Demonstrates effective identification of immune checkpoint binders in compounds, suggesting advancement in drug discovery.
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 ( K D = 233 nM), HX-TIM3-1 ( K D = 249 nM), and HX-VISTA-1 ( K D = 345 nM), demonstrated on-target functional activity in immune cell and tumor coculture assays.
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Abdel-Rahman et al. (2026) studied this question.
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