ArticleACS medicinal chemistry letters2026
HTS-Oracle X: AI-Guided Prospective Discovery of Small Molecule Immune Checkpoint Binders.
Article in ACS medicinal chemistry letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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,
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