AIRCHECK: An Open Platform for AI-Driven Drug Discovery

Artificial intelligence (AI) and machine learning (ML) can transform early-stage drug discovery by enabling data-driven hit identification for target proteins at scale. However, progress remains constrained by the limited availability of large, open, standardized, and reusable protein-ligand interaction datasets for training, benchmarking, and reproducibility. Modern screening technologies such as DNA-encoded libraries (DEL) and affinity selection mass spectrometry (AS-MS), including enantioselective AS-MS (E-ASMS), can generate binding data for libraries ranging from hundreds of thousands to billions of compounds, yet these datasets are often fragmented, inconsistently processed, poorly annotated, and difficult to reuse for AI/ML. We introduce AIRCHECK (Artificial Intelligence Ready CHEmiCal Knowledge-base), an open, cloud-based data and computing infrastructure that converts DEL and E-ASMS screening outputs, together with downstream experimental validation assays when available, into curated, standardized, AI-ready datasets. AIRCHECK applies shared standard operating procedures, automated, cloud-native pipelines for data validation, harmonized labelling, and chemical featurization, producing downloadable data packages with rich metadata and multiple molecular fingerprints and descriptors that can be accessed at scale via application programming interfaces. Beyond data access, AIRCHECK provides open-source, containerized AI/ML workflows with persistent experiment tracking and benchmarking, as well as baseline AI/ML models to support training, evaluation, and deployment across all included targets. As of January 2026, AIRCHECK hosts curated screening data for 114 protein targets (~350 GB of screening data and ~125 TB of associated chemical representations). Developed within the Target 2035 open-science framework, AIRCHECK enables collaborative, reproducible virtual screening and experimental follow-up to accelerate and democratize hit discovery across the human proteome.