A team of Australian researchers led by Monash University has created an AI tool that could make the process of discovering new medicines faster, cheaper and more effective.
PSICHIC, short for PhySIcoCHemICal, is a potentially game-changing AI tool that can rapidly and cost-effectively predict how molecules and proteins interact in our bodies, potentially revolutionising the early stages of drug discovery, according to a study published in Nature Machine Intelligence.
Dr. Lauren May, co-lead author from the Monash Institute of Pharmaceutical Sciences (MIPS), sees great promise in PSICHIC: “There is enormous potential for AI to completely change the drug discovery landscape. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein-molecule interactions.”
So, why should you care?
Well, identifying potential new drugs is an extremely complex and expensive process. One of the key challenges scientists currently face is figuring out how different molecules interact with proteins in the body, as this interaction determines the effectiveness and safety of a drug.
Current methods to predict these interactions often require detailed 3D structures, which can be costly and time-consuming to obtain. Kind of like trying to build a Lego set without the instructions — possible, but a major pain in the arse.
PSICHIC aims to address this challenge by using AI to predict protein-molecule interactions based on sequence data alone — no 3D structures required.
“The application of AI approaches to enhance the affordability and accuracy of drug discovery is a rapidly expanding area,” said data scientist, AI expert and lead author Professor Geoff Webb from Monash’s Department of Data Science and Artificial Intelligence.
Dr. Anh Nguyen, a co-lead author from MIPS, has expertise in AI approaches to drug-receptor interactions.
She stressed the significance of the interplay between molecules and proteins in biological processes and drug development.:Interactions between molecules and proteins underpin many biological processes, with drugs exerting their intended effects by selectively interacting with specific proteins. There have been significant global efforts to develop new AI-based methods to accurately determine how a molecule might behave when it interacts with its protein target — after all, this is the core building block to making medicines.”.
Huan Yee Koh, the study’s first author and a PhD candidate from Monash’s Faculty of Information Technology, explained the rationale behind developing PSICHIC specifically for drug discovery applications.
“AI has the potential to dramatically improve the robustness, efficiency and cost at multiple stages during the drug discovery process, from early stage discoveries right through to predicting clinical responses,” Koh said.
“However, since many AI systems fundamentally rely on pattern matching, these systems can suffer from unrestrained degrees of freedom. This can lead to memorisation of previously known patterns rather than learning the underlying mechanisms of protein-ligand interactions [ligands are small molecules that bind to proteins], ultimately hindering the discovery of novel drugs,” Koh said.
The PSICHIC team has made its data, code and optimised model available to the broader scientific community.
Main image: Getty
