Journal of Computer-Aided Molecular Design最新文献

Botulinum neurotoxins are class I tier bioterrorism agent, accountable for causing rare but fatal illness ‘botulism’. Out of seven serotypes, A, B, E, and F are responsible for intoxicating humans. Despite knowing harmful effects on human health for centuries, there is no commercial antidote for post-neuronal intoxication is available. In the present study, we report efficacy of regioisomers of [(8-hydroxyquinolin-7-yl)(phenyl) methylamino]benzoic acid against zinc-dependent light chain activities of BoNT/A, /B, /E & /F by combining molecular modeling with in vitro and in vivo studies. Based on structure similarity search, multiple regioisomers of 8-hydroxyquinoline were mined and screened by performing molecular docking. The best-scored compounds were analyzed for inhibitory and binding potential against these serotypes via endopeptidase and surface plasmon resonance assays. The best two compounds (NSC1011 and NSC1012) with potential inhibition and binding kinetics across serotypes were evaluated for therapeutic potential in mouse model. NSC1011 and NSC1012 (regioisomers) docking data revealed their binding energies with active domains of BoNT/A, /B, /E, and /F light chains ranging between − 9.70 to − 4.27, and  − 9.84 to − 7.23 kcal/mol, respectively. The endopeptidase assay displayed ˃ 90% inhibition of catalytic activities, with the IC₅₀ values varying among serotypes from 20 to 40 µM concentrations. SPR interaction of both compounds with the targeted proteins was observed in the range of 3.83E-05 to 4.95E-04 M. These molecules have shown complete protection at one MLD (mouse lethal dose), whereas median extension of animal survival was recorded up to 24 h when exposed to 5X MLD. The in silico, in vitro, and in vivo data reveal that NSC1011 and NSC1012 exhibited good binding affinity, stability, inhibition with promising therapeutic potential against human botulism-causing toxinotypes.

We introduce a generative drug-design framework that combines large chemical language models (CLMs) pretraining, target specific masked-language fine-tuning, and reinforcement learning (RL) to create novel small molecule inhibitors of EGFR. Using a multi-objective reward that balances predicted potency, drug-likeness, synthetic accessibility, and structural novelty, the model learns to explore chemically valid and diverse regions of EGFR-relevant chemical space beyond known inhibitors. The resulting compounds exhibit improved computational binding trends relative to reference EGFR inhibitors and include highly novel chemotypes with no close analogs in the training set. This study demonstrates how integrating pretrained chemical language models with reinforcement learning can accelerate target focused de novo molecular design and provides a generalizable framework for future applications in kinase inhibitor discovery.

Type II diabetes mellitus is a major endocrine disorder characterized by persistent hyperglycemia, insulin resistance, and dysregulation in glucose uptake by the cells. Peroxisome proliferator-activated receptor-γ (PPARγ) plays a significant role in the regulation of glucose and lipid metabolism as well as in post-diabetic inflammatory response. Therefore, PPARγ activators seem to be the drugs of choice. In the present work, structure-based virtual screening approach was employed to find newer compounds as PPARγ agonist. The ChemDiv library (freely available) of compounds was used for hierarchical virtual screening; the hits obtained were further evaluated based on in silico predicted binding energy and toxicity predictions. The structure-based approach yielded 18 high-affinity, stably binding hits, from which 08 hits (Sn1-Sn8) were predicted to be non-toxic. Further, in vitro exploration of the anti-diabetic as well as PPARγ agonistic potential was carried out on eight (08) ligands obtained from in silico scrutiny, using various in vitro assays. The synthesized quinazolinedione based compound (Sn9) was also evaluated similarly for exploration of its lead-likeness as PPARγ agonistic anti-diabetic candidate. Compounds Sn7 and Sn8 showed adequate glucose uptake by the cells, anti-adipogenicity, and PPARγ binding, while Sn4 and Sn9 showed moderate potential in the same examination. Safety profiles of these compounds on 3T3-L1 and C2C12 cells were also established. The in vitro studies suggested that imidazopyridine (present in Sn4, Sn8) and quinazolinedione (present in Sn7 and Sn9) have much potential against T2DM. Sn8 was found to be the best candidate, and it also demonstrated a stable trajectory and interaction profile in simulated physiological environment. The study confirms the lead-like potential of compound Sn8, and supports the exploration of imidazopyridine and quinazolinedione ring systems for further development of PPARγ agonistic lead compounds in the anti-diabetic arena.

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The integration of artificial intelligence (AI) with molecular modeling offers new opportunities to accelerate therapeutic discovery. In this study, we developed an AI-driven generative pipeline combining deep reinforcement learning (DRL), generative adversarial networks (GANs), and variational autoencoders (VAEs) to design novel peptide-like molecules targeting major proteins implicated in endometrial cancer (EC), including AKT1, ESR1, Connexin-43, and CTNNB1. From over 14,200 generated structures, approximately 2313 peptides met drug-likeness and structural criteria and were screened using deep learning-enhanced docking. Top-ranked peptides, such as Gitoxoside (− 11.53 kcal/mol) and 9-Fluoro-11 (− 11.38 kcal/mol), demonstrated stronger binding to AKT1 than the reference inhibitor Capivasertib (− 8.50 kcal/mol). Similar high-affinity interactions were observed for CTNNB1–SCHEMBL (− 12.33 kcal/mol) and ESR1–1Estra-1,3 (− 11.05 kcal/mol). Molecular dynamics (MD) simulations confirmed the stability of these complexes with RMSD values below 2.5 Å and minimal residue fluctuations. WaterSwap free energy calculations yielded highly favorable binding energies (− 34 to − 37 kcal/mol), further validating stable ligand–protein interactions. ADMET predictions indicated acceptable pharmacokinetic properties and low predicted toxicity for most candidates. Collectively, this integrative AI framework efficiently explores peptide chemical space, enabling the rapid identification of peptide-based and peptidomimetic inhibitors with strong binding affinity and stability. The findings highlight the potential of AI-assisted peptide design as a scalable and cost-effective strategy for developing next-generation therapeutics against endometrial cancer.

The c-Myc oncogene is crucial in tumorigenesis. Although it is a promising therapeutic target, its protein lacks a conventional drug-binding pocket, making it traditionally “undruggable”. Recent studies show that the c-Myc promoter can form a G-quadruplex (G4) structure, which suppresses transcription and offers a new strategy for indirect inhibition. In this study, structure-based virtual screening was performed using the c-Myc G4 crystal structure to screen the ChemDiv compound library, aiming to identify small molecules that bind to the G4 structure. Candidate compounds were evaluated in preliminary in vitro assays for biological activity. The results showed that Y502-3888 binds to the c-Myc G4 and downregulates c-Myc expression at both mRNA and protein levels. Collectively, these findings support the potential of Y502-3888 as a c-Myc G4 binder for the treatment of multiple myeloma (MM), providing a foundation for future development of anticancer agents targeting the c-Myc G4.

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A prodrug is a pharmacologically inactive (or attenuated) derivative that undergoes bioreversible transformation in vivo to release an active parent drug, enabling temporary optimization of properties such as solubility, permeability, and targeting. Despite expanding catalogs of known prodrugs, in silico screening remains limited by the absence of reliable negative examples: training/evaluation sets often contain only positives or ad-hoc decoys, leading to class imbalance, property-mismatch shortcuts, and irreproducible benchmarks. Unfortunately, the limitation of reliable negatives has resulted in there being no efficient machine learning-based prodrug screening approach. Therefore, we introduce Prodrug-ML, an efficient machine learning-based screen for prodrug-likeness that prioritizes candidates rather than asserting mechanistic truth. Prodrug-ML helps medicinal chemists triage prodrugging ideas during hit-to-lead and lead optimization, filter enumerated libraries of promoiety–attachment variants before ADMET assays, and retrospectively mine internal/ChEMBL-like collections to surface likely prodrug chemotypes. In practice, users (i) generate or collect candidate structures (e.g., parent drug ± pro-moieties), (ii) score them with Prodrug-ML, and (iii) advance only high-scoring candidates to synthesis/assay, thereby reducing wet-lab load while maintaining chemical diversity. In order to achieve such practical usage, the Prodrug-ML framework, containing the default classifier, LightGBM, addresses these issues by (i) constructing three complementary, property-controlled negative cohorts (DUD-E–style near-misses, random ChEMBL, and strictly filtered ChEMBL), (ii) hardness control and label-noise guardrails on decoys, (iii) domain-bias control, and (iv) cross-decoy validation with multimodel feature selection. Produg-ML has been evaluated five times on hold-out data and an unseen test benchmark, after 80% of training data. In the benchmarks, the multimodel ensemble consistently improves early retrieval and overall discrimination, attaining (textrm{EF}@1%approx 6text {--}8), (textrm{EF}@5%approx 5text {--}6), (textrm{BEDROC}_{20}approx 0.78text {--}0.82), (textrm{BEDROC}_{50}approx 0.90text {--}0.95), and (textrm{BEDROC}_{80}approx 0.95text {--}0.99), alongside ROC AUC (approx 0.86text {--}0.87), average precision (approx 0.60text {--}0.65), and F1 (approx 0.58text {--}0.62). As a result, these results, especially high BEDROC scores, are consistent with concentrating at least a prodrug within the top (sim 2text {--}3%) of ranked candidates, implying (sim 97text {--}98%) reductions in experimental time and cost when using standard wet-lab workflows that assay only the early tranche.

Eukaryotic translation initiation factor 4E (eIF4E) plays a critical role in cap-dependent translation by binding the 7-methylguanosine (m⁷G) cap at the 5′ end of mRNAs, thereby regulating the synthesis of proteins essential for cell growth, survival, and proliferation. Under homeostatic conditions, eIF4E selectively translates a subset of mRNAs; however, in cancer, aberrant signaling leads to persistent activation of eIF4E, promoting tumor progression, metastasis, and resistance to therapy. Despite its clinical relevance, very few studies have explored direct targeting of eIF4E’s cap-binding function using small molecules as a therapeutic strategy. In the present study, we adopted a multi-layered in silico and experimental pipeline to identify small-molecule inhibitors that can effectively disrupt human eIF4E activity. A library of over 400,000 compounds from the ZINC database was virtually screened using the Glide docking protocol in Schrödinger-Maestro. Compounds were shortlisted based on binding affinity and drug-likeness properties. Among the top hits, ZINC145267992, a nucleoside-like molecule, showed promising interaction with the cap-binding pocket of eIF4E. To overcome potential druggability limitations and improve clinical relevance, Entecavir (ETV), a clinically approved antiviral drug for hepatitis B and a structural analogue of ZINC145267992, was identified as a candidate for drug repurposing. Molecular dynamics simulations confirmed the stable interaction of ETV with eIF4E. Our findings not only reinforce the feasibility of targeting eIF4E in cancer but also demonstrate that repurposing FDA-approved drugs like Entecavir could offer a practical and efficient route to therapeutic intervention. This integrative approach opens new avenues for eIF4E-targeted strategies in oncology, aiming to selectively impair oncogenic translation.

The conventional separation of drug-target affinity (DTA) prediction and de novo molecule generation creates a significant bottleneck in drug discovery. To address this, we introduce MutiDTAGen, a unified multi-task learning framework that establishes a bidirectional system between these two complementary tasks. By utilizing shared deep representations and a dynamic optimization strategy, the proposed framework ensures that knowledge from affinity prediction directly guides the generation of target-specific molecules. Our method demonstrates improved performance across multiple benchmarks, achieving, for instance, a 12% reduction in Mean Squared Error (MSE) on the Davis dataset compared to the GraphDTA baseline. This synergistic approach not only enhances prediction accuracy but also improves the quality and target-specificity of generated compounds. By unifying prediction and generation within a single end-to-end architecture, this study offers a unified and efficient computational strategy for drug discovery.

This study elucidates the electronic and structural interplay of 5-(1 H-1,2,4-triazol-1-yl)-2-thiophenecarboxylic acid (TTCA) to assess its potential as a multifunctional heteroaromatic scaffold. Using DFT and TD-DFT calculations at the B3LYP/6-311 + + G(d, p) level, we demonstrate that intramolecular hydrogen bonding locks the triazole and thiophene rings into a highly rigid, planar configuration. This structural coplanarity facilitates extensive π-electron delocalization, which is critical for the molecule’s observed optoelectronic behavior. The analysis reveals a dual electronic character: a chemically stable ground state with a HOMO-LUMO gap of 3.13 eV, contrasted by significant visible-light photoactivity evidenced by a narrow optical transition energy of 1.7 eV. Molecular Electrostatic Potential (MEP) and Non-Covalent Interaction (NCI) analyses identify specific nucleophilic sites and weak interactions that empower TTCA to act as a versatile ligand. Validated by high statistical agreement with experimental literature data for structurally related analogs (FT-IR, NMR, UV–Vis), these results confirm TTCA as a promising candidate for charge-transfer applications, coordination chemistry, and optoelectronic material design.

Three nitro-substituted 2,3-dihydro-1H-perimidine derivatives (ortho, meta-, and para-nitrophenyl) were synthesised via a novel, additive-free ultrasonication-assisted method with high yields (up to 90%). Their structures were validated experimentally and supported by DFT calculations, which also provided insight into the reaction mechanism. Further molecular docking, integrated with MD simulation studies, against the identified EGFR mutants revealed strong binding affinities and stable interactions, especially for the ortho-nitro derivative. To validate these findings, we performed ADME and toxicity analyses that confirmed favourable drug-likeness and safety profiles. Potent anticancer activity consistent with computational predictions was confirmed by MTT assays on NCI-H460 cells. Cell cycle analysis showed that the compounds induced phase-specific arrest, contributing to reduced cell viability. Apoptosis was further validated by Annexin V flow cytometry and AO/EB fluorescence imaging, which revealed early and late apoptotic populations. Overall, the compounds demonstrated strong apoptotic and antiproliferative activity.