{"doi":"10.1093/bib/bbaa260","title":"MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs","abstract":"Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.","journal":"Briefings in Bioinformatics","year":2020,"id":98088,"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":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9567,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":238467,"name":"Maozi Chen","orcid":"0000-0002-0763-6963","position":1,"is_corresponding":false},{"id":484559,"name":"Ying Xue","orcid":"0000-0002-3053-5837","position":2,"is_corresponding":false},{"id":444454,"name":"Tianjian Liang","orcid":null,"position":3,"is_corresponding":false},{"id":483589,"name":"Hui Chen","orcid":"0000-0001-5462-8029","position":4,"is_corresponding":false},{"id":444456,"name":"Yuehan Zhou","orcid":null,"position":5,"is_corresponding":false},{"id":363543,"name":"Thomas D. Nolin","orcid":"0000-0003-4339-1382","position":6,"is_corresponding":false},{"id":484560,"name":"Randall B. Smith","orcid":"0000-0003-4262-4279","position":7,"is_corresponding":false},{"id":238473,"name":"Xiang‐Qun Xie","orcid":"0000-0002-6881-6175","position":8,"is_corresponding":false},{"id":238465,"name":"Zhiwei Feng","orcid":"0000-0001-6533-8932","position":0,"is_corresponding":true}],"reference_count":63,"raw_metadata":null,"created_at":"2026-07-18T22:36:34.157932Z","pmid":"33078827","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":[]}