Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies
Rohit Batra, Henry Chan, Ganesh Kamath, Rampi Ramprasad, Mathew J. Cherukara, Subramanian K. R. S. Sankaranarayanan
Abstract
, 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.
Topics & Concepts
Docking (animal)Virtual screeningComputational biologyDrug discoverySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Computer scienceFidelityMachine learningArtificial intelligenceChemistryBioinformaticsBiologyMedicineInfectious disease (medical specialty)DiseasePathologyNursingTelecommunicationsComputational Drug Discovery MethodsSynthesis and biological activitythermodynamics and calorimetric analyses