Virtual Screening Workflow Research Articles

ChemPrice, a Python Package for Automated Chemical Price Search

AbstractThe open‐access movement in chemistry has led to a surge in publicly accessible data repositories such as PubChem and ChemSpider, housing more than 100 million chemical compounds. These repositories are invaluable for large‐scale virtual screening but lack crucial pricing information, a vital economic aspect influencing commercial decisions. Currently, these databases merely provide hyperlinks to vendor websites for pricing details, necessitating manual exploration due to the diverse structures of vendor websites, making automated data extraction impractical. To tackle this challenge, we introduce ChemPrice, a Python package that streamlines the collection of pricing data through the integration of ChemSpace, Mcule, and Molport platforms that provide up‐to‐date price information from over 100 suppliers. ChemPrice standardizes pricing units, offering consolidated and best‐pricing information for specified compounds. Its modular design facilitates integration into automated virtual screening workflows, and a user‐friendly web interface caters to those with limited coding skills, enabling easy compound list uploading and pricing data retrieval. This innovation addresses the complexity of collecting compound prices and enables integration into the virtual screening code workflows, providing a valuable resource for a wide community of users seeking comprehensive and up‐to‐date pricing information to enhance their decision‐making processes.

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform.

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.

Binding Mechanism of the Active Form of Molnupiravir to RdRp of SARS-CoV-2 and Designing Potential Analogues: Insights from Molecular Dynamics Simulations.

Molnupiravir, an FDA-approved nucleoside prodrug for treating COVID-19, converts into N4-hydroxycytidine triphosphate (NHC-TP), which integrates into SARS-CoV-2 RNA by its RNA-dependent RNA polymerase (RdRp) causing lethal mutations in viral proteins. Due to the risk of RdRp-mediated drug resistance and potential off-target effects on host polymerases (e.g., human polymerase II/HPolII), it is crucial to understand NHC-TP interactions at polymerase active sites for developing new, resistance-proof treatments. In this study, we used molecular dynamics (MD) simulations to probe key interactions between NHC-TP and SARS-CoV-2 RdRp and designed novel NHC-TP analogues with greater selectivity for SARS-CoV-2 RdRp over HPolII by a virtual screening workflow. We docked NHC-TP to a modified SARS-CoV-2 RdRp-Remdesivir triphosphate structure (PDB ID: 7BV2) and generated 71 NHC-TP analogues with bulky substituents to increase the interaction with RdRP and to reduce HPolII incorporation. MD simulations assessed the stability, binding affinity, and site interactions of these analogues. The top 7 candidates, with favorable ADMET properties, likely inhibit replication via potential dual mechanisms (the replicative stalling and the induction of lethal mutations) while maintaining selectivity for SARS-CoV-2 RdRp.

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

HSADab: A comprehensive database for human serum albumin

Human Serum Albumin (HSA), the most abundant protein in human body fluids, plays a crucial role in the transportation, absorption, metabolism, distribution, and excretion of drugs, significantly influencing their therapeutic efficacy. Despite the importance of HSA as a drug target, the available data on its interactions with external agents, such as drug-like molecules and antibodies, are limited, posing challenges for molecular modeling investigations and the development of empirical scoring functions or machine learning predictors for this target. Furthermore, the reported entries in existing databases often contain major inconsistencies due to varied experiments and conditions, raising concerns about data quality. To address these issues, a pioneering database, HSADab, was established through an extensive review of >30,000 scientific publications published between 1987 and 2023. The database encompasses over 5000 affinity data points at multiple temperatures and >130 crystal structures, including both ligand-bound and apo forms. The current HSADab resource (www.hsadab.cn) serves as a reliable foundation for validating molecular simulation protocols, such as traditional virtual screening workflows using docking, end-point, and al-chemical free energy techniques. Additionally, it provides a valuable data source for the implementation of machine learning predictors, including plasma protein binding models and plasma protein-based drug design models.

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus providing a therapeutic target for leukemia treatment. In this study, we introduced a sophisticated virtual screening strategy combined with biological evaluations to discover potent LCK inhibitors. Our initial approach involved utilizing the PLANET algorithm to assess and contrast various scoring methodologies suitable for LCK inhibitor screening. After effectively evaluating PLANET, we progressed to devise a virtual screening workflow that synergistically combines the strengths of PLANET with the capabilities of Schrödinger's suite. This integrative strategy led to the efficient identification of four potential LCK inhibitors. Among them, compound 1232030-35-1 stood out as the most promising candidate with an IC50 of 0.43 nM. Further in vitro bioassays revealed that 1232030-35-1 exhibited robust antiproliferative effects on T-ALL cells, which was attributed to its ability to suppress the phosphorylations of key molecules in the LCK signaling pathway. More importantly, 1232030-35-1 treatment demonstrated profound in vivo antileukemia efficacy in a human T-ALL xenograft model. In addition, complementary molecular dynamics simulations provided deeper insight into the binding kinetics between 1232030-35-1 and LCK, highlighting the formation of a hydrogen bond with Met319. Collectively, our study established a robust and effective screening strategy that integrates AI-driven and conventional methodologies for the identification of LCK inhibitors, positioning 1232030-35-1 as a highly promising and novel drug-like candidate for potential applications in treating T-ALL.

Screening approach by a combination of computational and in vitro experiments: identification of fluvastatin sodium as a potential SIRT2 inhibitor.

Sirtuins (SIRTs) are NAD+-dependent deacetylases that play various roles in numerous pathophysiological processes, holding promise as therapeutic targets worthy of further investigation. Among them, the SIRT2 subtype is closely associated with tumorigenesis and malignancies. Dysregulation of SIRT2 activation can regulate the expression levels of related genes in cancer cells, leading to tumor occurrence and metastasis. In this study, we used computer simulations to screen for novel SIRT2 inhibitors from the FDA database, based on which 10 compounds with high docking scores and good interactions were selected for in vitro anti-pancreatic cancer metastasis testing and enzyme binding inhibition experiments. The results showed that fluvastatin sodium may possess inhibitory activity against SIRT2. Subsequently, fluvastatin sodium was subjected to molecular docking experiments with various SIRT isoforms, and the combined results from Western blotting experiments indicated its potential as a SIRT2 inhibitor. Next, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were performed, revealing the binding mode of fluvastatin sodium at the SIRT2 active site, further validating the stability and interaction of the ligand-protein complex under physiological conditions. Overall, this study provides a systematic virtual screening workflow for the discovery of SIRT2 activity inhibitors, identifies the potential inhibitory effect of fluvastatin sodium as a lead compound on SIRT2, and opens up a new direction for developing highly active and selectively targeted SIRT2 inhibitors.

Molecular docking, molecular dynamics simulations and binding free energy studies of interactions between Mycobacterium tuberculosis Pks13, PknG and bioactive constituents of extremophilic bacteria

Mycobacterial pathogens present a significant challenge to disease control efforts globally due to their inherent resistance to multiple antibiotics. The rise of drug-resistant strains of Mycobacterium tuberculosis has prompted an urgent need for innovative therapeutic solutions. One promising way to discover new tuberculosis drugs is by utilizing natural products from the vast biochemical space. Multidisciplinary methods can used to harness the bioactivity of these natural products. This study aimed to evaluate the antimycobacterial efficacy of functional crude extracts from bacteria isolated from gold mine tailings in South Africa. Bacterial strains were identified using 16S rRNA sequencing. The crude extracts obtained from the bacteria were tested against Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis mc2155, and Mycobacterium aurum A+. Untargeted HPLC-qTOF and molecular networking were used to identify the functional constituents present in extracts that exhibited inhibitory activity. A virtual screening workflow (VSW) was used to filter compounds that were strong binders to Mycobacterium tuberculosis Pks13 and PknG. The ligands returned from the VSW were subjected to optimization using density functional theory (DFT) at M06-2X/6-311++ (d,p) level of theory and basis set implemented in Gaussian16 Rev.C01. The optimized ligands were re-docked against Mycobacterium tuberculosis Pks13 and PknG. Molecular dynamics simulation and molecular mechanics generalized born surface area were used to evaluate the stability of the protein–ligand complexes formed by the identified hits. The hit that showed promising binding characteristics was virtually modified through multiple synthetic routes using reaction-driven enumeration. Three bacterial isolates showed significant activity against the two strains of Mycobacterium, while only two, Bacillus subtilis and Bacillus licheniformis, exhibited activity against both Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis mc2155, and Mycobacterium aurum A+. The tentatively identified compounds from the bacterial crude extracts belonged to various classes of natural compounds associated with antimicrobial activity. Two compounds, cyclo-(L-Pro-4-OH-L-Leu) and vazabitide A, showed strong binding against PknG and Pks13, with pre-MD MM-GBSA values of − 42.8 kcal/mol and − 47.6 kcal/mol, respectively. The DFT-optimized compounds exhibited the same docking scores as the ligands optimized using the OPSL-4 force field. After modifying vazabitide A, its affinity to the Pks13 binding site increased to − 85.8 kcal/mol, as revealed by the post-MD MM-GBSA analysis. This study highlights the potential of bacteria isolates from gold mine tailings as a source of new scaffolds for designing and optimizing anti-Mycobacterium agents. These agents synthesized in-silico can be further tested in-vitro to evaluate their efficacy.

Integrated Virtual Screening and Validation toward Potential HPPD Inhibition Herbicide.

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is one of the most widely studied herbicide targets and has gained significant attention. To identify potential effective HPPD inhibitors, a rational multistep virtual screening workflow was built, which included CBP models (based on the receptor-ligand interactions in the crystal complex), Hypogen models with activity prediction ability (according to the derivation of structure-activity relationships from a set of molecules with reported activity values), and a consensus docking procedure (consisting of LibDock, Glide, and CDOCKER). About 1 million molecules containing diketone or β-keto-enol substructures were filtered by Lipinski's rules, CBP model, and Hypogen model. A total of 12 compounds with similar docking postures were generated by consensus docking. Eventually, four molecules were screened based on the specific binding pattern and affinity of the HPPD inhibitor. The biological evaluation in vivo displayed that compounds III-1 and III-2 exhibited comparable herbicidal activity to isoxaflutole and possessed superior safety on various crops (wheat, rice, sorghum, and maize). The ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity) showed that compound III possessed relatively good toxicological results. This work provides a theoretical basis and valuable reference for the virtual screening and molecular design of novel HPPD inhibition herbicides.

3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Alzheimer’s disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD. Communicated by Ramaswamy H. Sarma

Identification and assessment of new PIM2 inhibitors for treating hematologic cancers: A combined approach of energy-based virtual screening and machine learning evaluation.

PIM2, part of the PIM kinase family along with PIM1 and PIM3, is often overexpressed in hematologic cancers, fueling tumor growth. Despite its significance, there are no approved drugs targeting it. In response to this challenge, we devised a thorough virtual screening workflow for discovering novel PIM2 inhibitors. Our process includes molecular docking and diverse scoring methods likemolecular mechanics generalized born surface area, XGBOOST, and DeepDock to rank potential inhibitors by binding affinities and interaction potential. Ten compounds were selected and subjected to an adequate evaluation of their biological activity. Compound 2 emerged as the most potent inhibitor with an IC50 of approximately 135.7 nM. It also displayed significant activity against various hematological cancers, including acute myeloid leukemia, mantle cell lymphoma, and anaplastic large cell lymphoma (ALCL). Molecular dynamics simulations elucidated the binding mode of compound 2 with PIM2, offering insights for drug development. These results highlight the reliability and efficacy of our virtual screening workflow, promising new drugs for hematologic cancers, notably ALCL.

Pinpointing top inhibitors for GSK3β from pool of indirubin derivatives using rigorous computational workflow and their validation using molecular dynamics (MD) simulations

Glycogen synthase kinase-3β (GSK3β) is a pivotal protein kinase implicated in a spectrum of debilitating diseases, encompassing cancer, diabetes, and neurodegenerative disorders. While the therapeutic potential of GSK3β inhibition is widely recognized, there remains an unmet need for a rigorous, systematic analysis probing the theoretical inhibition dynamics of a comprehensive library of indirubin derivatives against GSK3β using advanced computational methodologies. Addressing this gap, this study embarked on an ambitious endeavor, leveraging indirubin—a renowned scaffold—as a template to curate a vast library of 1000 indirubin derivatives from PubChem. These were enriched with varied substitutions and modifications, identified via a structure similarity search with a Tanimoto similarity threshold of 85%. Harnessing a robust virtual screening workflow, we meticulously identified the top 10 contenders based on XP docking scores. Delving deeper, we gauged the binding free energy differentials (ΔGBind) of these hits, spotlighting the top three compounds that showcased unparalleled binding prowess. A comparative pharmacophore feature mapping with the reference inhibitor OH8, co-crystallized with GSK3β (PDB ID: 6Y9R), was undertaken. The binding dynamics of these elite compounds were further corroborated with 100 ns molecular dynamics simulations, underlining their stable and potent interactions with GSK3β. Remarkably, our findings unveil that these indirubin derivatives not only match but, in certain scenarios, surpass the binding affinity and specificity of OH8. By bridging this research chasm, our study amplifies the therapeutic promise of indirubin derivatives, positioning them as frontrunners in the quest for groundbreaking GSK3β inhibitors, potentially revolutionizing treatments for a myriad of ailments.

A computational strategy for systematic virtual screening of plasmodium falciparum heme detoxification protein inhibitors from the Drugbank database

Antimalarial drug resistance poses one of the greatest threats to malaria treatment, resulting in increased morbidity and mortality. Heme Detoxification Protein (HDP) is among the essential hemoglobinases of P. falciparum (Pf), a vital molecular target for the treatment of malaria. In this study, we utilized the virtual screening workflow tool of the Schrodinger suite to find the best hits for the PfHDP from the DrugBank library. A total of 14,942 compounds were identified against the PfHDP. The top compounds with the highest docking scores and least energy scores were subjected to molecular simulations for 500 nanosecond to check the stability of the protein-drug complexes. The top three DrugBank compounds were found to be stable over 500 ns, namely DB09298 (silibinin), DB07426 (1-Hydroxy-2-(1,1':3',1''-Terphenyl-3-Yloxy) Ethane-1,1-Diyl] Bis (Phosphonic Acid), and DB07410 [(2-(3-Dibenzofuran-4-yl-Phenyl)-1-Hydroxy-1-Phosphono-Ethyl]-Phosphonic Acid). Overall analysis suggests that the top three compounds, DB09298, DB07426, and DB07410, have good stability for 500 ns. Their scaffolds can be used to design and develop new analogs of the target HDP protein. Silibinin, the anti-cancer drug, was found to be highly stable for the entire simulation period as compared to the other compounds. DB07426 shows its therapeutic effect on bones, especially in the treatment of osteoporosis, and DB07410 has anti-tumor, antibacterial, anti-oxidative, and anti-viral activities. All three compounds can be considered for repurposing as antimalarial drugs to evaluate the binding capacity or inhibition potential of these compounds. Further in-vivo and in-vitro analysis against the PfHDP protein should be conducted. Communicated by Ramaswamy H. Sarma

Noncovalent SARS-COV-2 main protease inhibitors: A virtual screening and molecular dynamic simulation study

The main severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease (Mpro, also known as 3CLpro) plays a central role in virus replication, thereby constituting an appealing therapeutic target for coronavirus disease 2019 (COVID-19) treatment. In this study, we used recently reported crystal structures of SARS-CoV-2 Mpro complexed with small-molecule inhibitors. Using the established structure–activity relationships of small-molecule inhibitors of the Mpro of SARS-CoV-2, we used the Virtual Screening Workflow (with hydrogen bond constraints) implemented in Schrödinger Suite 2021 to screen approximately one million compounds in the ChemDiv compound library to identify potential novel noncovalent Mpro inhibitors. Our docking analysis yielded 65 promising lead compounds, the inhibitory activity of which against Mpro was assessed using a fluorescent SARS-CoV-2 Mpro inhibition assay. Notably, six compounds exhibited SARS-CoV-2 Mpro inhibition with IC50 values ranging 22.95–64.63 μM. These hit compounds exhibited good structural diversity, which prompted us to investigate their SARS-CoV-2 Mpro binding modes. We used a previously validated IFD-BPMD-MD workflow to predict the binding positions of these hit compounds. Our simulations showed that these hit complexes exhibited diverse and robust binding to the SARS-CoV-2 Mpro. Our study findings offer valuable insights into the development of novel noncovalent small-molecule protease inhibitor.

Design and synthesis of novel 8-(azaindolyl)-benzoazepinones as potent and selective ROCK inhibitors.

We report the characterization of potent and selective ROCK inhibitors identified through a core-hopping strategy. A virtual screening workflow, combining ligand- and structure-based methods, was applied on a known series of ROCK inhibitors bearing an acetamido-thiazole scaffold. The most promising replacement of the central core was represented by a benzoazepinone ring, which was selected as a starting point for a subsequent chemical exploration. The overall design approach exploited previous SARs available for congeneric series and crystallographic information to optimize the hinge-binding group as well as the terminal aromatic moiety interacting with the glycine-rich loop. The introduction of elongated and flexible charged groups led to compound 15, which exhibited sub-nanomolar potencies in biochemical and cellular assays, as well as a remarkable selectivity over PKA. HDX studies not only supported the postulated binding mode of compound 15 but also confirmed the crucial role of specific ROCK peptide segments in driving ligand selectivity.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Discovery of potential WEE1 inhibitors via hybrid virtual screening.

G2/M cell cycle checkpoint protein WEE1 kinase is a promising target for inhibiting tumor growth. Although various WEE1 inhibitors have entered clinical investigations, their therapeutic efficacy and safety profile remain unsatisfactory. In this study, we employed a comprehensive virtual screening workflow, which included Schrödinger-Glide molecular docking at different precision levels, as well as the utilization of tools such as MM/GBSA and Deepdock to predict the binding affinity between targets and ligands, in order to identify potential WEE1 inhibitors. Out of ten molecules screened, 50% of these molecules exhibited strong inhibitory activity against WEE1. Among them, compounds 4 and 5 showed excellent inhibitory activity with IC50 values of 1.069 and 3.77nM respectively, which was comparable to AZD1775. Further investigations revealed that compound 4 displayed significant anti-proliferative effects in A549, PC9, and HuH-7 cells and could also induce apoptosis and G1 phase arrest in PC9 cells. Additionally, molecular dynamics simulations unveiled the binding details of compound 4 with WEE1, notably the crucial hydrogen bond interactions formed with Cys379. In summary, this comprehensive virtual screening workflow, combined with in vitro testing and computational modeling, holds significant importance in the development of promising WEE1 inhibitors.

Discovery of pyrimidoindol and benzylpyrrolyl inhibitors targeting SARS-CoV-2 main protease (Mpro) through pharmacophore modelling, covalent docking, and biological evaluation

The main protease (Mpro) enzyme has an imperative function in disease progression and the life cycle of the SARS-CoV-2 virus. Although the orally active drug nirmatrelvir (co-administered with ritonavir as paxlovid) has been approved for emergency use as the frontline antiviral agent, there are a number of limitations that necessitate the discovery of new drug scaffolds, such as poor pharmacokinetics and susceptibility to proteolytic degradation due to its peptidomimetic nature. This study utilized a novel virtual screening workflow that combines pharmacophore modelling, multiple-receptor covalent docking, and biological evaluation in order to find new Mpro inhibitors. After filtering and analysing ∼66,000 ligands from three different electrophilic libraries, 29 compounds were shortlisted for experimental testing, and two of them exhibited ≥20% inhibition at 100 μM. Our top candidate, GF04, is a benzylpyrrolyl compound that exhibited the highest inhibition activity of 38.3%, with a relatively small size (<350 Da) and leadlike character. Interestingly, our approach also identified another hit, DR07, a pyrimidoindol with a non-peptide character, and a molecular weight of 438.9 Da, reporting an inhibition of 26.3%. The established approach detailed in this study, in conjunction with the discovered inhibitors, has the capacity to yield novel perspectives for devising covalent inhibitors targeting the COVID-19 Mpro enzyme and other comparable targets.

Discovery of Small‐Molecule Inhibitors Targeting Progranulin‐Sortilin: An in‐silico Approach Using Molecular Docking, Molecular Dynamics Simulations, and DFT Studies

AbstractIntroduction: Reduction in progranulin (PGRN) have been associated with various neurodegenerative diseases. PGRN binds with high affinity to sortilin (SORT), a membrane transporter, resulting in its cellular uptake and eventual degradation in the lysosome. Inhibition of the SORT‐PGRN interaction has the potential to increase PGRN levels up to 2.5‐fold. Methodology: A virtual screening of curated CNS library of &gt;47 K ligands was done with sortilin receptor (6X3L) through virtual screening workflow in Schrodinger suite. Co‐crystallised ligand was used as a positive control. Docking was done through HTVS, then SP and finally XP model followed by binding free energy calculations (MMGBSA). Based on the result analysis of molecular docking, binding free energy and interactions, docked complexes were chosen for molecular dynamics (MD) studies. DFT studies and MEP plotting were carried out for the screened ligands. Drug likeliness and ADMET studies were also carried out. Results: The virtual screening workflow yielded 139 ligands. Two test ligands and a control were selected and further evaluated through molecular dynamics studies. Both the test ligands (1625 &amp; 127) had comparative docking score (−5.96 &amp; −6.46 kcal/mol) as that of control ligand (−6.21 kcal/mol respectively) and but better binding free energy (−54.66, −53.12 &amp; −43.21 kcal/mol respectively). MD simulations confirmed the docking results for all the three ligands where our test ligand 1625 reached equilibrium quickly as compared to the rest. Our test compounds also showed favourable characteristics of a CNS acting drug and favourable ADMET properties. According to DFT studies, the test ligands 127 and 1625 had FMO (HOMO‐LUMO) energy gaps of 8.86 eV and 9.83 eV, respectively, that were lesser than the control ligand (9.89 eV). Additionally, FMO energy gap of the test ligands and the control ligand were in agreement with the results of docking. MEP diagram provided the picture of electron rich, neutral and electron deficient surface of the test ligands and that of the control with respective color code red, green and blue. Conclusion: Our study results showed a promising CNS specific ligand as an inhibitor of PRGN‐SORT interactions and has a potential to be developed as a drug through in‐vitro and in‐vivo studies.

Expanding the therapeutic arsenal against cancer: a computational investigation of hybrid xanthone derivatives as selective Topoisomerase 2α ATPase inhibitors

The DNA topoisomerase II (topo II) enzyme plays an important role in the replication, recombination, and repair of DNA. Despite their widespread applications in cancer therapy, new, selective, and potent topo II inhibitors with better pharmaceutical profiles are needed to handle drug resistance and severe adverse effects. In this respect, an array of 36 new anticancer compounds was designed based on a Xanthone core tethered to multifunctional Pyridine-amines and Imidazole scaffold via alkyl chain linkers. An integrated in silico approach was used to understand the structural basis and mechanism of inhibition of the hybrid xanthone derivatives. In this study, we established an initial virtual screening workflow based on pharmacophore mapping, docking, and cancer target association to validate the target selection process. Next, a simulation-based docking was conducted along with pharmacokinetic analysis to filter out the five best compounds (7, 10, 25, 27, and 30) having binding energies within the range of −60.45 to −40.97 kcal/mol. The screened compounds were further subjected to molecular dynamics simulation for 200 ns followed by MM-GBSA and ligand properties analysis to assess the stability and binding affinity to hTOP2α. The top-ranking hits 3,7-bis(3-(2-aminopyridin-3-ylhydroxy)propoxy)-1-hydroxy-9H-xanthen-9-one (ligand 7) and 3,8-bis(3-(2-aminopyridin-3-ylhydroxy)propoxy)-1-hydroxy-9H-xanthen-9-one (ligand 25) were found to have no toxicity, optimum pharmacokinetic and, DFT properties and stable intermolecular interactions with the active site of hTopo IIα protein. In conclusion, further in vitro and in vivo experimental validation of the identified lead molecules is warranted for the discovery of new human Topoisomerase 2 alpha inhibitors. Communicated by Ramaswamy H. Sarma