Journal of Computer-Aided Molecular Design最新文献

Radiosensitizers are agents that make tumour cells more sensitive to radiation therapy. One key mechanism involves inhibition of the DNA-dependent protein kinase (DNA-PK), an enzyme crucial for repairing DNA double-strand breaks in mammalian cells. Suppression of the DNA-PK enzyme compromises the double-strand break repairs to amplify the radiation induced toxicity among the tumour cells. In this study, 73 6‑Anilino Imidazo[4,5‑c]pyridin-2-one derivatives were curated as potent DNA-PK inhibitors and subjected them to 2D -and 3D-Quantitative Structure Activity Relationship analyses to explore their structural requirements. Apart from conventional methodology, we implemented newly developed MolSHAP analyses for R-group analyses. Significant information regarding structural requirements were retrieved from each of these cheminformatic analyses. Additionally, to understand the interaction between the ligands and the DNA-PK receptor, molecular dynamics (MD) simulation analysis of 100 ns were carried out for the most and the least potent compounds among the dataset. The findings indicated H-bond and π-π interactions to be the key factors for binding interactions. Furthermore, novel ligands were designed through the MolSHAP tool and were validated through the chemometric model developed in this investigation. The designed compound exhibited favourable predicted activity and replicated key interaction profiles of the co-crystallized bound ligand in MD simulations. The investigation was carried out through open-access tools to safeguard reproducibility and accessibility among researchers.

The SARS-CoV-2 papain-like protease (PLpro) represents a crucial therapeutic target due to its dual role in viral polyprotein processing and suppression of host immune responses through de-ubiquitination and de-ISGylation activities. To identify novel allosteric druggable sites on PLpro, we developed a molecular dynamics approach with flooding fragments (MDFFr), which extends a previously established method –Molecular Dynamics flooding– enabling broader applicability across biological targets. Using MDFFr, we evaluated interactions of known phenolic inhibitors with SARS-CoV-2 PLpro and identified several biologically significant sites, encompassing allosteric hotspots, cryptic pockets, and regions involved in protein–protein interactions. Our simulations not only confirmed experimentally characterized binding sites, including fragment-binding and protein–protein interaction regions for ubiquitin and ISG15 (Interferon-Stimulated Gene 15), but also uncovered previously unrecognized hotspots for further investigation. These results establish MDFFr as a suitable approach for physics-based druggability assessment of biological targets using only protein 3D structure, while providing detailed insights into fragment-protein interactions at both druggable sites and protein–protein interfaces. These findings also unveil new opportunities for allosteric inhibition of PLpro, potentially advancing therapeutic strategies against SARS-CoV-2 and other coronavirus-related diseases. Furthermore, by using “real” drug-like fragments (rather than standard cosolvent “probes”), MDFFr enhances translational relevance and directly informs drug repurposing and ligand discovery efforts.

Two series of alizarine derivatives containing vanillin scaffold (10a-h) or aromatic amide function (12a-h) were synthesized and structurally characterized. The cytotoxic evaluation revealed higher activity towards leukemia cancer cell lines (K562 and HL-60) than solid tumor cells (HeLa and MCF-7). The compound 10 h, containing a benzyl group, showed the most prominent activity against K562 cells, and the lowest toxicity towards healthy cells among all active derivatives. The most active compounds 10f, 10 h, and 12 h were further investigated and induced a significant increase in the percentage of HeLa, K562, and HL-60 cells in the subG1 cell cycle phase in comparison with the control cells. Compounds 10f and 10 h activated apoptosis in K562 cells through all three tested caspases, while derivative 12 h only induced the activation of the main effector caspase-3. Molecular docking simulations suggest that these compounds can form stable complexes with caspase-3, consistent with their experimentally confirmed involvement in caspase-dependent apoptotic pathways. All three tested derivatives demonstrated moderate to strong binding to bovine serum albumin (BSA), with preferential occupation of subdomain IIA (site I), as supported both experimentally and through docking studies. The interaction study of these compounds with DNA indicated their ability to interact with ct-DNA through the minor groove.

Hit-to-lead (H2L) optimization is a critical stage in small-molecule drug discovery, where efficient exploration of chemical space is required to identify promising lead compounds. Conventional H2L workflows rely on iterative synthesis and experimental evaluation, which limit the range of chemical space that can be explored. In contrast, in silico approaches enable efficient selection of promising compounds from a much larger chemical space by generating large numbers of virtual compounds and evaluating them computationally. To harness this potential, we developed an in silico–driven H2L protocol that integrates molecular generation, binding affinity prediction based on relative binding free energies calculated using the non-equilibrium switching (NES) method, and the evaluation of key properties—such as solubility, metabolic stability, and membrane permeability—using machine learning (ML) techniques. In this study, within the context of H2L optimization, we examined the applicability, accuracy, and utility of NES, a relatively new high-precision binding free energy calculation method, and evaluated its effectiveness in large-scale exploration of substituent space. The phosphodiesterase 9A inhibitor was used as a model system. Starting from the reported high-throughput screening hit compound, we first modified the core structure and then sequentially conducted large-scale exploration of two substitution sites. Following this protocol, we narrowed down compounds predicted to those exhibiting not only high binding affinity but also favorable physicochemical and ADME-related properties. Among these, we verified whether the lead compound reported in the literature was included, and confirmed that it appeared as one of the top-ranked candidates. These results demonstrate that an in silico protocol combining large-scale molecular generation, high-accuracy affinity prediction using NES, and ML-based ADME prediction enables H2L optimization that considers a broader substituent space.

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Pancreatic and Esophageal cancers are highly aggressive with high mortality and limited treatment, causing over 466,000 and 544,100 deaths worldwide in 2020 respectively. This highlights the urgent need for safer,and effective anticancer agents. Catalpol, a natural iridoid glycoside, shows anticancer potential, but due to its poor drug-like properties it requires structural modification. This study investigates pyrazole-modified catalpol derivatives as dual inhibitors for these cancers using Quantitative Structure Activity Relationship (QSAR) modelling, molecular docking, and pharmacokinetic studies. We analyzed fourteen pyrazole-modified catalpol derivatives with reported IC50values against four cancer cell lines(BxPC-3, PANC-1, Eca109, and EC9706). The molecules were optimized using DensityFunctional Theory (DFT), and 2D molecular descriptors were calculated using PaDEL. QSAR models were developed by utilizing a Genetic Function Algorithm (GFA) and Multiple Linear Regression (MLR) and validated using statistical metrics such as R², Q², R²adj, and R²pred. Docking studies targeted VEGFR-2 and BRAF V600E kinases using AutoDockVina, while ADMET and drug-likeness properties were predicted using SwissADME and pkCSM tools. The external validation R²pred values for BxPC-3, PANC-1, Eca109, and EC9706 cell lines were 0.9412, 0.9535, 0.9981, and 0.9935, respectively. Among the derivatives, compound 3k showed the highest binding affinity for VEGFR-2 (− 8.18 kcal/mol) and BRAF (− 8.64 kcal/mol), surpassing the control drugs etoposide (− 8.00 kcal/mol) and dabrafenib (− 8.15 kcal/mol) respectively. ADMET analysis confirmed good intestinal absorption, limited blood-brain barrier penetration, non-toxicity, acceptable total clearance, and compliance with Lipinski’s rule. Overall, the study suggests that pyrazole-modified catalpol derivatives, especially compound 3k, are promising multi-target inhibitors for pancreatic and esophageal cancers, justify further in-vitro and in-vivo studies.

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.