{"doi":"10.3390/molecules28248034","title":"In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling","abstract":"The “Long-COVID syndrome” has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<−6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.","journal":"Molecules","year":2023,"id":366525,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9554,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1123206,"name":"Fengyang Han","orcid":"0000-0002-2775-9717","position":1,"is_corresponding":false},{"id":578640,"name":"Beihong Ji","orcid":"0000-0003-0387-4056","position":2,"is_corresponding":false},{"id":660680,"name":"Xibing He","orcid":"0000-0001-7431-7893","position":3,"is_corresponding":false},{"id":324184,"name":"Luxuan Wang","orcid":"0000-0001-9071-2578","position":4,"is_corresponding":false},{"id":1123207,"name":"Taoyu Niu","orcid":"0009-0004-2214-1397","position":5,"is_corresponding":false},{"id":578639,"name":"Jingchen Zhai","orcid":"0000-0003-2691-8867","position":6,"is_corresponding":false},{"id":86256,"name":"Junmei Wang","orcid":"0000-0002-9607-8229","position":7,"is_corresponding":false},{"id":951171,"name":"Lianjin Cai","orcid":"0000-0001-5354-4050","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-19T01:14:59.372548Z","pmid":"38138524","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":[]}