AIRCHECK: Powering AI-Driven Drug Discovery with Open Science

AIRCHECK (Artificial Intelligence-Ready CHEmical Knowledge base) overcomes obstacles in small molecule hit discovery - including fragmented datasets, limited reproducibility, and inefficient computational models - by integrating cutting-edge experimental techniques—including DNA-Encoded Libraries (DEL), and Enantiomer-selective Affinity Selection Mass Spectrometry (EAS-MS)—with machine learning (ML) and AI-driven analytics. Built on Google Cloud, AIRCHECK provides a scalable, open-access data platform that enhances hit identification, benchmarking, and reproducibility through MLOps workflows. By curating high-quality datasets and developing robust baseline AI models, AIRCHECK ensures that predictive insights translate into experimentally validated drug candidates. This presentation will provide an overview of AIRCHECK’s vision, mission, and technological framework, detailing how it leverages large-scale datasets and computational models to streamline drug discovery. We will also discuss the AIRCHECK roadmap, its role in accelerating AI-driven pharmaceutical research, and its long-term impact on enhancing reproducibility, reducing costs, and democratizing access to high-quality drug discovery data.