HTS-Oracle X: AI-Guided Prospective Discovery of Small Molecule Immune Checkpoint Binders

Abstract Targeting immune checkpoint protein–protein interactions (PPIs) using small molecules remains limited by the characteristically low hit rates of conventional high-throughput screening against these interfaces. Here we report HTS-Oracle X, a multimodal deep learning platform that integrates bidirectional cross-attention fusion of ChemBERTa SMILES embeddings with extended RDKit descriptors, trains on continuous biophysical binding signals rather than binary labels, and employs Monte Carlo Dropout uncertainty quantification for uncertainty-adjusted compound selection. Trained on 45,760 Dianthus TRIC-screened compounds per target under scaffold-aware cross-validation, HTS-Oracle X was applied prospectively to a 100,160-compound enamine library against CD28, TIM-3, and VISTA. From 150 model-selected compounds, 45 dose–response confirmed binders were identified, yielding enrichment factors of 234–408× over experimentally established random prospective baselines and 16 sub-micromolar hits. The top hits, HX-CD28-1 (KD = 233 nM), HX-TIM3-1 (KD = 249 nM), and HX-VISTA-1 (KD = 345 nM), demonstrated on-target functional activity in immune cell and tumor coculture assays.