Journal of Computer-Aided Molecular Design最新文献

Paclitaxel, a cornerstone in cancer chemotherapy, stabilizes microtubules by binding to the β-tubulin taxane site. However, resistance mechanisms, often driven by β-tubulin mutations, undermine its efficacy. While such mutations are known to alter drug sensitivity, their molecular impact on paclitaxel binding remains incompletely understood. Here, we employ molecular docking, molecular dynamics (MD) simulations, and Molecular Mechanics with Generalized Born Surface Area (MM/GBSA) calculations to quantify how clinically relevant β-tubulin mutations affect paclitaxel binding affinity. Root mean square fluctuation (RMSF) analysis was used to assess local structural dynamics near the binding site. Our results show that despite minor variations in docking scores, MM/GBSA analyses revealed significant mutation-induced shifts in binding free energy, particularly for residues near the M loop, H5–H6 helices, and S9–S10 region. Complementary RMSF analysis indicated altered flexibility in several of these regions, suggesting potential disruptions to local stabilization mechanisms. Differences between single- and two-dimer simulations highlight the importance of modeling lateral protofilament contacts when evaluating microtubule-targeting agents. These findings underscore the relevance of structure-based modeling for understanding drug resistance mechanisms and informing the development of mutation-aware taxane therapies.

The increasingly complex nature of drug discovery requires new computational approaches to reduce cost and development time. This work introduces a novel bidirectional transformer-based architecture that seamlessly maps natural-language drug indications to Simplified Molecular Input Line Entry System (SMILES) encoded molecular structures. The proposed model MedT5-Bi integrates three key contributions that collectively enhance molecular generation from textual descriptions. First, a Molecule-Aware Embeddings (MAEmb) module fuses MolEmbedder token embeddings with structural insights derived from a Graph Neural Network (GNN) to effectively capturing both sequential and topological features of chemical entities. Second, a Dynamic Attention Mechanism (DAM) adaptively switches between softmax and log-linear attention formulations based on input length and complexity, thereby maintaining performance consistency across varying sequence distributions. Third, the system is fine-tuned via reinforcement learning (RL) using a carefully designed composite reward function that jointly optimizes chemical validity, structural similarity, and fingerprint-based metrics. This RL-based training stage aligns generative outputs with desired chemical properties while improving the model’s generalization across diverse indication inputs. Evaluated on a large, augmented ChEMBL dataset. The proposed architecture outperforms existing state-of-the-art model by 16.6(-)24.8% on standard benchmarks including BLEU, ROUGE, Levenshtein distance, Morgan/Tanimoto similarity, and Text2Mol metrics.

Human metapneumovirus (HMPV) ranks among the chief causes of serious respiratory illness in young children, accounting for about 3–10% of hospital admissions for acute lower respiratory tract infections in those under five years of age. Despite recent outbreaks and its rising incidence in recent years, no licensed vaccines or targeted therapies are currently available. In this study, surface viral proteins were selected as antigenic candidates, and their consensus sequences were derived from 782 HMPV genomes. Then using immunoinformatic approaches, immunodominant CTL, HTL, LBL epitopes within these proteins consensus sequence that exhibited high antigenicity, exhibiting no toxicity, no allergenic potential, and broad conservancy across HMPV clades were identified and combined with adjuvants, the PADRE sequence, and linkers for vaccine development. Physicochemical analysis confirmed that the resulting multi‐epitope mRNA vaccine is stable under physiological conditions. Molecular docking analyses revealed robust interactions with important immune receptors and subsequent molecular dynamics simulations validated the stability of these complexes over time. Immune simulations predicted robust humoral and cellular responses. Finally, a Kozak sequence was included to enhance mRNA stability and translational efficiency, followed by an MITD sequence to enhance epitope presentation, a TAA codon to terminate translation, and for stability 5′ UTR and 3′ UTR was added and the engineered mRNA’s secondary structure was predicted. Additionally final vaccine construct was cloned in silico into the pVAX1 vector, and virtual agarose gel electrophoresis was performed. These results support the potential of our multi‐epitope mRNA vaccine as a promising preventive strategy against HMPV infection.

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This study developed and validated Quantitative Structure-Activity Relationship models to predict the inhibitory activity (pIC₅₀) of 225 EGFR inhibitors. A genetic algorithm selected eight molecular descriptors, which were used to construct two models: a multiple linear regression (MLR) and a stacked ensemble regression (SER). The SER model showed only marginally higher accuracy ((Delta r^2 = +0.022)) but exhibited greater predictive instability ((Delta r^2_{m(test)} = 0.0802) vs. MLR’s 0.0184) and reduced interpretability. Thus, MLR was retained as the primary model due to its OECD-compliant mechanistic transparency and superior generalizability. Rigorous applicability domain analysis confirmed the MLR model’s reliability. Notably, molecular docking (PDB ID: 8A27) identified a top-ranked inhibitor (Compound 121) with high binding affinity ((-12.023) kcal/mol), forming critical hydrogen bonds and hydrophobic interactions with EGFR’s active site. Virtual screening of 32 structural analogs of Compound 121 revealed additional promising candidates. This work provides a robust framework for EGFR inhibitor discovery, combining computational modeling with structural insights.

Molecular dynamics (MD) simulations are extensively employed in biomedical research to explore atomic-level molecular interactions, with broad applications in drug discovery, tissue engineering, and structural biology. This study uses MD simulations to examine the interaction dynamics between graphene oxide (GO) derivatives and two FDA-approved biocompatible polymers, poly(lactic-co-glycolic acid) (PLGA) and poly(ε-caprolactone) (PCL). Custom-built PLGA structures with varying PLA/PGA ratios and PCL were modeled to assess their interactions with GO and reduced graphene oxide (rGO). System stability was evaluated using hydrogen bond occupancy, radius of gyration, potential and binding energies, radial distribution functions, and solvation free energy. Comparative analyses revealed that 75:25 PLGA–GO and 75:25 PLGA–rGO systems exhibited the most stable interaction profiles among PLGA variants, while PCL–GO was the most stable among PCL systems. To our knowledge, this is the first comparative MD study systematically evaluating atomic-scale interactions of GO and rGO with PLGA at different copolymer ratios and with PCL. These findings provide molecular-level insights to guide the design and optimization of polymer–nanoparticle composites for biomedical applications.

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The analysis of interaction dynamics between graphene oxide (GO) derivatives and two biocompatible synthetic polymers, poly(lactic-co-glycolic acid) (PLGA) and poly(ε-caprolactone) (PCL), using molecular dynamics (MD) simulations provides valuable molecular-level insights for the rational design and optimization of polymer–nanoparticle composites for future biomedical applications.

Olfactory receptors (ORs) form the largest subfamily of class A G protein-coupled receptors (GPCRs); however, only a few 3D structures of ORs have been determined. Structure-based virtual screening and improved structural insights are required to effectively identify novel odor molecules and elucidate their binding modes along with mechanisms of activation and inactivation. Herein, we propose a protocol to provide an active state model for the target OR (OR9Q2) with agonist molecules using AlphaFold2, molecular simulations, and virtual screening. Furthermore, we extracted ligand-stable bound sections using the ligand-stable duration (LSD) protocol defined in this study to analyze conformational ensembles of complex structures. We constructed promising complex structures and demonstrated their reliability by calculating the area under the receiver operating characteristic (ROC) curve in virtual screening tests using experimentally validated active and inactive compounds. This study offers a reliable structure-based screening protocol for olfactory receptors, which can subsequently aid novel odorant discovery and advancing fragrance, flavour, and biosensor industries.

Cryptococcosis, caused by Cryptococcus neoformans and Cryptococcus gattii, remains a severe fungal infection, with current antifungal treatments facing challenges such as toxicity, prolonged therapy, and resistance. Isocitrate lyase (ICL1), a key enzyme in the glyoxylate cycle, is a potential antifungal target. This study combined in silico and in vitro approaches to identify ICL1 inhibitors. Virtual screening of FDA-approved drugs selected five candidates: bufexamac, isoniazid, nifuraldezone, nifuroxazide, and ribavirin. Molecular dynamics simulations and binding free energy calculations highlighted π-interactions with Trp97 as crucial for ligand stabilization, with isoniazid emerging as a top candidate due to strong binding and structural stability. In vitro testing confirmed isoniazid’s antifungal activity against Cryptococcus spp., but MIC values were high, indicating variable susceptibility. For C. neoformans, ATCC 499 showed the highest MIC (70 mg/mL), while IEC-Crypto01 exhibited 35 mg/mL. For C. gattii, ATCC R265 displayed 2.19 mg/mL, and IEC-Crypto04 was inhibited at 8.75 mg/mL. These results suggest a strain-dependent response and a limited direct antifungal effect at high concentrations. However, previous reports showed that isoniazid also inhibits cryptococcal biofilm formation, reinforcing its potential role in combination therapies. Additionally, the data suggests a dual mechanism of action, targeting both metabolism and membrane integrity. This study provides novel insights into ICL1 inhibition and contributes to drug repurposing efforts for cryptococcosis. Despite high MIC values, isoniazid’s antifungal activity warrants further investigation, particularly in synergistic combinations with existing antifungals.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting millions of people worldwide, with its prevalence expected to rise in the coming years. Due to the complexity of AD and the intricate interplay among its pathological mechanisms, the development of multitarget-directed ligands (MTDLs) has emerged as a promising therapeutic strategy. These compounds could simultaneously modulate multiple pathogenic pathways. Specifically, cholinergic and amyloid mechanisms, implicated in the onset of the disease, are regulated by AChE and BACE1, respectively. Therefore, targeting both pathways offers substantial therapeutic potential for AD. Computational tools can be useful in the identification of potential MTDL for these enzymes, reducing both costs and time in the drug discovery process. This review explores the relevance of this approach in the research and development for novel AD therapies, highlighting ongoing efforts focused on the identification and development of MTDLs for AChE and BACE1 inhibition through in silico methods. Virtual screening was the most frequently applied technique for a fast selection of ligands based on their affinity for the enzymes of interest. The in silico ADMET prediction also appears with a technique that allows the screening of compounds with drug-likeness. Moreover, evidence suggests that combining multiple computational methods can effectively identify drug candidates with optimized properties for target modulation and brain bioavailability.

The alarming rise in flaviviruses like Zika virus (ZikV) and dengue virus (DenV) has made them a major global health concern, especially in tropical and subtropical regions where nearly half of the global population is at risk. The urgency for safe and effective antiviral treatment is underscored by the fact that, despite growing research efforts, there are still no FDA-approved drugs available. The methyltransferases of ZikV and DenV, i.e., non-structural protein-5 (NS5), stand out as a highly conserved enzyme that is involved in viral replication and evasion of the host immune system via a capping mechanism, making them a key target for antiflaviviral drug development. In this study, we employed a virtual reality and cheminformatics-assisted pipeline followed by biological validation to identify a potent natural inhibitor of NS5. The systematic computational analysis identified the natural compound ZINC8952607 as a putative inhibitor with high binding affinity towards the methyltransferases of ZikV and DenV. Initial screening and docking analysis reveal that the lead compound strongly binds at the active site of the NS5 protein with a higher affinity. Further, extensive analysis involving molecular dynamics and DFT establishes greater reactivity of the lead compound and its stable complex formation with the NS5 protein. In vitro assay determined the cytotoxicity of the lead molecule with a CC₅₀ value of 3.43 ± 0.17 µM, indicating its reasonable safety as a lead molecule. These findings highlight ZINC8952607 as a potential lead candidate and reinforce the importance of targeting NS5 to develop new antiviral drugs.

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