Ligand-based Virtual Screening Research Articles

Multi-target Phenylpropanoids Against Epilepsy.

Epilepsy is a neurological disease with no defined cause, characterized by recurrent epileptic seizures. These occur due to the dysregulation of excitatory and inhibitory neurotransmitters in the central nervous system (CNS). Psychopharmaceuticals have undesirable side effects; many patients require more than one pharmacotherapy to control crises. With this in mind, this work emphasizes the discovery of new substances from natural products that can combat epileptic seizures. Using in silico techniques, this review aims to evaluate the antiepileptic and multi-target activity of phenylpropanoid derivatives. Initially, ligand-based virtual screening models (LBVS) were performed with 468 phenylpropanoid compounds to predict biological activities. The LBVS were developed for the targets alpha- amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), voltage-gated calcium channel Ttype (CaV), gamma-aminobutyric acid A (GABAA), gamma-aminobutyric acid transporter type 1 (GAT-1), voltage-gated potassium channel of the Q family (KCNQ), voltage-gated sodium channel (NaV), and N-methyl D-aspartate (NMDA). The compounds that had good results in the LBVS were analyzed for the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, and later, the best molecules were evaluated in the molecular docking consensus. The TR430 compound showed the best results in pharmacokinetic parameters; its oral absorption was 99.03%, it did not violate any Lipinski rule, it showed good bioavailability, and no cytotoxicity was observed either from the molecule or from the metabolites in the evaluated parameters. TR430 was able to bind with GABAA (activation) and AMPA (inhibition) targets and demonstrated good binding energy and significant interactions with both targets. The studied compound showed to be a promising molecule with a possible multi-target activity in both fundamental pharmacological targets for the treatment of epilepsy.

Chemical Space for Peptide-based Antimicrobials.

Multidrug-resistant (MDR) bacteria represent a global public health threat, and antimicrobial peptides (AMPs), derived from naturally occurring linear or cyclic peptides, can provide the solution. However, most AMPs are sensitive to proteases and have poor pharmacokinetics. The EU-funded ERC Advanced Grant SPACE4AMPS aims to identify new AMPs by applying the concepts of chemical space and ligand-based virtual screening, which are well known for small molecule drug discovery, to the world of peptides. We create virtual libraries of peptides and related molecules and use these approaches to select a few tens of compounds for synthesis and detailed. evaluation of antibacterial, toxicity and stability effects. Recent results and prospects of this ongoing project are presented in this review.

Application of computer-aided drug design in drug discovery and development: Updating knowledge

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.

In silico identification and virtual screening to discover potent therapeutic phytochemicals against CMT2A

Charcot-Marie-Tooth (CMT2A) neuropathies are a set of monogenic diseases that affect the peripheral nervous system. The pathogenesis of CMT2A, a disease caused by genetic mutations, is linked to impaired mitochondrial dynamics and axonal biology. Therapeutic options are still limited, with only a few drugs and other authorized or underdeveloped approaches. A ligand-based virtual screening methodology was used to identify the potential MFN promoters. The natural compound subset of the ZINC database (n = 559600) was obtained and filtered using a ligand-based virtual screening technique. The top 200 compounds were identified to have more than four features that matched the target compound. Pyrx software was used to analyze the molecular docking. Based on the number and type of important binding interactions and docking results, we selected top-20 compounds with the best binding affinities for the targeted protein. 3D-QSAR analyses were performed on potential ligands identified through molecular docking analyses to predict biological activity. The pEC50 and docking scores were used to identify the potential drugs. The ADMET analysis was used to assess the kinetic characteristics of the top two drugs. According to molecular dynamics simulation, the top compound ZINC000005313168 showed high conformational stability. Based on docking results and MD findings, the best in silico hits among the compounds investigated was ZINC000005313168. These findings suggest that the compound ZINC000005313168 could be used to treat CMT2A disease. Further in vivo and in vitro studies are required to consider the present analyses.

Targeting Hypoglycemic Natural Products from the Cloud Forest Plants Using Chemotaxonomic Computer-Assisted Selection.

The cloud forest (CF), a hugely biodiverse ecosystem, is a hotspot of unexplored plants with potential for discovering pharmacologically active compounds. Without sufficient ethnopharmacological information, developing strategies for rationally selecting plants for experimental studies is crucial. With this goal, a CF metabolites library was created, and a ligand-based virtual screening was conducted to identify molecules with potential hypoglycemic activity. From the most promising botanical families, plants were collected, methanolic extracts were prepared, and hypoglycemic activity was evaluated through in vitro enzyme inhibition assays on α-amylase, α-glucosidase, and dipeptidyl peptidase IV (DPP-IV). Metabolomic analyses were performed to identify the dominant metabolites in the species with the best inhibitory activity profile, and their affinity for the molecular targets was evaluated using ensemble molecular docking. This strategy led to the identification of twelve plants (in four botanical families) with hypoglycemic activity. Sida rhombifolia (Malvaceae) stood out for its DPP-IV selective inhibition versus S. glabra. A comparison of chemical profiles led to the annotation of twenty-seven metabolites over-accumulated in S. rhombifolia compared to S. glabra, among which acanthoside D and cis-tiliroside were noteworthy for their potential selective inhibition due to their specific intermolecular interactions with relevant amino acids of DPP-IV. The workflow used in this study presents a novel targeting strategy for identifying novel bioactive natural sources, which can complement the conventional selection criteria used in Natural Product Chemistry.

Navigating a 1E+60 Chemical Space of Peptide/Peptoid Oligomers.

Herein we report a virtual library of 1E+60 members, a common estimate for the size of the drug-like chemical space. The library consists of linear or cyclic oligomers forming molecules within the size range of peptide drugs. We demonstrate ligand-based virtual screening using a genetic algorithm.

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations.

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Ligand-based pharmacophore modeling, virtual screening, and molecular dynamics simulations of Pfhsp90 fingerprints in Plasmodium malaria treatment

The World Health Organization (WHO) documents malaria as one of the leading causes of high morbidity and mortality worldwide. The disease affects millions and kills thousands of people annually. Efforts to reduce the global burden of malaria have prompted the WHO to recommend prevention strategies such as using antimalarial drugs. However, these strategies have been ineffective because of antimalarial drug resistance. The only efficacious malaria treatment is Artemisinin-based Combination Therapy (ACT). However, the extended ACT clearance times, linked to the emergence of artemisinin monotherapy resistance recorded most recently in Africa and the Great Mekong region, pose a danger to its efficacy. Therefore, better efficacious antimalarial drugs are required. Since P. falciparum heat shock protein 90 (PfHsp90) is a well-characterized malaria drug target, this study uses it to discover more efficacious antimalarial drugs. An in silico approach was used to discover PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria using a molecular dynamics simulation (MDS) and hierarchical virtual screening. Geldanamycin (GDM), a well-known anti-PfHsp90 compound, was used to identify PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria by screening it against the ZINC20 database via the ZINCPHARMER web server. This virtual screening process resulted in 17 hits. These ZINCPHARMER hits were subjected to drug-likeness and pharmacokinetics properties analysis in the SwissADME web server, and nine of them satisfied the requirements. The nine ZINC20 compounds were docked with PfHsp90 using the PyRx software version 0.8 to understand their interactions. From the molecular docking results, ZINC09060002 (−8.2 kcal/mol), ZINC72133064 (−7.8 kcal/mol), ZINC72163401 (−7.7 kcal/mol), ZINC72358537 (−8.1 kcal/mol), and ZINC72358557 (−7.6 kcal/mol) had better binding affinities to PfHsp90 than GDM (−7.5 kcal/mol). The stability of these molecularly docked protein–inhibitor complexes was assessed through MDS using GROMACS 2022. ZINC72163401, ZINC72358537, and ZINC72358557 formed stable complexes with PfHsp90. The lead compounds were subjected to in vitro validation for their inhibitory capability. They showed promising inhibition of parasite growth with IC50 values ranging between 200 and 400 ng/mL. In this regard, the three PfHsp90 inhibitors can be further developed as potential antimalarial drugs. However, further structural optimization studies and clinical (in vivo) tests are necessary to ascertain the antimalarial activity of these compounds in humans.

Identification of Novel Compounds Targeting the Liver X Receptor (LXR): In-silico Studies, Screening, Molecular Docking, and Chemico-pharmacokinetic Analysis

Studies have demonstrated the association between LXR activity dysregulation with many diseases, including atherosclerosis, diabetes and cancer. In recent years, several LXR agonists have surfaced, but none have been approved for human use due to adverse effects or unforeseen reasons. In this study, we first analysed the mRNA and protein expression of LXRs across tissues, network and pathway analysis, and reinterpreted their physiological function and disease association by utilizing multiple biological data repositories, including RNA-seq human protein atlas, DisGeNET, etc. Then, we performed ligand-based virtual screening, chemico-pharmacokinetic analysis, docking and simulation to identify potential new compounds. Our findings of mRNA, protein expression, network and disease enrichment analysis reveal diverse physiological functions of LXRs addressing the possibility of pharmacological manipulation with small molecules would provide therapeutic strategies for disease management. Evaluation of the docking and chemico-pharmacokinetic properties directed to the selection of LXR-623 and AZ876 as promising candidates for LXR-α and LXR-β for further in-silico investigation. Comprehensive screening for new ligands targeting LXRs based on the chemical structures of LXR-623 and AZ876, identified ZINC000005399501 and ZINC000021912941 with the highest binding affinity (−9.8 and −10.7 kcal/mol) for LXRα and LXRβ, respectively. Our results also supported in simulation study, along with favorable chemico-pharmacokinetic features.

Structural insight into the lead identification of a dual inhibitor of PDE1B and PDE10A: Integrating pharmacophore-based virtual screening, molecular docking, and structure-activity-relationship approaches

Schizophrenia is a chronic neuropsychiatric disorder affecting more than 1% of the world's population. Current antipsychotic treatments show inadequacy in mitigating the negative and cognitive symptoms of schizophrenia. In addition, these medications cause undesirable extrapyramidal side effects. According to the studies, inhibition of phosphodiesterase (PDE) 1B and PDE10A simultaneously can alleviate positive, negative, and cognitive symptoms of schizophrenia. Thus, this study aims to identify new dual inhibitors of PDE1B and PDE10A using ligand-based pharmacophore modelling, virtual screening, and molecular docking studies. Accordingly, the generated pharmacophore models of PDE1B and PDE10A comprised hydrogen bond acceptor, aromatic ring, and hydrophobic features. These features were essential for retrieving the active hits from the Universal Natural Product Database in the virtual screening. Additional filters were subsequently employed to identify potential hits that could be developed into central nervous system-active compounds. Hits meeting all the screening criteria were subjected to docking studies with PDE1B and PDE10A. Among these hits, UNPD167314 exhibited significant binding affinities for the target receptors. It occupied the P-clamp and displayed hydrophobic, aromatic, and hydrogen bond interactions with the active site residues of both receptors, thus selected as a lead compound for the design of potent and selective dual inhibitors. The structural modifications of UNPD167314 resulted in the design of 35 novel inhibitors. Out of 35, four compounds exhibited high and comparable binding affinities for both PDE1B and PDE10A, making them promising candidates for further evaluation and optimisation.

Use of the Novel Site-Directed Enzyme Enhancement Therapy (SEE-Tx) Drug Discovery Platform to Identify Pharmacological Chaperones for Glutaric Acidemia Type 1.

Allosteric regulators acting as pharmacological chaperones hold promise for innovative therapeutics since they target noncatalytic sites and stabilize the folded protein without competing with the natural substrate, resulting in a net gain of function. Exogenous allosteric regulators are typically more selective than active site inhibitors and can be more potent than competitive inhibitors when the natural substrate levels are high. To identify novel structure-targeted allosteric regulators (STARs) that bind to and stabilize the mitochondrial enzyme glutaryl-CoA dehydrogenase (GCDH), the computational site-directed enzyme enhancement therapy (SEE-Tx) technology was applied. SEE-Tx is an innovative drug discovery platform with the potential to identify drugs for treating protein misfolding disorders, such as glutaric acidemia type 1 (GA1) disease. Putative allosteric regulators were discovered using structure- and ligand-based virtual screening methods and validated using orthogonal biophysical and biochemical assays. The computational approach presented here could be used to discover allosteric regulators of other protein misfolding disorders.

Discovering potential asthma therapeutics targeting hematopoietic prostaglandin D2 synthase: An integrated computational approach

Allergic asthma, a chronic inflammatory illness that affects millions worldwide, has serious economic and health consequences. Despite advances in therapy, contemporary treatments have poor efficacy and negative effects. This study investigates hematopoietic prostaglandin D2 synthase (HPGDS) as a potential target for novel asthma therapies. Targeting HPGDS may provide innovative treatment methods. A library of phytochemicals was used to find putative HPGDS inhibitors by structure-based and ligand-based virtual screening. Among the 2295 compounds screened, four compounds (ZINC208828240, ZINC95627530, ZINC14727536, and ZINC14711790) demonstrated strong binding affinities of −10.4, −10.3, −9.2, −9.1 kcal/mol respectively with key residues, suggesting their potential as a highly effective HPGDS inhibitor. Molecular dynamics (MD) simulations and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) computations were further performed to evaluate the stability and binding affinity of the complexes. MD simulations and MMPBSA confirmed that compound ZINC14711790 showed high stability and binding affinity (binding energy −31.52 kcal/mol) than other compounds, including HQL-79, suggesting that this compound might be used as promising inhibitors to treat asthma. RMSD and RMSF analysis also revealed that ZINC14711790 exhibited strong dynamic stability. The findings of this study show the efficacy of ZINC14711790 as HPGDS inhibitors with high binding affinity, dynamic stability, and appropriate ADMET profile.

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

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

Groenlandicine enhances cisplatin sensitivity in cisplatin-resistant osteosarcoma cells through the BAX/Bcl-2/Caspase-9/Caspase-3 pathway

Groenlandicine is a protoberberine alkaloid isolated from Coptidis Rhizoma, a widely used traditional Chinese medicine known for its various biological activities. This study aims to validate groenlandicine’s effect on both cisplatin-sensitive and cisplatin-resistant osteosarcoma (OS) cells, along with exploring its potential molecular mechanism.The ligand-based virtual screening (LBVS) method and molecular docking were employed to screen drugs. CCK-8 and FCM were used to measure the effect of groenlandicine on the OS cells transfected by lentivirus with over-expression or low-expression of TOP1. Cell scratch assay, CCK-8, FCM, and the EdU assay were utilized to evaluate the effect of groenlandicine on cisplatin-resistant cells. WB, immunofluorescence, and PCR were conducted to measure the levels of TOP1, Bcl-2, BAX, Caspase-9, and Caspase-3. Additionally, a subcutaneous tumor model was established in nude mice to verify the efficacy of groenlandicine.Groenlandicine reduced the migration and proliferation while promoting apoptosis in OS cells, effectively damaging them. Meanwhile, groenlandicine exhibited weak cytotoxicity in 293T cells. Combination with cisplatin enhanced tumor-killing activity, markedly activating BAX, cleaved-Caspase-3, and cleaved-Caspase-9, while inhibiting the Bcl2 pathway in cisplatin-resistant OS cells. Moreover, the level of TOP1, elevated in cisplatin-resistant OS cells, was down-regulated by groenlandicine both in vitro and in vivo. Animal experiments confirmed that groenlandicine combined with cisplatin suppressed OS growth with lower nephrotoxicity.Groenlandicine induces apoptosis and enhances the sensitivity of drug-resistant OS cells to cisplatin via the BAX/Bcl-2/Caspase-9/Caspase-3 pathway. Groenlandicine inhibits OS cells growth by down-regulating TOP1 level.Therefore, groenlandicine holds promise as a potential agent for reversing cisplatin resistance in OS treatment.

Discovery of BI-9787, a potent zwitterionic ketohexokinase inhibitor with oral bioavailability

Fructose metabolism by ketohexokinase (KHK) is implicated in a variety of metabolic disorders. KHK inhibition is a potential therapeutic strategy for the treatment of diseases including diabetes, non-alcoholic fatty liver disease, and non-alcoholic steatohepatitis. The first small-molecule KHK-inhibitors have entered clinical trials, but it remains unclear if systemic inhibition of KHK by small-molecules will eventually benefit patients. Here we report the discovery of BI-9787, a potent, zwitterionic KHK inhibitor characterized by high permeability and favorable oral rat pharmacokinetics. BI-9787 was identified by optimizing chemical starting points generated via a ligand-based virtual screening of Boehringer’s virtual library of synthetically accessible compounds (BICLAIM). It serves as a high-quality in vitro and in vivo tool compound for investigating the role of fructose metabolism in disease.

Ligand-Based Virtual Screening as a Path to New Chemotypes for Candidate PET Radioligands for Imaging Tauopathies.

Ligand-based virtual screening (LBVS) has rarely been tested as a method for discovering new structural scaffolds for PET radioligand development. This study used LBVS to discover potential chemotype leads for developing radioligands for PET imaging of tauopathies. ZINC12, a free database of over 12 million commercially available compounds, was searched to discover novel scaffolds based on similarities to four query compounds. Thirteen high-ranking hits were purchased and assayed for their ability to compete against three tritiated radioligands at their distinct binding sites in Alzheimer's disease brain tissue. Three hits were 2-substituted 6-methoxy naphthalenes. Synthetic elaboration of this new chemotype yielded three new ligands (25, 26, and 28) with high affinity for the [3H]6 (flortaucipur) neurofibrillary tangle binding site. Compound 28 showed remarkably high affinity (Ki, 7 nM) and other desirable properties for a candidate PET radioligand, including low topological polar surface area, moderate computed log D, and amenability for labeling with carbon-11. LBVS appears to be uniquely valuable for discovering new chemotypes for candidate PET radioligands.

Modeling Molecular Study between SDO1/Inhibitors: Search of New Treatments for Amyotrophic Lateral Sclerosis

Background: The Amyotrophic lateral sclerosis (ALS) is a degenerative and most frequent motor neuron disease characterized by the progressive impairment of upper and lower motor neurons. The treatment of the disease is still palliative and limited to the use of only two drugs, riluzole and edaravone, which only prolong survival by a few months. Taking into account the low number of therapy available for this disease, identification of novel therapeutic strategies for ALS is urgently needed. The superoxide dismutase 1 (SOD1) was the first gene in which mutations were found to be causative for the neurodegenerative disease and has been used as a promising target for the ALS treatment. Methods: In this work we used powerful computational tools (in silico method) such as Ligand-based Virtual Screening (SBVS), docking and molecular dynamics techniques to collaborate with the discovery of new candidates for more potent drugs to be used in the ALS disease treatment. Results: Compound 1 shows good stability in the active site of the SOD1 enzyme, with an intermolecular interaction energy of -154.80 kcal/mol. In addition, the presence of some amino acids such as Glu24, Glu21, Pro28, Lys23 and Lys30 is important for to maintain stability of this compound inside SOD1. Conclusion: This study was essential due to a low number of therapy available for this disease until the moment. With this study, it was possible to observe that Compound 1 is the most promising for the design of SOD1 mutant enzyme potential inhibitors. However, experimental tests in the SOD1 mutant to validate the inhibitory effect of Compound 1 will be required.

Targeting COVID-19 and varicocele by blocking inflammasome: Ligand-based virtual screening

COVID-19 is a new generation of outbreaks that invade not only local emerging region, continental but also the whole globe. Varicocele on the other hand, is a testicular vascular disease that underlies 40 % of male infertility cases. Fortunately, the two diseases can be blocked through targeting one common target, NLRP3 inflammasome. Upon searching for similar drugs that gained FDA-approval in ChEMBL library along with examining their potential blockade of the receptor through docking using CB-DOCK-2, three potential approved drugs can be repurposed, ChEMBL 4297185, ChEMBL 1201749, ChEMBL 1200545 which had binding energy of −9.8 and −9.7 kcal/mol (stronger than the reference inhibitor, −9.3 kcal/mol). Also, ADME profile of the top 3 drugs showed better attributes. Also, the simulated proteins exhibited stable pattern with strong free binding energies. Among the potential inhibitor drugs ChEMBL 4297185 was found to remain inside the binding site of the protein during the 200 ns simulation time. Hence, it is anticipated to have the highest binding and thus inhibition potential against the protein. The suggested drugs, especially ChEMBL 4297185, are potentially repurposable toward treating COVID-19 and varicocele which deserve further experimental validation.

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors.

The fusion of traditional chemical descriptors with Graph Neural Networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrative strategy vary significantly among different GNNs. Specifically, while GCN and SchNet demonstrate pronounced improvements by incorporating descriptors, SphereNet exhibits only marginal enhancement. Intriguingly, despite SphereNet's modest gain, all three models-GCN, SchNet, and SphereNet-achieve comparable performance levels when leveraging this combination strategy. This observation underscores a pivotal insight: sophisticated GNN architectures may be substituted with simpler counterparts without sacrificing efficacy, provided that they are augmented with descriptors. Furthermore, our analysis reveals a set of expert-crafted descriptors' robustness in scaffold-split scenarios, frequently outperforming the combined GNN-descriptor models. Given the critical importance of scaffold splitting in accurately mimicking real-world drug discovery contexts, this finding accentuates an imperative for GNN researchers to innovate models that can adeptly navigate and predict within such frameworks. Our work not only validates the potential of integrating descriptors with GNNs in advancing ligand-based virtual screening but also illuminates pathways for future enhancements in model development and application. Our implementation can be found at https://github.com/meilerlab/gnn-descriptor.

Ligand-Based Virtual Screening for Discovery of Indole Derivatives as Potent DNA Gyrase ATPase Inhibitors Active against Mycobacterium tuberculosis and Hit Validation by Biological Assays.

Mycobacterium tuberculosis is the single most important global infectious disease killer and a World Health Organization critical priority pathogen for development of new antimicrobials. M. tuberculosis DNA gyrase is a validated target for anti-TB agents, but those in current use target DNA breakage-reunion, rather than the ATPase activity of the GyrB subunit. Here, virtual screening, subsequently validated by whole-cell and enzyme inhibition assays, was applied to identify candidate compounds that inhibit M. tuberculosis GyrB ATPase activity from the Specs compound library. This approach yielded six compounds: four carbazole derivatives (1, 2, 3, and 8), the benzoindole derivative 11, and the indole derivative 14. Carbazole derivatives can be considered a new scaffold for M. tuberculosis DNA gyrase ATPase inhibitors. IC50 values of compounds 8, 11, and 14 (0.26, 0.56, and 0.08 μM, respectively) for inhibition of M. tuberculosis DNA gyrase ATPase activity are 5-fold, 2-fold, and 16-fold better than the known DNA gyrase ATPase inhibitor novobiocin. MIC values of these compounds against growth of M. tuberculosis H37Ra are 25.0, 3.1, and 6.2 μg/mL, respectively, superior to novobiocin (MIC > 100.0 μg/mL). Molecular dynamics simulations of models of docked GyrB:inhibitor complexes suggest that hydrogen bond interactions with GyrB Asp79 are crucial for high-affinity binding of compounds 8, 11, and 14 to M. tuberculosis GyrB for inhibition of ATPase activity. These data demonstrate that virtual screening can identify known and new scaffolds that inhibit both M. tuberculosis DNA gyrase ATPase activity in vitro and growth of M. tuberculosis bacteria.