We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target\'s binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is implicated in accumulation of amyloid β-protein (Aβ) and phosphorylation of Tau proteins, and thus represents an important therapeutic target for neurodegenerative diseases. Though many DYRK1A inhibitors have been discovered, there is still no marketed drug targeting DYRK1A. This is partly due to the lack of effective and safe chemotypes. Therefore, it is still necessary to identify new classes of DYRK1A inhibitors. By performing virtual screening with the workflow mainly composed of pharmacophore modeling and molecular docking as well as the following DYRK1A inhibition assay, we identified compound L9, ((Z)-1-(((5-phenyl-1H-pyrazol-4-yl)methylene)-amino)-1H-tetrazol-5-amine), as a moderately active DYRK1A inhibitor (IC50: 1.67 μM). This compound was structurally different from the known DYRK1A inhibitors, showed a unique binding mode to DYRK1A. Furthermore, compound L9 showed neuroprotective activity against okadaic acid (OA)-induced injury in the human neuroblastoma cell line SH-SY5Y by regulating the expression of Aβ and phosphorylation of Tau protein. This compound was neither toxic to the SH-SY5Y cells nor to the human normal liver cell line HL-7702 (IC50: >100 μM). In conclusion, we have identified a novel DYRK1A inhibitor with neuroprotective activity through virtual screening and in vitro biological evaluation, which holds the promise for further study.

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs\' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski\'s rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

Background: In traditional Mongolian or Tibetan medicine in China, Chebulae Fructus (CF) is widely used to process or combine with aconitums to decrease the severe toxicity of aconitums. Researches in this area have predominantly focused on tannins, with few research on other major CF components for cardiotoxicity mitigation. The present study aimed to clarify whether triterpenoids can attenuate the cardiotoxicity caused by mesaconitine (MA) and investigate the mechanism of cardiotoxicity attenuation. Methods: Firstly, the pharmacophore model, molecular docking, and 3D-QSAR model were used to explore the mechanism of CF components in reducing the toxicity of MA mediated by the TRPV1 channel. Then three triterpenoids were selected to verify whether the triterpenoids had the effect of lowering the cardiotoxicity of MA using H9c2 cells combined with MTT, Hoechst 33258, and JC-1. Finally, Western blot, Fluo-3AM, and MTT assays combined with capsazepine were used to verify whether the triterpenoids reduced H9c2 cardiomyocyte toxicity induced by MA was related to the TRPV1 channel. Results: Seven triterpenoids in CF have the potential to activate the TRPV1 channel. And they exhibited greater affinity for TRPV1 compared to other compounds and MA. However, their activity was relatively lower than that of MA. Cell experiments revealed that MA significantly reduced H9c2 cell viability, resulting in diminished mitochondrial membrane potential and nuclear pyknosis and damage. In contrast, the triterpenoids could improve the survival rate significantly and counteract the damage of MA to the cells. We found that MA, arjungenin (AR), and maslinic acid (MSA) except corosolic acid (CRA) upregulated the expression of TRPV1 protein. MA induced a significant influx of calcium, whereas all three triterpenoids alleviated this trend. Blocking the TRPV1 channel with capsazepine only increased the cell viability that had been simultaneously treated with MA, and AR, or MSA. However, there was no significant difference in the CRA groups treated with or without capsazepine. Conclusion: The triterpenoids in CF can reduce the cardiotoxicity caused by MA. The MSA and AR function as TRPV1 agonists with comparatively reduced activity but a greater capacity to bind to TRPV1 receptors, thus antagonizing the excessive activation of TRPV1 by MA.

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.

OBJECTIVE: Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies.
METHODS: Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model.
RESULTS: Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition.
CONCLUSIONS: This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.

The type II-C-KIT signaling network has been extensively studied for its potential as a target for cancer treatment, leading to the investigation of quinoline derivatives as compounds with inhibitory effects on c-Kit kinase. In this study, a multistage approach was employed, including the creation of pharmacophore models, 3D QSAR analysis, virtual screening, docking investigations, and molecular dynamics stimulation. The pharmacophore evaluation included a data set of 29 ligands, which resulted in the generation of the ADDHR_1pharmacophore model as the most promising, with a survival score of 5.6812. The main objective was to utilize the atom-based 3D-QSAR approach for generating robust 3D-QSAR models aimed at identifying new TypeII-C-kit kinase inhibitors. The evaluations of these models have convincingly demonstrated their high predictive power (Q2 = 0.6547, R2 = 0.9947). Using atom-based 3D-QSAR data, a total of 7564 novel compounds were generated from R-group enumeration. Molecular docking and MM-GBSA study revealed that compound A1 exhibited the highest binding score of -9.30 kcal/mol and a Δ GBind value of -90.56 kcal/mol. The ZINC compounds were then screened using the pharmacophore model, followed by virtual screening, which identified ZINC65798256, ZINC09317958, ZINC73187176, and ZINC76176670 as potential candidates with promising docking scores. Among these, ZINC65798256 demonstrated the best binding interactions with amino acid residues, ASP810, LYS623, CYS673, and THR670 (PDB ID: 1T46). To further analyze the structural features and molecular interactions, molecular dynamics simulation was conducted for a time scale of 100 ns.Communicated by Ramaswamy H. Sarma.