Details

Autor(en) / Beteiligte

• Batra, Rohit
• Chan, Henry
• Kamath, Ganesh
• Ramprasad, Rampi
• Cherukara, Mathew J
• Sankaranarayanan, Subramanian K.R.S

Titel

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

Ist Teil von

• The journal of physical chemistry letters, 2020-09, Vol.11 (17), p.7058-7065

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. Here, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host–virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors (2χ n , topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.