ChemPrint: An AI-Driven Framework for Enhanced Drug Discovery

In the evolving landscape of drug discovery, traditional methods face significant challenges due to low efficiency and high resource demands. This study introduces our GALILEO AI drug discovery platform and its foundational ChemPrint model, designed to improve the efficiency of drug discovery. Addressing the challenges of low hit rates and the difficulty in exploring novel chemical spaces, this platform adopts adaptive molecular embeddings and stringent model training environments to enhance predictive capabilities and navigate uncharted molecular territories. We validate this approach through a case study targeting the AXL and BRD4 oncology targets, where ChemPrint achieved a 45.5% in vitro hit rate and identified 20 novel acting compounds. These compounds exhibited substantial chemical novelty, with an average Tanimoto similarity score of 0.32 to their training set, significantly diverging from known compounds and demonstrating ChemPrint’s capability to extrapolate, surpassing traditional benchmarks and industry standards. This underscores the platform’s ability to bridge the gap between the potential of AI and its practical application in therapeutic discovery.