Current Computer - Aided Drug Design - Online First

There has been increasing interest in neuroimaging studies in recent years, and computer-aided approaches have gained prominence in improving diagnostic accuracy. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Traditional diagnostic approaches often rely on subjective assessments, highlighting the need for more objective, data-driven methods. This study aims to classify ADHD subtypes and assess medication effects by converting resting-state fMRI images into one-dimensional (1D) signals and extracting statistical features using Variational Mode Decomposition (VMD).

Resting-state fMRI data from the ADHD-200 dataset, including 41 healthy controls (HC), 41 medicated ADHD-Combined (ADHD-C) individuals, and 41 non-medicated ADHD-C individuals, were analyzed. The 1D fMRI signals were decomposed into nine sub-bands using VMD. Statistical features were extracted from each sub-band and classified using Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN).

VMD-derived features substantially improved classification performance. The highest binary classification accuracy was achieved by LDA: 96.34% distinguishing non-medicated ADHD from controls and 88.41% for medicated ADHD versus controls. The classification between medicated and non-medicated ADHD yielded 79.63% accuracy. Ternary classification across all groups reached 69.51% accuracy.

These findings show that the VMD-based approach improves the classification of ADHD subtypes and helps evaluate medication effects. However, the lower performance in multi-class classification reflects the complexity of ADHD neuroimaging data.

The VMD-based approach improves classification accuracy, especially in distinguishing ADHD subtypes and medication effects, supporting its potential as an objective tool for diagnosis and treatment planning.

Hyperlipidemia is linked to multiple cardiovascular and cerebrovascular diseases. Traditional Chinese Medicine formulations show potential for managing this condition, but the underlying mechanisms remain unclear. This study investigates the therapeutic effects of the Fangji-Astragalus (FJ-HQ) on hyperlipidemia and explores its key components and molecular pathways.

Network pharmacology was applied to identify active ingredients in FJ-HQ and drug-disease co-targets. Transcriptomic analysis and HPLC-MS/MS were integrated to screen core components and associated targets. In vivo and in vitro experiments evaluated the effects of FJ-HQ in hyperlipidemic rat models and cell models.

A total of 23 active ingredients and 109 drug–disease co-targets were identified, with enrichment in inflammatory and signaling pathways, notably the PI3K/AKT/mTOR and p53 pathways. Transcriptomic profiling revealed seven differentially expressed targets. Integrated chemical and serum analysis identified calycosin as the core component and highlighted CAMTA2 and RXRA as downstream targets. In hyperlipidemic rats, FJ-HQ lowered total cholesterol, triglycerides, and low-density lipoprotein cholesterol, and increased high-density lipoprotein cholesterol and apolipoprotein A1. FJ-HQ also modulated the expression of P53, AKT1, and IL6, as well as mRNA levels within the PI3K/AKT/mTOR pathway. In cell models, serum containing FJ-HQ inhibited lipid droplet formation.

These findings demonstrate that FJ-HQ alleviates hyperlipidemia by modulating the PI3K/AKT/mTOR and p53 pathways, reducing lipid levels, and suppressing lipid droplet formation, with calycosin as a pivotal active component.

In summary, our study confirms the therapeutic effects of FJ-HQ on hyperlipidemia and identifies calycosin as a crucial component. Furthermore, we have experimentally validated the influence of FJ-HQ on the PI3K/AKT/mTOR signaling pathway. These findings highlight the potential of FJ-HQ as an effective lipid-lowering agent and provide preclinical evidence for future treatments of hyperlipidemia.

Inflammatory bowel disease (IBD) poses a major threat to human health. Current pharmacological therapies primarily manage symptoms and are often associated with adverse effects.

To develop targeted natural drugs with fewer side effects for IBD therapy by identifying potential agents from medicinal and edible Chinese herbs (MECHs) and clarifying their underlying molecular mechanisms.

An integrated approach was employed, combining single-cell analysis, transcriptomics, reverse network pharmacology, immunological infiltration assessment, molecular docking, ADMET evaluation, and molecular dynamics (MD) simulations.

Multi-omic integration identified nine differentially infiltrating immune cell types and a CXCL8-CXCR2-driven neutrophil communication axis. Frequent intercellular communication was observed among neutrophils, epithelial cells, monocytes, B cells, and T cells. Topological screening yielded 15 hub targets and identified MMP2 and PTGS2 as key targets. Molecular docking, ADMET analyses, and 100-ns MD simulations converged on the natural product (NP) MOL009551 (isoprincepin) as a high-affinity, stable MMP2 binder (ΔG = -11.0 kcal/mol), supporting MMP2-directed isoprincepin as a novel therapeutic candidate for IBD.

Bioinformatic analyses suggest that MMP2 may play an important role in IBD, and isoprincepin, identified from MECHs, may serve as a potential therapeutic agent by modulating MMP2 activity. However, experimental validation of their direct interaction and therapeutic efficacy remains necessary, along with further mechanistic and preclinical studies to clarify their potential for IBD treatment.

This study provides a comprehensive understanding of the molecular mechanisms underlying IBD, identifies MMP2 as a key target, and highlights isoprincepin as a promising natural product for IBD therapy.

This study aimed to investigate the therapeutic mechanism of coptisine in rotator cuff injury (RCI) through network pharmacology and experimental validation. This is the first study to examine the role of coptisine in rotator cuff injury (RCI), revealing a novel mechanism by which coptisine inhibits the PI3K/Akt/mTOR pathway, thereby coordinating inflammation resolution and tendon repair.

Network pharmacology was used to identify potential coptisine and RCI targets, which were then analyzed functionally to indicate critical pathways. A rat RCI model (right supraspinatus tendon transection) was used to validate the mechanism by detecting pathological changes, inflammatory factors, and mRNA expression related to the PI3K/Akt/mTOR pathway.

Network pharmacology identified 29 overlapping coptisine and RCI targets, with an emphasis on the PI3K/Akt/mTOR pathway. Coptisine reduced tendon atrophy and inflammation in RCI rats, lowered blood TNF-α and IL-6 levels, elevated IL-10, and decreased PI3K, Akt, and mTOR mRNA expression in tendon tissues. These findings align with the pathway-target connection predicted by network pharmacology-specifically, core targets like PIK3CA and PIK3CB (key components of the PI3K/Akt/mTOR pathway) were confirmed to be regulated by coptisine, suggesting the alkaloid exerts anti-inflammatory and tendon-protective effects by suppressing this pathway, which is known to mediate inflammation and protein metabolism in injured tendons.

Coptisine improved RCI in rats by decreasing inflammation and the PI3K/Akt/mTOR pathway, suggesting a possible therapeutic target for RCI.

Penicillin G Acylase (PGA) plays a central role in the synthesis of β- lactam antibiotics. While certain variants have been extensively studied, their catalytic efficiency remains suboptimal for industrial application, necessitating further enzyme engineering to enhance substrate binding and reaction kinetics. This study aims to rationally design and engineer PGA variants with improved catalytic efficiency and stability toward β-lactam antibiotics, using an integrated approach of 4D QSAR modeling and neural network-guided mutation prediction.

A dataset of 30 enzyme-substrate complexes involving three PGA variants and diverse β-lactam substrates was compiled. Ten complexes were randomly selected for external validation. The binding conformation of Cefotaxime to a Bacillus thermotolerans PGA variant was used as a reference for molecular docking and structural alignment. Binding site analyses identified optimal substrate orientations, followed by 4D grid-based energy profiling, which revealed 15 high-energy hotspot residues per variant. These positions were systematically mutated in silico, generating 1130 variants through a neural network-based residue substitution algorithm.

Subsequent docking studies with Cefotaxime showed a strong positive correlation between predicted docking energies and Ki values derived from the 4D QSAR model, validating the model's predictive capability. Molecular dynamics simulations (2 × 100 ns) for selected variants, particularly Sequence Id_0, Id_2, Id_5, and Id_7, demonstrated stable binding interactions and favourable atomic distances, indicative of improved substrate affinity.

In Sequence Id_11, the hotspot is Phe148. Chain A showed the best results with Val and Leu as single mutants, followed by Met56 in Chain B with Leu, and Ser144 in Chain A with Glu, Ala, Ile, and Arg. In the case of Sequence Id_03, the hotspot is Phe147. Chain A showed good results with Ala, Lys, Thr, and Ser, whereas Tyr71 in Chain B showed good results with Glu, Lys, and Thr, and Arg266 in Chain B showed good results with Ala, Thr, and Val. Those that showed the highest sum of docking scores and Ki were chosen for further studies.

The study highlights the critical role of residue Phe148 in mediating stable interactions with Cefotaxime and other β-lactam substrates. The integrated computational strategy establishes a robust framework for engineering catalytically superior PGA variants, offering a valuable basis for further experimental validation and application in antibiotic biosynthesis.

DNA methyltransferase 1 (DNMT1) has recently emerged as a potential therapeutic target for diabetic wound healing (DWH) Studies have shown that inhibition of DNMT1 may be valuable in accelerating DWH

Virtual screening of 3,646 phytochemicals derived from the IMPPAT database was performed against DNMT1. This was followed by exhaustive docking ADMET analysis and molecular dynamics simulation to identify potential phytochemical inhibitors of DNMT1

Out of the 17967 phytochemicals present in the database 3646 of them were chosen for fast screening based on their drug-likeness properties. When compared with the reference compound over 2500 compounds exhibited lower binding energies. The top 972 compounds having binding energies ≤ 8.7 kcal/mol were chosen and 40 out of 972 compounds passed through the ADMET filters. These were then subjected to molecular docking and the compound with the least binding energy and favourable hydrogen bonding was then selected for molecular dynamics simulation. The stability of the Oroxindin-DNMT1 complex was further validated by molecular dynamics simulation studies

Derived from the traditional Chinese remedy Huang-Qin Oroxindin has been shown to possess a range of pharmacological effects including anti-inflammatory antitumor and antioxidant properties. The wound-healing potential of Oroxindin has to be evaluated in vitro and in vivo for further validation

Oroxindin emerged as the ideal phytochemical among the 3,646 screened The ability of Oroxindin to accelerate DWH still needs to be evaluated in vitro and in vivo for further validation

The white water lily (Nymphaea alba) is a traditional medicinal plant recognized for its diverse array of bioactive properties. However, its potential in wound healing remains largely unexplored. This study aimed to evaluate the phytochemical profile, cytotoxicity, and wound healing efficacy of Nymphaea alba flower extract (NAFE) using both in vitro and in vivo models, as well as computational network analysis.

Qualitative phytochemical screening of NAFE was conducted using standard techniques. Cytotoxicity was assessed on HaCaT keratinocyte cells at concentrations ranging from 0 to 1000 µg/ml. In vivo wound healing was evaluated using excision wound models in Wistar albino rats treated with 2.5% and 5% NAFE ointments, measuring wound contraction, epithelialization time, and breaking strength. In vitro scratch assays were used to assess cell migration at selected concentrations of NAFE. A wound-healing-associated network analysis was performed using IMPPAT, STRING, GeneCards, and OMIM databases to explore the molecular targets and interactions of bioactive compounds.

Phytochemical analysis confirmed the presence of alkaloids, flavonoids, phenolics, tannins, and glycosides. NAFE was found to be non-cytotoxic with an IC50 of 245 µg/ml. In vivo, 5% NAFE ointment showed 98.92% wound closure by day 14 and complete closure by day 21, comparable to betadine. Epithelialization time (15.83±0.16 days) was nearly equivalent to the standard drug. In vitro assays demonstrated enhanced HaCaT cell migration at concentrations of 122.5 and 245 µg/ml. Network analysis identified kaempferol and quercetin as key compounds interacting with wound-healing proteins, notably AKT1, ESR1, and EGFR.

The findings suggest that NAFE promotes wound healing by enhancing wound contraction, epithelialization, and cell migration, likely through the modulation of molecular pathways involved in tissue repair. The presence of bioactive compounds such as kaempferol and quercetin underpins the extract's pharmacological potential.

Nymphaea alba flower extract exhibits promising wound-healing activity through multiple mechanisms, including enhancement of cell migration and regulation of key proteins involved in tissue regeneration. These results support its potential as a natural therapeutic agent in wound management.

Ovarian cystadenoma (OC) is a common benign tumor in women. Wang’s formula for gynecological masses (WGM), a patented traditional Chinese medicine, was reported to have therapeutic potential for OC.

Here, we explored the pharmacological effects of WGM on treating OC via network pharmacology, molecular docking, and molecular dynamics simulations. The active ingredients in WGM and their putative targets were acquired from the TCMSP and BATMAN-TCM platforms. The known therapeutic targets of OC were obtained from the DrugBank, OMIM, and GeneCards databases. GO and KEGG analyses of the overlapping targets were performed via the DAVID database. Molecular docking and molecular dynamics (MD) simulations were conducted to evaluate the binding efficacy of the chemical ingredients to the core targets.

In total, 287 chemicals in WGM may relieve OC by targeting 134 genes involved in malignant tumors, endocrine resistance, and oxidative stress, of which ERBB2, ESR1, and AKT1 play vital roles. Molecular docking revealed stable binding energies of the receptors to the ligands, which bond via electrostatic interactions and van der Waals interactions in MD simulations.

The in silico bioinformatics analysis revealed the mechanisms of WGM treatment for OC. More pharmacological evidence of WGM treatment for OC, such as in vivo and clinical studies, is needed before WGM can benefit more patients.

Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application.

The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database.

A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses.

This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions.

This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Inflammation is a natural process; however, chronic inflammation may result in numerous health issues. Etoricoxib (ETX), a selective cyclooxygenase-2 (COX-2) inhibitor, serves as an anti-inflammatory agent for various types of arthritis. However, prolonged use of ETX is associated with several adverse effects, including cardiovascular toxicity.

The current research aims to design analogues of ETX having superior pharmacokinetic properties and safer toxicological profiles employing the bioisosteric approach.

The bioisosteres of various groups in ETX were produced utilizing the MolOpt online tool, resulting in the generation of novel ETX analogues. The pharmacokinetics (ADME) and toxicological profiles of the generated analogues were calculated by ADMETLab 3.0 server. The druglikeness (DL) and drugscore (DS) were calculated using OSIRIS property explorer (PEO). The molecular docking analysis of the ETX analogues against the target protein (PDB ID: 5KIR) was carried out using AutoDock Vina, and their results were visualized by Discovery Studio 2021. Molecular dynamics (MD) simulation of the top three complexes was conducted using the Schrödinger suite. Binding free energy for the A098-5KIR, A188-5KIR, and D121-5KIR complexes was conducted using MM-GBSA/PBSA method.

A total of 1200 ETX bioisosteres were produced; among them, 51 were screened on the basis of ADMET profile, DL, and DS scores and selected for the docking study. A docking study revealed that 12 analogues show good interactions and docking scores. Furthermore, the MD simulation of ligands A098, A188, and D121 demonstrated stability throughout the 100 ns simulation period.

The findings of the ADMET study, DL, DS, docking study, MD simulation, and binding free energy calculation indicate that the analogues A098, A188, and D121, which are bioisosteres of ETX, may serve as potential anti-inflammatory agents for inflammation-related disorders.