Odyssey of Molecular Docking-Coupled Dynamics Simulations and Pharmacokinetic Profiling

Hyperglycemia, as a hallmark of the metabolic malady diabetes mellitus, has been an overwhelming healthcare burden owing to its high rates of comorbidity and mortality, as well as prospective complications affecting different body organs. Available therapeutic agents, with α-glucosidase inhibitors as one of their cornerstone arsenal, control stages of broad glycemia while showing definitive characteristics related to their low clinical efficiency and off-target complications. This has propelled the academia and industrial section into discovering novel and safer candidates. Herein, we provided a thorough computational exploration of identifying candidates from the marine-derived Aspergillus terreus isolates. Combined structural- and ligand-based approaches using a chemical library of 275 metabolites were adopted for pinpointing promising α-glucosidase inhibitors, as well as providing guiding insights for further lead optimization and development. Structure-based virtual screening through escalating precision molecular docking protocol at the α-glucosidase canonical pocket identified 11 promising top-docked hits, with several being superior to the market drug reference, acarbose. Comprehensive ligand-based investigations of these hits' pharmacokinetics ADME profiles, physiochemical characterizations, and obedience to the gold standard Lipinski's rule of five, as well as toxicity and mutagenicity profiling, proceeded. Under explicit conditions, a molecular dynamics simulation identified the top-stable metabolites: butyrolactone VI (SK-44), aspulvinone E (SK-55), butyrolactone I 4''''-sulfate (SK-72), and terrelumamide B (SK-173). They depicted the highest free binding energies and steadiest thermodynamic behavior. Moreover, great structural insights have been revealed, including the advent of an aromatic scaffold-based interaction for ligand-target complex stability. The significance of introducing balanced hydrophobic/polar moieties, like triazole and other bioisosteres of carboxylic acid, has been highlighted across docking, ADME/Tox profiling, and molecular dynamics studies for maximizing binding interactions while assuring safety and optimal pharmacokinetics for targeting the intestinal-localized α-glucosidase enzyme. Overall, this study provided valuable starting points for developing new α-glucosidase inhibitors based on nature-derived unique scaffolds, as well as guidance for prospective lead optimization and development within future pre-clinical and clinical investigations.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Toward α-1,3/4 fucosyltransferases targeted drug discovery: In silico uncovering of promising natural inhibitors of fucosyltransferase 6

Sialyl Lewis X (sLex ) antigen is a fucosylated cell-surface glycan that is normally involved in cell-cell interactions. The enhanced expression of sLex on cell surface glycans, which is attributed to the upregulation of fucosyltransferase 6 (FUT6), has been implicated in facilitating metastasis in human colorectal, lung, prostate, and oral cancers. The role that the upregulated FUT6 plays in the progression of tumor to malignancy, with reduced survival rates, makes it a potential target for anticancer drugs. Unfortunately, the lack of experimental structures for FUT6 has hampered the design and development of its inhibitors. In this study, we used in silico techniques to identify potential FUT6 inhibitors. We first modeled the three-dimensional structure of human FUT6 using AlphaFold. Then, we screened the natural compound libraries from the COCONUT database to sort out potential natural products (NPs) with best affinity toward the FUT6 model. As a result of these simulations, we identified three NPs for which we predicted binding affinities and interaction patterns quite similar to those we calculated for two experimentally tested FUT6 inhibitors, that is, fucose mimetic-1 and a GDP-triazole derived compound. We also performed molecular dynamics (MD) simulations for the FUT6 complexes with identified NPs, to investigate their stability. Analysis of the MD simulations showed that the identified NPs establish stable contacts with FUT6 under dynamics conditions. On these grounds, the three screened compounds appear as promising natural alternatives to experimentally tested FUT6 synthetic inhibitors, with expected comparable binding affinity. This envisages good prospects for future experimental validation toward FUT6 inhibition.

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking

Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iteratively refines a ligand pose. We combine these two approaches by training a pose scoring function in a diffusion-inspired manner. In our method, PLANTAIN, a neural network is used to develop a very fast pose scoring function. We parameterize a simple scoring function on the fly and use L-BFGS minimization to optimize an initially random ligand pose. Using rigorous benchmarking practices, we demonstrate that our method achieves state-of-the-art performance while running ten times faster than the next-best method. We release PLANTAIN publicly and hope that it improves the utility of virtual screening workflows.

In Silico Screening and Identification of Antidiabetic Inhibitors Sourced from Phytochemicals of Philippine Plants against Four Protein Targets of Diabetes (PTP1B, DPP-4, SGLT-2, and FBPase)

Current oral medications for type 2 diabetes target a single main physiological mechanism. They either activate or inhibit receptors to enhance insulin sensitivity, increase insulin secretion, inhibit glucose absorption, or inhibit glucose production. In advanced stages, combination therapy may be required because of the limited efficacy of single-target drugs; however, medications are becoming more costly, and there is also the risk of developing the combined side effects of each drug. Thus, identifying a multi-target drug may be the best strategy to improve treatment efficacy. This study sees the potential of 2657 Filipino phytochemicals as a source of natural inhibitors against four targets of diabetes: PTP1B, DPP-4, SGLT-2, and FBPase. Different computer-aided drug discovery techniques, including ADMET profiling, DFT optimization, molecular docking, MD simulations, and MM/PBSA energy calculations, were employed to elucidate the stability and determine the binding affinity of the candidate ligands. Through in silico methods, we have identified seven potential natural inhibitors against PTP1B, DPP-4, and FBPase, and ten against SGLT-2. Eight plants containing at least one natural inhibitor of each protein target were also identified. It is recommended to further investigate the plants' potential to be transformed into a safe and scientifically validated multi-target drug for diabetes therapies.

A cheminformatics-biophysics correlate to identify promising lead molecules against matrix metalloproteinase-2 (MMP-2) enzyme: A promising anti-cancer target

Matrix metalloproteinase-2 (MMP-2) is an endopeptidase enzyme that is devoted to extracellular matrix proteins degradation. The enzyme is warranted as promising drugs target for different light threating diseases such as arthritis, cancer and fibrosis. Herein, in this study, three drug molecules: CMNPD8322, CMNPD8320, and CMNPD8318 were filtered as high affinity binding compounds with binding energy score of -9.75 kcal/mol, -9.11 kcal/mol, -9.05 kcal/mol, respectively. The control binding energy score was -9.01 kcal/mol. The compounds docked deeply inside the pocket interacting with S1 pocket residues. The docked complexes dynamics in real time at cellular environment was then done to decipher the stable binding conformation and intermolecular interactions network. The compounds complexes achieved very stable dynamics with root mean square deviation (RMSD) with mean value of around 2-3 Å compared to control complex that showed higher fluctuations of 5 Å. The simulation trajectories frames based binding free energy demonstrated all the compounds-MMP-2 complexes reported highly stable energy, particularly the van der Waals energy dominate the overall net energy. Similarly, the complexes revalidation of WaterSwap based energies also disclosed the complexes highly stable in term docked conformation. Also, the compounds illustrated the compounds favorable pharmacokinetics and were non-toxic and non-mutagenic. Thus, the compounds might be used thorough experimental assays to confirm compounds selective biological potency against MMP-2 enzyme.

Hecogenin a Plant Derived Small Molecule as an Antagonist to BACE-1: A Potential Target for Neurodegenerative Disorders

The field of drug discovery has recognized the significance of computer-aided drug design. Recent advancements in structure identification and characterization, bio-computational science and molecular biology have significantly contributed to the development of novel treatments for various diseases. Alzheimer's disease is prevalent in over 50 million affected people, with the pathological condition of amyloidal plaque formation by the beta-amyloidal peptide that results in lesions of the patient's brain, thus making the target prediction and treatment a hurdle. In this study, we evaluated the potential of 54 bioactive compounds from Justicia adhatoda L. and Sida cordifolia L. identified through LC-MS/MS against the β-site amyloid precursor cleaving enzyme (beta-secretase) that results in the formation of amyloidal plaques. To study the drug-likeness of the phytocompounds, Lipinski's rule of five for ADME profiling and toxicity prediction was performed. Molecular docking was performed using auto-dock tool of PyRx software; molecular dynamic simulations were performed using the Schrodinger suite. Molecular docking against BACE-1 protein revealed that hecogenin, identified from S. cordifolia has a broad spectrum of pharmacological applications and a binding affinity score of -11.3 kcal/Mol. The Hecogenin-BACE-1 protein complex was found to be stable after 30 ns of MD simulation, resulting in its substantial stability. Further studies focusing on the in vivo neuroprotective activity of hecogenin against the disease will pave the way for efficient drug discovery from natural sources in a precise manner.

GDockScore: a graph-based protein-protein docking scoring function

Summary

Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.

Availability and implementation

The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Novel Potential Janus Kinase Inhibitors with Therapeutic Prospects in Rheumatoid Arthritis Addressed by In Silico Studies

Rheumatoid arthritis (RA) is a debilitating autoimmune disorder with an inflammatory condition targeting the joints that affects millions of patients worldwide. Several unmet needs still need to be addressed despite recent improvements in the management of RA. Although current RA therapies can diminish inflammation and alleviate symptoms, many patients remain unresponsive or experience flare-ups of their ailment. The present study aims to address these unmet needs through in silico research, with a focus on the identification of novel, potentially active molecules. Therefore, a molecular docking analysis has been conducted using AutoDockTools 1.5.7 on Janus kinase (JAK) inhibitors that are either approved for RA or in advanced phases of research. The binding affinities of these small molecules against JAK1, JAK2, and JAK3, which are target proteins implicated in the pathophysiology of RA, have been assessed. Subsequent to identifying the ligands with the highest affinity for these target proteins, a ligand-based virtual screening was performed utilizing SwissSimilarity, starting with the chemical structures of the previously identified small molecules. ZINC252492504 had the highest binding affinity (-9.0 kcal/mol) for JAK1, followed by ZINC72147089 (-8.6 kcal/mol) for JAK2, and ZINC72135158 (-8.6 kcal/mol) for JAK3. Using SwissADME, an in silico pharmacokinetic evaluation showed that oral administration of the three small molecules may be feasible. Based on the preliminary results of the present study, additional extensive research is required for the most promising candidates to be conducted so their efficacy and safety profiles can be thoroughly characterized, and they can become medium- and long-term pharmacotherapeutic solutions for the treatment of RA.

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.

Insights from the molecular docking analysis of gambogic acid with the Chikungunya spike glycoprotein E2

Chikungunya fever is a mosquito-borne disease caused by the chikungunya virus (CHIKV) and has drawn substantial attention in recent years. So far, no effective treatment is available in the form of drugs or vaccines. In this study, we aimed to screen some drugs against the pathogenic Chikungunya virus through a molecular docking approach. As a fact, the spike E2 protein plays an important role in viral attachment to the human host cell, binding to a cell receptor MXRA8. The molecules screened for in-silico interaction against MXRA8 were selected with top hit based on binding affinity. The existing intermolecular bonds were investigated further in the active site of the protein that interacts with the top-hit ligands. Gambogic acid (guttic acid) was depicted as the furthermost potential inhibitor when compared to the others it had the lowest binding affinity (-10.9 kcal/mol). Gambogic acid, as a potential antiviral agent against the spike E2 protein, could be a promising candidate.

Reverse vaccinology approaches to introduce promising immunogenic and drug targets against antibiotic-resistant Neisseria gonorrhoeae: Thinking outside the box in current prevention and treatment

Gonorrhea is an urgent antimicrobial resistance threat and its therapeutic options are continuously getting restricted. Moreover, no vaccine has been approved against it so far. Hence, the present study aimed to introduce novel immunogenic and drug targets against antibiotic-resistant Neisseria gonorrhoeae strains. In the first step, the core proteins of 79 complete genomes of N. gonorrhoeae were retrieved. Next, the surface-exposed proteins were evaluated from different aspects such as antigenicity, allergenicity, conservancy, and B-cell and T-cell epitopes to introduce promising immunogenic candidates. Then, the interactions with human Toll-like receptors (TLR-1, 2, and 4), and immunoreactivity to elicit humoral and cellular immune responses were simulated. On the other hand, to identify novel broad-spectrum drug targets, the cytoplasmic and essential proteins were detected. Then, the N. gonorrhoeae metabolome-specific proteins were compared to the drug targets of the DrugBank, and novel drug targets were retrieved. Finally, the protein data bank (PDB) file availability and prevalence among the ESKAPE group and common sexually transmitted infection (STI) agents were assessed. Our analyses resulted in the recognition of ten novel and putative immunogenic targets including murein transglycosylase A, PBP1A, Opa, NlpD, Azurin, MtrE, RmpM, LptD, NspA, and TamA. Moreover, four potential and broad-spectrum drug targets were identified including UMP kinase, GlyQ, HU family DNA-binding protein, and IF-1. Some of the shortlisted immunogenic and drug targets have confirmed roles in adhesion, immune evasion, and antibiotic resistance that can induce bactericidal antibodies. Other immunogenic and drug targets might be associated with the virulence of N. gonorrhoeae as well. Thus, further experimental studies and site-directed mutations are recommended to investigate the role of potential vaccine and drug targets in the pathogenesis of N. gonorrhoeae.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the μ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Molecular docking analysis of KRAS inhibitors for cancer management

The majority of human tumors are characterized by abnormal signaling caused by oncogenic RAS proteins. KRAS is a member of the RAS family and is currently one of the most thoroughly researched targets for cancer treatment due to its prevalence in a variety of deadly malignancies. Targeting the KRAS protein, which plays a crucial role in regulating cell growth, differentiation, and apoptosis, shows great potential as a strategy for fighting cancer. Herein, in silico screening of 530 natural compounds against KRAS protein was performed. The top-scoring hits, namely ZINC32502206, ZINC98363763, ZINC85645815, and ZINC98364259 displayed a robust affinity towards KRAS as evidenced by their respective binding affinity values of -10.50, -10.01, -9.80, and -9.70 kcal/mol, respectively which were notably higher than that of the control compound AMG 510 (-9.10 kcal/mol). Through virtual screening and visual inspection, it was observed that these hits effectively interacted with the essential residues located within the active site of KRAS. Based on the findings of this study, it can be inferred that these compounds may have the potential to be employed in the treatment of cancer by targeting KRAS.

Targeting the PEDV 3CL protease for identification of small molecule inhibitors: an insight from virtual screening, ADMET prediction, molecular dynamics, free energy landscape, and binding energy calculations

BACKGROUND

The porcine epidemic diarrhea virus (PEDV) represents a major health issue for piglets worldwide and does significant damage to the pork industry. Thus, new therapeutic approaches are urgently needed to manage PEDV infections. Due to the current lack of a reliable remedy, this present study aims to identify novel compounds that inhibit the 3CL protease of the virus involved in replication and pathogenesis.

RESULTS

To identify potent antiviral compounds against the 3CL protease, a virtual screening of natural compounds (n = 97,999) was conducted. The top 10 compounds were selected based on the lowest binding energy and the protein-ligand interaction analyzed. Further, the top five compounds that demonstrated a strong binding affinity were subjected to drug-likeness analysis using the ADMET prediction, which was followed by molecular dynamics simulations (500 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. Based on these parameters, four putative lead (ZINC38167083, ZINC09517223, ZINC04339983, and ZINC09517238) compounds were identified that represent potentially effective inhibitors of the 3CL protease.

CONCLUSION

Therefore, these can be utilized for the development of novel antiviral drugs against PEDV. However, this requires further validation through in vitro and in vivo studies.

Mutual-DTI: A mutual interaction feature-based neural network for drug-target protein interaction prediction

The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

RNA-binding proteins in cancer drug discovery

RNA-binding proteins (RBPs) are crucial players in tumorigenesis and, hence, promising targets in cancer drug discovery. However, they are largely regarded as 'undruggable', because of the often noncatalytic and complex interactions between protein and RNA, which limit the discovery of specific inhibitors. Nonetheless, over the past 10 years, drug discovery efforts have uncovered RBP inhibitors with clinical relevance, highlighting the disruption of RNA-protein networks as a promising avenue for cancer therapeutics. In this review, we discuss the role of structurally distinct RBPs in cancer, and the mechanisms of RBP-directed small-molecule inhibitors (SMOIs) focusing on drug-protein interactions, binding surfaces, potency, and translational potential. Additionally, we underline the limitations of RBP-targeting drug discovery assays and comment on future trends in the field.

Molecular Docking and Dynamics Simulation Studies Predict Potential Anti-ADAR2 Inhibitors: Implications for the Treatment of Cancer, Neurological, Immunological and Infectious Diseases

Altered RNA editing has been linked to several neurodevelopmental disorders, including autism spectrum disorder (ASD) and intellectual disability, in addition to depression, schizophrenia, some cancers, viral infections and autoimmune disorders. The human ADAR2 is a potential therapeutic target for managing these various disorders due to its crucial role in adenosine to inosine editing. This study applied consensus scoring to rank potential ADAR2 inhibitors after performing molecular docking with AutoDock Vina and Glide (Maestro), using a library of 35,161 compounds obtained from traditional Chinese medicine. A total of 47 compounds were predicted to be good binders of the human ADAR2 and had insignificant toxicity concerns. Molecular dynamics (MD) simulations, including the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) procedure, also emphasized the binding of the shortlisted compounds. The potential compounds had plausible binding free energies ranging from −81.304 to −1068.26 kJ/mol from the MM/PBSA calculations. ZINC000085511995, a naphthoquinone had more negative binding free energy (−1068.26 kJ/mol) than inositol hexakisphosphate (IHP) [−873.873 kJ/mol], an agonist and a strong binder of ADAR2. The potential displacement of IHP by ZINC000085511995 in the IHP binding site of ADAR2 could be explored for possible deactivation of ADAR2. Bayesian-based biological activity prediction corroborates the neuropharmacological, antineoplastic and antiviral activity of the potential lead compounds. All the potential lead compounds, except ZINC000014612330 and ZINC000013462928, were predicted to be inhibitors of various deaminases. The potential lead compounds also had probability of activity (Pa) > 0.442 and probability of inactivity (Pi) < 0.116 values for treating acute neurologic disorders, except for ZINC000085996580 and ZINC000013462928. Pursuing these compounds for their anti-ADAR2 activities holds a promising future, especially against neurological disorders, some cancers and viral infections caused by RNA viruses. Molecular interaction, hydrogen bond and per-residue decomposition analyses predicted Arg400, Arg401, Lys519, Trp687, Glu689, and Lys690 as hot-spot residues in the ADAR2 IHP binding site. Most of the top compounds were observed to have naphthoquinone, indole, furanocoumarin or benzofuran moieties. Serotonin and tryptophan, which are beneficial in digestive regulation, improving sleep cycle and mood, are indole derivatives. These chemical series may have the potential to treat neurological disorders, prion diseases, some cancers, specific viral infections, metabolic disorders and eating disorders through the disruption of ADAR2 pathways. A total of nine potential lead compounds were shortlisted as plausible modulators of ADAR2.

Targeting Toxoplasma gondii ME49 TgAPN2: A Bioinformatics Approach for Antiparasitic Drug Discovery

As fewer therapeutic options are available for treating toxoplasmosis, newer antiparasitic drugs that can block TgAPN2 M1 aminopeptidase are of significant value. Herein, we employed several computer-aided drug-design approaches with the objective of identifying drug molecules from the Asinex library with stable conformation and binding energy scores. By a structure-based virtual screening process, three molecules—LAS_52160953, LAS_51177972, and LAS_52506311—were identified as promising candidates, with binding affinity scores of −8.6 kcal/mol, −8.5 kcal/mol, and −8.3 kcal/mol, respectively. The compounds produced balanced interacting networks of hydrophilic and hydrophobic interactions, vital for holding the compounds at the docked cavity and stable binding conformation. The docked compound complexes with TgAPN2 were further subjected to molecular dynamic simulations that revealed mean RMSD for the LAS_52160953 complex of 1.45 Å), LAS_51177972 complex 1.02 Å, and LAS_52506311 complex 1.087 Å. Another round of binding free energy validation by MM-GBSA/MM-PBSA was done to confirm docking and simulation findings. The analysis predicted average MM-GBSA value of <−36 kcal/mol and <−35 kcal/mol by MM-PBSA. The compounds were further classified as appropriate candidates to be used as drug-like molecules and showed favorable pharmacokinetics. The shortlisted compounds showed promising biological potency against the TgAPN2 enzyme and may be used in experimental validation. They may also serve as parent structures to design novel derivatives with enhanced biological potency.

VSFlow: an open-source ligand-based virtual screening tool

Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported.

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors

Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors. However, proteins from the SLC family play an essential role in various diseases. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). In contrast to most other SLCs, the NET has been relatively well studied. However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. Here, a computational screening pipeline was developed to find new NET inhibitors. The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. There were five hit compounds identified (hit rate 16%) with sub-micromolar inhibitory potencies toward NET, which are promising for follow-up experimental research. This study demonstrates a data-driven approach to diversify known chemical space to identify novel ligands and is to our knowledge the first to select this set based on the sequence similarity of related targets.

Quest for selective MMP9 inhibitors: a computational approach

Matrix Metalloproteinases-9 (MMP-9) is one of the important targets that play a vital role in various diseases such as cancer, Alzheimer's, arthritis, etc. Traditionally, MMP-9 inhibitors have been unable to achieve selectivity to get around this target; thereby, novel mechanisms such as inhibition of activated MMP-9 zymogen (pro-MMP-9) have been discovered. The JNJ0966 was one of the few compounds that attained the requisite selectivity by inhibiting the activation of MMP-9 zymogen (pro-MMP-9). Since JNJ0966, no other small molecules have been identified. Herein, extensive in silico studies were called upon to bolster the prospect of exploring potential candidates. The key objective of this research is to identify the potential hits from the ChEMBL database via molecular docking and dynamics approach. Protein with PDB ID: 5UE4, having a unique inhibitor in an allosteric binding pocket of MMP-9, was chosen for the study. Structure-based virtual screening and MMGBSA binding affinity calculations were performed, and five potential hits were finalized. Detailed analysis of the best-scoring molecules was performed with ADMET analysis and molecular dynamics (MD) simulation. All five hits outperformed JNJ0966 in the docking assessment, ADMET analysis, and molecular dynamics simulation. Accordingly, our research findings imply that these hits can be investigated for in vitro and in vivo studies against proMMP9 and might be explored as potential anticancer drugs. The outcome of our research might contribute in expediting the exploration of drugs that inhibits proMMP-9.Communicated by Ramaswamy H. Sarma.

Virtual screening and multilevel precision-based prioritisation of natural inhibitors targeting the ATPase domain of human DNA topoisomerase II alpha

Human DNA topoisomerase II alpha (hTopIIα) is a classic chemotherapeutic drug target. The existing hTopIIα poisons cause numerous side effects such as the development of cardiotoxicity, secondary malignancies, and multidrug resistance. The use of catalytic inhibitors targeting the ATP-binding cavity of the enzyme is considered a safer alternative due to the less deleterious mechanism of action. Hence, in this study, we carried out high throughput structure-based virtual screening of the NPASS natural product database against the ATPase domain of hTopIIα and identified the five best ligand hits. This was followed by comprehensive validation through molecular dynamics simulations, binding free energy calculation and ADMET analysis. On stringent multilevel prioritization, we identified promising natural product catalytic inhibitors that showed high binding affinity and stability within the ligand-binding cavity and may serve as ideal hits for anticancer drug development.Communicated by Ramaswamy H. Sarma.

In Silico Screening and Molecular Dynamics Simulation Studies in the Identification of Natural Compound Inhibitors Targeting the Human Norovirus RdRp Protein to Fight Gastroenteritis

Norovirus (HNoV) is a leading cause of gastroenteritis globally, and there are currently no treatment options or vaccines available to combat it. RNA-dependent RNA polymerase (RdRp), one of the viral proteins that direct viral replication, is a feasible target for therapeutic development. Despite the discovery of a small number of HNoV RdRp inhibitors, the majority of them have been found to possess a little effect on viral replication, owing to low cell penetrability and drug-likeness. Therefore, antiviral agents that target RdRp are in high demand. For this purpose, we used in silico screening of a library of 473 natural compounds targeting the RdRp active site. The top two compounds, ZINC66112069 and ZINC69481850, were chosen based on their binding energy (BE), physicochemical and drug-likeness properties, and molecular interactions. ZINC66112069 and ZINC69481850 interacted with key residues of RdRp with BEs of -9.7, and -9.4 kcal/mol, respectively, while the positive control had a BE of -9.0 kcal/mol with RdRp. In addition, hits interacted with key residues of RdRp and shared several residues with the PPNDS, the positive control. Furthermore, the docked complexes showed good stability during the molecular dynamic simulation of 100 ns. ZINC66112069 and ZINC69481850 could be proven as potential inhibitors of the HNoV RdRp in future antiviral medication development investigations.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Pan-Genomics of Escherichia albertii for Antibiotic Resistance Profiling in Different Genome Fractions and Natural Product Mediated Intervention: In Silico Approach

Escherichia albertii is an emerging, enteric pathogen of significance. It was first isolated in 2003 from a pediatric diarrheal sample from Bangladesh. In this study, a comprehensive in silico strategy was followed to first list out antibiotic-resistant genes from core, accessory and unique genome fractions of 95 available genomes of E. albertii. Then, 56 drug targets were identified from the core essential genome. Finally, ZipA, an essential cell division protein that stabilizes the FtsZ protofilaments by cross-linking them and serves as a cytoplasmic membrane anchor for the Z ring, was selected for further downstream processing. It was computationally modeled using a threading approach, followed by virtual screening of two phytochemical libraries, Ayurvedic (n = 2103 compounds) and Traditional Chinese Medicine (n = 36,043 compounds). ADMET profiling, followed by PBPK modeling in the central body compartment, in a population of 250 non-diseased, 250 cirrhotic and 250 renally impaired people was attempted. ZINC85624912 from Chinese medicinal library showed the highest bioavailability and plasma retention. This is the first attempt to simulate the fate of natural products in the body through PBPK. Dynamics simulation of 20 ns for the top three compounds from both libraries was also performed to validate the stability of the compounds. The obtained information from the current study could aid wet-lab scientists to work on the scaffold of screened drug-like compounds from natural resources and could be useful in our quest for therapy against antibiotic-resistant E. albertii.

Computational Approaches to the Rational Design of Tubulin-Targeting Agents

Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.

Opportunities and challenges in application of artificial intelligence in pharmacology

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

Structure-based virtual screening to identify potential lipase inhibitors to reduce lipid storage in Wolman disorder

Wolman disorder (WD) was first described in Iranian-Jewish (IJ) children, and it is caused by a deficiency of the lysosomal acid lipase (LAL). Newborns with WD are healthy and active at birth but soon develop severe malnutrition symptoms and often die before 1 year. In particular, spleens, livers, bone marrows, intestines, adrenal glands, and lymph nodes accumulate harmful amounts of lipids. G87V mutation in LIPA is responsible for Wolman disorder. Some reports suggest that δ-tocopherol can reduce lipid accumulation in cholesterol storage disorders. Hence, we used δ-tocopherol for the virtual screening process in this study. Initially, the lead compounds were docked with native and G87V mutant LIPA. Subsequently, the ADME and toxicity parameters for screened compounds were determined to ensure the safety profiles. Finally, the molecular dynamics simulations result indicated that dl-alpha-Tocopherol-13C3, a molecule obtained from the PubChem database, is identified as a potential and stable lead molecule that could be effective against the G87V mutant form of LIPA.

Virtual screening and activity evaluation of human uric acid transporter 1 (hURAT1) inhibitors

Hyperuricemia is a disease caused by disorder of purine metabolism, mainly due to insufficient renal excretion of uric acid. Urate transporter 1 (URAT1) is the most widely studied target of urate transporters, and used for uric acid (UA) reabsorption. This study used the AlphaFold2 algorithm to predict the structure of URAT1. Virtual screening and biological evaluation were used to discover novel URAT1 inhibitors that target the critical amino acids. Seven compounds were screened from the T2220 database and validated as URAT1 inhibitors by cell biology experiments. The IC50 values of benbromarone, NP023335, TN1148, and TN1008 were 6.878, 18.46, 24.64, and 53.04 μM, respectively. Molecular dynamics simulation was used to investigate the binding mechanism of URAT1 to NP023335, which forms stable contact with Ser35, Phe365, and Arg477. These interactions are essential for maintaining the biological activity of NP023335. The three compounds' pharmacokinetic characteristics were predicted, and NP023335's properties matched those of an empirical medication with the benefits of high solubility, low cardiotoxicity, good membrane permeability, and oral absorption. The natural product NP023335 will serve as a promising hit compound for facilitating the further design of novel URAT1 inhibitors.

Improving drug discovery through parallelism

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M^pro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M^pro inhibitors against SARS-CoV-2. The 33.8 kDa M^pro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (M^pro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M^pro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Structural Analysis, Multi-Conformation Virtual Screening and Molecular Simulation to Identify Potential Inhibitors Targeting pS273R Proteases of African Swine Fever Virus

The African Swine Fever virus (ASFV) causes an infectious viral disease in pigs of all ages. The development of antiviral drugs primarily aimed at inhibition of proteases required for the proteolysis of viral polyproteins. In this study, the conformation of the pS273R protease in physiological states were investigated, virtually screened the multi-protein conformation of pS273R target proteins, combined various molecular docking scoring functions, and identified five potential drugs from the Food and Drug Administration drug library that may inhibit pS273R. Subsequent validation of the dynamic interactions of pS273R with the five putative inhibitors was achieved using molecular dynamics simulations and binding free energy calculations using the molecular mechanics/Poison-Boltzmann (Generalized Born) (MM/PB(GB)SA) surface area. These findings demonstrate that the arm domain and Thr159-Lys167 loop region of pS273R are significantly more flexible compared to the core structural domain, and the Thr159-Lys167 loop region can serve as a "gatekeeper" in the substrate channel. Leucovorin, Carboprost, Protirelin, Flavin Mononucleotide, and Lovastatin Acid all have Gibbs binding free energies with pS273R that were less than -20 Kcal/mol according to the MM/PBSA analyses. In contrast to pS273R in the free energy landscape, the inhibitor and drug complexes of pS273R showed distinct structural group distributions. These five drugs may be used as potential inhibitors of pS273R and may serve as future drug candidates for treating ASFV.