{"doi":"10.3390/ph18091323","title":"Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology","abstract":"<jats:p>Background/Objectives: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood–brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing LIMK1 inhibitors through an integrated computational strategy. Methods: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure–Random Forest) achieved R2_train = 0.8747 and R2_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. Results: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (−6.68, −6.25, and −5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including MAPK1, PRKCB, HDAC1, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. Conclusions: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation.</jats:p>","journal":"Pharmaceuticals","year":2025,"id":601529,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1542410,"name":"Abida Khan","orcid":null,"position":1,"is_corresponding":false},{"id":1542412,"name":"Mashael N. Alanazi","orcid":null,"position":2,"is_corresponding":false},{"id":1542414,"name":"Naira Nayeem","orcid":null,"position":3,"is_corresponding":false},{"id":1542416,"name":"Hayet Ben Khaled","orcid":null,"position":4,"is_corresponding":false},{"id":1542418,"name":"Mohd Imran","orcid":"0000-0002-6064-1040","position":5,"is_corresponding":false},{"id":1542409,"name":"Saleh I. Alaqel","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology","abstract":"<jats:p>Background/Objectives: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood–brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing LIMK1 inhibitors through an integrated computational strategy. Methods: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure–Random Forest) achieved R2_train = 0.8747 and R2_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. Results: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (−6.68, −6.25, and −5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including MAPK1, PRKCB, HDAC1, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. Conclusions: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41011194","pmcid":"PMC12472226","openalex_id":"https://openalex.org/W4413993341","authors":[],"funders":[{"funder_name":"King Salman Center For Disability Research","grant_id":"KSRG-2024-231","title":null}],"total_grants":1,"fwci":0.0,"citation_percentile":0.21119762,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/1424-8247/18/9/1323/pdf?version=1756970880","host_type":"journal"},{"url":"https://www.mdpi.com/1424-8247/18/9/1323/pdf?version=1756970880","host_type":"publisher"},{"url":"https://www.mdpi.com/1424-8247/18/9/1323/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/ph18091323","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41011194","host_type":"repository"},{"url":"https://doaj.org/article/a89885b7229d44b79338aead25a40d78","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12472226","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12472226/","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12472226","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12472226?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Computational Drug Discovery Methods","S100 Proteins and Annexins","Cell Image Analysis Techniques"],"mesh_terms":[],"keywords":["Cheminformatics","Repurposing","Drug repositioning","Pharmacology","Medicine","Ischemic stroke","Cofilin","Bioinformatics","Computational biology","Biology","Internal medicine","Drug","Ischemia","Cell","QSAR","Stroke","Drug Repurposing","Limk1","Network Pharmacology"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"uniprot"},{"name":"chembl"},{"name":"pdb"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T16:38:38.624716Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}