Speeding New COVID Treatments

Speeding/Improving COVID Treatment with machine learning

Speeding New COVID Treatments with Computational Tool

University of New Mexico, Michael Haederle, May 3, 2021 in CACM

Scientists at the University of New Mexico (UNM) and the University of Texas at El Paso have developed a computational tool to help drug researchers quickly identify anti-COVID molecules before the virus invades human cells or disable it in the early stages of infection. The team unveiled REDIAL-2020, an open source suite of computational models that can help to rapidly screen small molecules for potential COVID-fighting traits. REDIAL-2020 is based on machine learning (ML) algorithms that quickly process massive volumes of data and tease out patterns that might be missed by human researchers. The team validated the ML forecasts by comparing datasets from the National Center for Advancing Translational Sciences to the known effects of approved drugs in UNM's DrugCentral database.

Deep Learning Searches for Covid-19 Treatments

Treatments using deep learning, based on human gene interactions with drugs.

Computer Model Makes Strides in Search for Covid-19 Treatments

Ohio State News, Emily Caldwell, February 1, 2021

A deep learning computer model developed by Ohio State University (OSU) computer scientists to predict human genes' interaction with drugs has identified at least 10 compounds as potential Covid-19 treatments. The DeepCE model taps the L1000 repository of human cell-line data concerning how gene expression changes in response to medications, and the DrugBank dataset on chemical structures and other details of roughly 11,000 approved and investigational drugs. The investigators applied DeepCE's gene expression prediction matrix to the genetic data in early Covid-19 papers and additional government information. Said OSU's Ping Zhang, "We can predict gene expression values for new chemicals not from one cell to one cell, but automatically predict the role of a drug on different cell lines and different genes."