Drug discovery and protein engineering are some of the most challenging aspects of cutting-edge scientific research. It is also a field that holds great promise in addressing some of the burning issues in global healthcare. Unfortunately, the intricacies of protein molecule interactions, dynamics, and the effects of mutations are exceedingly painstaking and slow. Researchers from…
In a recent study published in Nature Communications, researchers at the University of Washington, University of California, San Francisco, and University of Michigan, along with others, revisited the concept of drug discovery by using an innovative AI-augmented virtual screening tool. Named RosettaVS, this technology merges machine learning approaches with a physical model to provide excellent…
Improved identification of interactions between proteins and ligands has been invaluable in the progress of computational biology, potentially enhancing drug discovery and revealing more about the molecular world. Traditional approaches tend to utilize structural information, which is sometimes scarce or nonexistent. Enter PSICHIC (PhySIcoCHemICal): a graph convolutional network model, physicochemical and chemotherapeutic interactions in sequence…
In the drug discovery process, high-throughput screening quickly evaluates a wide range of chemical compounds to find hit compounds for certain biological targets. False-positive results, on the other hand, cause delays in hit compound screening and waste money. In order to address this, scientists from Central South University, China, and collaborators have developed ChemFH, an…
A team of researchers utilized HASTEN, a machine-learning (ML) model, to perform virtual screening on ultra-large chemical compound libraries. Several challenges are faced when screening libraries of such large scale, and conventional brute-force docking methods that are usually used have drawbacks, especially for libraries that span nearly a billion compounds. HASTEN is a quick and…
The researchers at Michigan State University in the United States used extensive structural datasets along with a refined and annotated collection of antiviral phytochemicals to determine which naturally derived medicines have the best chance of provoking strong binding interactions with SARS-CoV-2 proteins and thus preventing or disrupting the viral infection process. Millions of people have…
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