SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery

May 1, 2024· Bum Chul Kwon , Simona Rabinovici-Cohen , Beldine Moturi , Ruth Mwaura , Kezia Wahome , Oliver Njeru , Miguel Shinyenyi , Catherine Wanjiru , Sekou Remy , William Ogallo , Itai Guez , Partha Suryanarayanan , Joseph Morrone , Shreyans Sethi , Seung-Gu Kang , Tien Huynh , Kenney Ng , Diwakar Mahajan , Hongyang Li , Matan Ninio , Shervin Ayati , Efrat Hexter , Wendy Cornell
Abstract
Biomedical foundation models, trained on diverse sources of small molecule data, hold great potential for accelerating drug discovery. However, their complex nature often presents a barrier for researchers seeking scientific insights and drug candidate generation. SPARK addresses this challenge by providing a user-friendly, web-based interface that empowers researchers to leverage these powerful models in their scientific workflows. Through SPARK, users can specify target proteins and desired molecule properties, adjust pre-trained models for tailored inferences, generate lists of potential drug candidates, analyze and compare molecules through interactive visualizations, and filter candidates based on key metrics (e.g., toxicity). By seamlessly integrating human knowledge and biomedical AI models’ capabilities through an interactive web-based system, SPARK can improve the efficiency of collaboration between human experts and AI, thereby accelerating drug candidate discovery and ultimately leading to breakthroughs in finding cures for various diseases.
Publication
International Joint Conference on Artificial Intelligence
publication