In an article published in Nature Communications, the researchers describe how they trained PeptiVerse using a wide range of data sets, allowing it to predict properties that can help determine whether a peptide is worth pursuing as a potential drug, such as the peptide's likelihood of dissolving, entering cells, avoiding toxicity and lasting long enough in the body to have an effect.

While tools for predicting such properties exist, those tools often focus on a narrower set of traits or only one kind of peptide. PeptiVerse, by contrast, brings many of those predictions together in one open-source, easily accessible platform, allowing users to evaluate both ordinary peptides and chemically modified versions designed to work better as drugs.

"Peptide drugs have enormous potential, but binding to the right target is only one part of what makes a molecule useful," says Pranam Chatterjee, Africk-Lesley Distinguished Scholar of Innovation in Engineering and assistant professor of bioengineering (BE) and computer and information science (CIS), and senior author of the new study.

"In drug discovery, one of the worst outcomes is finding out too late that a promising molecule cannot actually become a medicine," says Chatterjee. "PeptiVerse gives researchers a way to check many of those make-or-break properties earlier, before they invest the time and resources required to synthesize and test candidate drugs."