Abstract
Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.
Highlights
Discovery and design of effective treatments against COVID-19 is an active research field
3.1 Integration Efforts Led to a Curated Dataset of Anticoronavirus Molecules
We looked to obtain an equal number (1,305) of inactive molecules, which were in larger numbers, namely, within large bioassays
Summary
Discovery and design of effective treatments against COVID-19 is an active research field. Tremendous efforts have been deployed worldwide to find new molecules with therapeutic potential against its pathogenic agent SARS-CoV-2 (Song et al, 2021). The most forerunner achievements mainly consisted in drug repurposing attempts of previously described drugs able to affect the SARSCoV such as chloroquine and its derivatives Other antivirals or antibiotics were assessed for their potential as COVID-19 therapeutics (Pillaiyar et al, 2020; Kelleni, 2021). As of today, no candidates have been yet retained as a universal COVID-19 treatment (Hoffmann et al, 2020; Dragojevic Simic et al, 2021). Various approaches were adopted, including computational methods toward a faster discovery of drugs, given the urge of the global sanitary situation
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