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?:abstract
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Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19 We developed \'REDIAL-2020\', a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata ncats nih gov/covid19/index html), with multiple categorical machine learning algorithms The \'best models\' are combined in an ensemble consensus predictor that outperforms single models where external validation is available This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral org/Redial) Acceptable input formats are: drug name, PubChem CID, or SMILES;the output is an estimate of anti-SARS-CoV-2 activities The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data The ML models have 60% to 74% external predictivity, based on three separate datasets Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment The source code and specific models are available through Github (https://github com/sirimullalab/redial-2020), or via Docker Hub (https://hub docker com/r/sirimullalab/redial-2020) for users preferring a containerized version
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