COMPUTATIONAL BIOLOGY AND CHEMISTRY计算生物学与化学
COMPUTATIONAL BIOLOGY AND CHEMISTRY(英文缩写 COMPUT BIOL CHEM),ISSN 1476-9271,eISSN 1476-928X,中文译名:计算生物学与化学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.737 | Q2 |
| 2022 | 3.100 | Q2 |
| 2023 | 2.600 | Q2 |
| 2024 | 3.100 | Q1 |
| 2025 | 3.400 | Q1 |
COMPUTATIONAL BIOLOGY AND CHEMISTRY 最新收录文献
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1. Banxia Baizhu Tianma decoction inhibits neuronal cell death, inflammatory injury, and blood-brain barrier disruption after SAH via the PI3K-AKT pathway.
1. 半夏白术天麻汤通过PI3K-AKT通路抑制蛛网膜下腔出血后的神经元细胞死亡、炎症损伤和血脑屏障破坏PMID:日期:2026-12-01Neuronal programmed cell death, inflammation, and blood-brain barrier (BBB) disruption drive secondary injury after subarachnoid hemorrhage (SAH). However, no multi-target therapies are currently available. Banxia Baizhu Tianma Decoction (BBTD) shows therapeutic potential for cerebral hemorrhage. This study aimed to investigate BBTD's protective mechanisms against these pathologies post-SAH. A rat SAH model was established via endovascular perforation. Neuroprotection was assessed using behavioral tests (Foot-Fault, Rotarod, Forelimb Placing, Garcia scores, and water maze), molecular analyses (Western blot, TUNEL, immunofluorescence), BBB assays (edema, Evans Blue/FITC-dextran permeability), and inflammation evaluation (microglial polarization, cytokine ELISA). Signaling pathways were screened via mass spectrometry/network pharmacology and validated with the PI3K inhibitor LY294002. BBTD attenuated neuronal apoptosis by suppressing caspase-3/7/8/9, Bax, and Cytc activation; inhibited necroptosis via suppression of the RIP1/RIP3/MLKL complex; and reduced ferroptosis by downregulating ACSL4 while upregulating GPX4. BBB integrity was restored through increased ZO-1/occludin/claudin-5 expression, inhibition of MMP-3/9/13, and enhanced VASP/eNOS phosphorylation. Inflammation was mitigated via microglial M1-to-M2 polarization shift, reduced cytokine release, and NLRP3/caspase-1 downregulation. The PI3K-AKT pathway was identified as the core mechanism and functionally validated, and the top five high-affinity molecules were identified. BBTD confers neuroprotection against SAH by multi-target inhibition of programmed cell death, BBB disruption, and neuroinflammation, primarily via PI3K-AKT activation. This provides a foundation for preclinical evidence. The data from this study will be made available to qualified investigators upon reasonable inquiry.
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2. Integrated InPETM drug analysis and transcriptomics to explore potential mechanisms and key active components of multiple traditional Chinese medicine prescriptions for psoriasis.
PMID:日期:2026-12-01Psoriasis is a chronic immune-mediated inflammatory skin disease characterized by epidermal hyperproliferation, skin barrier dysfunction, and immune dysregulation, with a complex pathogenesis. This study aimed to investigate the potential molecular mechanisms underlying the therapeutic effects of traditional Chinese medicine (TCM) compound prescriptions for psoriasis. The psoriasis transcriptomic dataset GSE14905 was downloaded from the Gene Expression Omnibus (GEO) database. Psoriasis-related genes and TCM compound prescriptions for psoriasis were collected. Differentially expressed gene (DEG) analysis and functional enrichment analysis, Integrated Pharmacology and Efficacy Testing Model (InPETM) drug analysis, drug-likeness evaluation, key gene screening, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, molecular docking, and molecular dynamics simulations were performed. A total of 1969 DEGs were identified and enriched in psoriasis-related pathways, including the TNF signaling pathway. Four core herbs (Bai Zhu, Gan Cao, Huang Qi, Ku Shen) and four key active compounds (Succinic Acid, Quercetin, Linoleic Acid, Kaempferol) were screened. Key genes (IL6, IL1B, MMP9, JUN) targeted by these compounds were jointly enriched in immune-related signaling pathways, such as T-cell receptor signaling pathway, and correlated with differentially infiltrated immune cell subsets, including activated CD4⁺ memory T cells and M1 macrophages. Molecular docking and molecular dynamics simulations suggested favorable and relatively stable binding interactions between Quercetin/Kaempferol and proteins encoded by MMP9, IL6, and IL1B. Core herbs and their key active compounds, especially Quercetin and Kaempferol, may be associated with potential anti-psoriatic effects through interactions with IL6, IL1B, MMP9, and JUN, and their involvement in immune, inflammatory, and epidermal proliferation-related pathways.
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3. Phytochemical profiling and mechanistic insights into the anxiolytic potential of Sphaeranthus indicus Linn.: A network pharmacology, molecular docking and simulation approach.
PMID:日期:2026-12-01Anxiety problems, such as Generalized Anxiety Disorder, are a significant source of concern for people worldwide. Current medicines frequently have side effects or are ineffective. Sphaeranthus indicus Linn, often known as Mundi, is an Ayurvedic medication that has demonstrated promising results to aid with anxiety. However, the molecular mechanism underlying its function is not known. In this study we used computational tools to understand how Sphaeranthus indicus Linn might assist persons with anxiety disorders. A library of 57 phytochemicals was compiled from databases and literature sources and was tested for BBB permeability, ADMET and toxicity. Subsequently a network was built to link the compounds with their targets and anxiety related diseases. Upon investigation of the network, it was discovered that eight phytochemicals including Eugenol, β-sitosterol, Estragole, and Camphor interacted with several targets, while four targets seemed to be most important, the serotonin transporter (SLC6A4), the glucocorticoid receptor (NR3C1), acetylcholinesterase (ACHE) and the dopamine D2 receptor (DRD2). Docking of the eight phytochemicals was performed with all four targets, which revealed that β-sitosterol docked best to all targets, while several phytochemicals exhibited favourable binding. Further, 100 ns atomistic simulations were performed for β-sitosterol with all four targets, which indicated stable binding. Thus, we conclude that these phytochemicals probably work as serotonin reuptake inhibitors by blocking the serotonin transporter, probably modulate dopamine signalling as well by interacting with dopamine D2 receptor. We also predict that these phytochemicals interact with the acetylcholinesterase and the glucocorticoid receptor, suggesting their potential to modulate neurotransmission and glucocorticoid signalling that may influence neurotransmission and stress-related signalling. Taken together, the findings from this work, provide the first network pharmacology-based evidence for S. indicus' multitarget anxiolytic processes, support its traditional use in neuropsychiatric diseases, and identify various phytochemicals as promising natural lead candidates for anxiolytic medication development.
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4. Dual targeting of VEGFR2 and ErbB3 by a novel quinoline derivative E5: Mechanistic insights from DFT, molecular docking and MD simulations.
PMID:日期:2026-12-01Acquired resistance to anti-angiogenic therapy often arises via erb-b2 receptor tyrosine kinase 3 (ErbB3)-mediated compensatory signalling, necessitating dual vascular endothelial growth factor receptor 2 (VEGFR2)/ErbB3 inhibitors that can simultaneously block both pathways to overcome resistance. Herein, we adopted a multi-scale computational strategy integrating density functional theory (DFT), molecular docking, and molecular dynamics (MD) simulations to screen and characterize dual-target candidates from our in-house compound library. A novel quinoline derivative, 7-(benzyloxy)-N-(3-chloro-2-fluorophenyl)-6-methoxyquinolin-4-amine (E5), was successfully identified as a potent dual-target inhibitor with favourable dual-target binding potency. Experimental validation using in vitro kinase assays confirmed that E5 potently inhibits VEGFR2 and ErbB3, with IC values of 0.42 μM and 0.04 μM, respectively. Our DFT calculations revealed that E5 adopts a flat conformation (20.05 × 10.61 × 6.02 ų) with balanced electrostatic potential and the lowest LUMO energy (-0.88 eV) among reference compounds, enabling it to adapt to the distinct ATP-binding pockets of VEGFR2 and ErbB3 with different gatekeeper residues. High-precision molecular docking identified target-specific binding modes: van der Waals and hydrophobic interactions dominate in the E5-VEGFR2 association, while ErbB3 engages with E5 via hydrogen bonding to THR768, sulfur-π interaction with CYS721, and π-π stacking with PHE834. 200 ns all-atom MD simulations further confirmed the stability of E5-VEGFR2 and E5-ErbB3 complexes, with MM/PBSA binding free energies of -32.41 kcal/mol (VEGFR2) and -31.22 kcal/mol (ErbB3), both predominantly contributed by van der Waals interactions. This work demonstrates the reliability and efficiency of computational approaches for the rational screening and discovery of dual-target kinase inhibitors, highlighting E5 as a promising lead compound that warrants further investigation to address clinical anti-angiogenic therapeutic resistance.
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5. Mechanistic insights into Ginkgo biloba leaves against cisplatin-induced ototoxicity: An approach combining network pharmacology and computer simulation.
5. 银杏叶对抗顺铂诱导的耳毒性的机制见解：一种结合网络药理学和计算机模拟的方法PMID:日期:2026-12-01Although Ginkgo biloba leaves (GBLs) extract exhibits considerable therapeutic potential for alleviating cisplatin-induced ototoxicity (CIO), its exact molecular mechanisms remain unclear. In this study, we investigated the potential therapeutic mechanisms of GBLs against CIO by integrating network pharmacology with molecular docking and molecular dynamics simulations. Active components and potential targets of GBLs were identified through the TCMSP, SEA, and SuperPred databases. CIO-related disease targets were obtained using GenCLiP 3 and CTD databases. Common targets associated with both the active components and CIO were determined using the Venny tool. Through network construction and analysis, the major active components and key targets were identified. Gene enrichment analysis was performed using the DAVID platform. The binding activity between the major active components and key targets was evaluated using molecular docking. The stability of the binding conformations of the high-affinity complexes was validated using molecular dynamics simulations. Pharmacokinetic properties of the major active components were comprehensively evaluated using SwissADME. In total, 26 active components from GBLs and 105 intersecting targets were identified. The main active components of GBLs against CIO were quercetin, luteolin, genkwanin, kaempferol, chrysoeriol, ginkgolide B, and bilobalide. The key targets included HIF1A, HSP90AA1, MTOR, NFKB1, STAT3, and TNF. Gene enrichment analysis revealed that the otoprotective effects of GBLs were primarily involved in the inflammatory response, positive regulation of apoptotic process, and response to hypoxia, and were closely associated with the HIF-1, sphingolipid, IL-17, Toll-like receptor, PI3K-Akt, and NOD-like receptor signaling pathways. Molecular docking analysis demonstrated strong binding affinities between the main active components and key targets. Molecular dynamics simulations indicated that both the ginkgolide B-MTOR and luteolin-TNF complexes maintained stable binding conformations. Most of the main active components exhibited favorable pharmacokinetic properties. This study highlights the therapeutic potential of GBLs in alleviating CIO and clarifies the possible molecular mechanisms involved. However, these findings remain merely predictive and require validation through in vitro and in vivo experiments.
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6. A cross-platform validated biomarker signature for mucoepidermoid carcinoma identified by integrated WGCNA and machine learning.
PMID:日期:2026-12-01Mucoepidermoid carcinoma (MEC), the most prevalent malignant salivary gland cancer, remains incompletely characterized at the molecular level. An integrated analytical pipeline was employed, combining differential expression analysis, weighted gene co-expression network analysis (WGCNA), resampling-based stability selection, machine learning, and cross-platform validation to identify robust diagnostic biomarkers for MEC. Two public microarray datasets (GSE169753 and GSE262344) were harmonized to form a discovery cohort of 49 samples (39 MEC and 10 normal salivary gland tissues) comprising 19,565 genes. Candidate biomarkers were identified by intersecting WGCNA hub genes with differentially expressed genes identified within the same 8,000-gene expression subset and further refined through 100 resampling iterations, retaining genes selected in at least 60% of the iterations. Elastic-net, random forest, and linear support vector machine models were trained, and the final gene signature was externally validated in an independent RNA-seq cohort (GSE282430). Eleven genes were identified: HTN3, MUCL1, GPR45, PLIN5, PAIP2B, PART1, CD109, PIP, ABCC6, CSN1S1, and KLK1. This signature demonstrated strong discrimination between MEC and normal salivary gland tissue and showed good cross-platform reproducibility in an independent RNA-seq cohort. Functional enrichment analyses revealed downregulation of pathways associated with normal sensory and secretory salivary functions in MEC. These results support the potential diagnostic utility of an 11-gene biomarker panel for MEC.
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7. Integrative bioinformatics, machine learning, and molecular docking identify HigBA toxin-antitoxin systems as key mediators of quorum sensing in Acinetobacter baumannii.
7. 综合生物信息学、机器学习和分子对接确定HigBA毒素-抗毒素系统是鲍曼不动杆菌群体感应的关键介质PMID:日期:2026-12-01Acinetobacter baumannii is a high-priority pathogen due to its extensive antimicrobial resistance and persistence in clinical environments. Quorum sensing (QS) and toxin-antitoxin (TA) systems regulate virulence and stress tolerance, yet their interconnection remains unclear. We analyzed transcriptomic data (GSE87009) from A. baumannii treated with 3-oxo-C12-HSL using WGCNA, differential expression, and machine learning (LASSO and Random Forest). To identify potential inhibitors of the HigBA TA system, we performed molecular docking of 202 chemically diverse compounds - comprising approved drugs, natural products, and synthetic molecules - against the four HigBA proteins using AutoDock Vina, followed by Protein-Ligand Interaction Profiler (PLIP)-based interaction profiling to validate binding modes. WGCNA identified Module 1 (10 genes) as the primary QS-responsive module (r = -0.996, p = 0.0041), containing two complete HigBA systems (chromosomal and plasmid-borne), proteases, transposases, and a catalase. LASSO and Random Forest converged on the four TA genes as robust QS predictors. Docking identified Ligand 17 as the best binder for HigB2 (-7.65 kcal/mol), followed by Ligand 147 for HigA2 (-6.61 kcal/mol), Ligand 198 for HigB1 (-6.79 kcal/mol), and Ligand 178 for HigA1 (-4.51 kcal/mol). PLIP validation confirmed that all ligands occupy the ATP-binding cleft; Deslanoside (147) forms 23 hydrogen bonds, Meclizine (198) anchors via CYS83, while Promazine (178) and Estradiol (17) rely on hydrophobic contacts. These findings provide a mechanistic link between QS and TA systems, thus providing new therapeutic targets for tackling virulence and persistence.
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8. Scaffold-aware benchmarking of GCN and GIN for DFT-derived molecular Gibbs energy in traditional Chinese medicine molecules.
PMID:日期:2026-12-01Absolute gas-phase molecular Gibbs energy G is an extensive quantum-chemical quantity that can be dominated by elemental composition, yet graph-model benchmarks often omit a composition control. This study established a composition-controlled, scaffold-aware benchmark for 2089 traditional Chinese medicine molecular records to compare four model tracks under identical held-out frameworks. The records, representing 2071 unique PubChem CIDs and 1986 distinct canonical SMILES, were audited for repeated structures, stereochemical information and ORCA target provenance. One Bemis-Murcko partition was shared by ordinary least-squares regression on explicit-hydrogen element counts (EC-LR), a five-layer graph convolutional network (GCN), a five-layer graph isomorphism network (GIN), and ordinary least-squares regression on binary Morgan fingerprints (FP-LR). Fold-level statistics were summarized as unweighted means and sample standard deviations, whereas pooled diagnostics used one out-of-fold prediction per record and model. Direct output comparison matched 2084 of 2089 values exactly to the ORCA Final Gibbs free energy field in E; all 2089 values were retained unchanged and analyzed identically. EC-LR achieved R² = 0.9998 ± 0.0003 and MAE = 0.189 ± 0.229 E, showing that absolute G is composition dominated. GCN was more accurate than GIN and FP-LR, but EC-LR remained superior. Out-of-fold atom feature occlusion and node deletion characterized GCN sensitivity without assigning causal thermodynamic contributions. The results support an audited control framework rather than a new architecture and do not establish that graph learning is necessary or universally superior. More discriminating studies should examine atomization, formation or reaction free energies, or predict residuals after removal of an explicit composition baseline.
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9. Rule-based labels versus experimental endpoints: A systematic assessment of generalisation in QSAR models for ADMET prediction using fruit phytochemicals.
PMID:日期:2026-12-01Computational ADMET assessment is now a cornerstone of early drug discovery, yet QSAR classifiers are sometimes trained on physicochemical rule-derived labels rather than measured biological data. This practice raises a fundamental question: do strong internal performance statistics actually reflect predictive accuracy against experimental endpoints? To address this directly, we built Random Forest (RF) and XGBoost (XGB) classifiers for gastrointestinal (GI) absorption and blood-brain barrier (BBB) permeation using 995 fruit phytochemicals from 29 species, annotated with binary labels derived from published physicochemical thresholds; the same descriptors used to generate these labels were also supplied as model inputs, a design feature discussed further below. Stratified 80:20 partitioning was applied, and SMOTE oversampling was applied after splitting and restricted to the training fold to prevent data leakage. Hyperparameters were optimised by GridSearchCV with stratified five-fold cross-validation. All four model configurations attained essentially ceiling-level internal discrimination: AUC-ROC = 1.000 and MCC ≥ 0.980. Applying these models to an independent ChEMBL experimental dataset - Caco-2 apparent permeability values (n = 117) for GI absorption and logBB measurements (n = 102) for BBB permeation - caused AUC values to fall to 0.606-0.684 and MCC to 0.206-0.409, representing generalisation gaps of 0.32-0.39 AUC units. Y-randomisation (20 permutations, ΔAUC ≈ 0.50) indicated that the models encode a non-random structure-property signal rather than spurious correlation. A three-arm feature-representation comparison (physicochemical descriptors only, Morgan fingerprints only, and the combined representation used elsewhere in this study) found comparably near-ceiling internal performance across all three arms, indicating that the internal performance ceiling reflects the deterministic structure of the rule-derived labels rather than direct access to the label-generating descriptors specifically. A 95th-percentile Euclidean-distance applicability domain analysis showed complete structural coverage of the external compounds, indicating that structural out-of-domain extrapolation is unlikely to explain the observed gaps. These gaps are most plausibly attributed to the mismatch between computationally derived training labels and experimentally measured biological transport, although assay heterogeneity, transporter-mediated mechanisms, and other sources of biological complexity discussed in Section 4 are likely additional contributors. Topological polar surface area consistently emerged as the dominant predictive descriptor for both endpoints. These findings, drawn from a single dataset and two endpoints, support the case that external experimental validation deserves explicit attention in computational ADMET studies that rely on rule-derived training labels.
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10. Comparative genomics of ESKAPE pathogen species: Integrating pan-genome architecture, antimicrobial resistance, and virulence factor repertoires.
PMID:日期:2026-12-01ESKAPE pathogens are major causes of hospital-acquired infections and are characterized by extensive antimicrobial resistance (AMR) and diverse virulence mechanisms. Although species-specific pan-genome studies have revealed substantial genomic diversity, the relationships among genome plasticity, resistance burden, and virulence remain incompletely understood across the ESKAPE complex. We analyzed 120 high-quality genomes representing six single-species ESKAPE groups (20 genomes per species). Genome quality was assessed using CheckM2. Species-specific pan-genomes were constructed with Roary, AMR genes were identified using AMRFinderPlus, and virulence factors were detected against the VFDB database using DIAMOND. AMR genes were mapped to core and accessory genome compartments through integration of Prokka annotations and Roary outputs. Statistical associations were evaluated using Fisher's exact tests and correlation analyses, with false discovery rate correction applied within each test family. Core-genome maximum-likelihood phylogenies were reconstructed to provide an evolutionary framework. Pan-genome sizes ranged from 4720 to 17,272 genes, with Enterobacter and Pseudomonas possessing the largest accessory genomes. Multidrug resistance (MDR; resistance to ≥3 antimicrobial classes) was detected in 93.3% of strains. After false discovery rate correction, AMR genes remained significantly enriched in the accessory genomes of Enterobacter, Enterococcus, Klebsiella, and Staphylococcus, whereas Acinetobacter and Pseudomonas did not show significant enrichment in either genome compartment. Within-species analyses identified significant positive associations between accessory genome size and AMR class burden in Staphylococcus, Enterococcus, and Enterobacter, whereas the moderate Pearson correlation observed in Pseudomonas was not significant after FDR correction. Virulence factor repertoires varied markedly among species, with Pseudomonas exhibiting the highest burden and Enterococcus the lowest. ESKAPE pathogens display distinct patterns of resistance and virulence. Accessory genome expansion was associated with higher AMR burden in several species, whereas other species showed no significant association between accessory genome size and AMR burden and no significant enrichment of AMR genes in either genome compartment, highlighting the species-specific nature of AMR evolution.