New AI Tools Promise a Revolution in Peptide-Based Drug Discovery
The future ofโฃ drug development may lie in targeting the incredibly specific building blocks of proteins – peptides. Recent โฃadvancements inโ artificial intelligence are unlocking the โขpotential of these small protein fragments, offering a “universal tool” for creating new therapies, according to researchers in the field.
For years, scientistsโ have sought ways to identify adn utilize peptide binding sites withinโ proteins. Now, โa wave ofโฃ sophisticated โalgorithms is accelerating this โprocess.Researchersโ at the University of Toronto have been at the forefront, developingโค toolsโค likeโค PepNN-Struct and PepNN-Seq, which predict where peptides bind based on a โขprotein’s structure or genetic code. These models feed into PepMLM, further refining โขthe โidentification process.
Building on this foundation,the Toronto team recently unveiled PepFlow,a โขdeep-learningโฃ model capable โof predicting peptide structures themselves.This is crucial as โpeptides are inherently flexible โmolecules, โexisting โin multiple shapes, and โunderstanding these conformations is key to understanding their function and how to best utilize them therapeutically.
“Peptides are vital biological molecules and are naturally dynamic, so we need to model their different conformations to understand their function,” explains Philip M. Kim, PhD,โ professor of molecular genetics and computer science at the University ofโ Toronto, and a leading โขfigure in this research.โ Dr.kim, who holds a Canada Research Chair in machine learning in protein and peptide science, โalso co-developed the PepNN models.
The potential of peptide-based therapies is already evident in successfulโ drugsโ like โขglucagon-likeโฃ peptide-1 analogues, such as Ozempic, which are used to manage โdiabetes and obesity. Though, โขthese new AI tools aimโข to expand the scope โขof treatableโ conditions dramatically.
While these deep-learning models areโค stillโฃ evolving, their promise is driving innovation. Several biotechnology companies have already spun out โfrom the PepMLM research, dedicatedโ to refining these models and applyingโ them to a wider range of diseases.
A key question โคfor the future, Dr. kim notes,is whether these models can accuratelyโ predict peptide behaviorโค in new situations,or if they simply excel at analyzing โฃdata they’ve alreadyโ been โtrained on. Even if the models primarily “interpolate” from existing โdata, that capability alone could be incredibly valuable.
This research wasโ supported by funding from Duke University, cornell University, the CHDIโ Foundation, theโฃ Wallace H. Coulter Foundation, the hartwell Foundation,โฃ the Krembil Foundation,โค and the National Institutes of health. Dr. kim disclosed โคfinancial ties to Fable Therapeutics, TBG Therapeutics, and Zymedi as a cofounder and consultant.
