Abstract 6409: AI-accelerated discovery of B7-H3 and DLL3-targeted cyclic peptide radioligands: From library design to preclinical validation

Abstract Background: Radiopharmaceutical drug conjugates (RDCs) represent a transformative paradigm in precision oncology, yet target-specific ligand discovery remains a critical bottleneck. We developed an integrated AI-augmented platform to rapidly identify cyclic peptide binders for two emerging RDC targets: B7-H3 (immune checkpoint) and DLL3 (Notch ligand). Methods: A structurally diverse phage display library (1.5×1011capacity; 8-17 aa macrocycles) was engineered with NGS-validated complexity. Recombinant 4Ig-B7-H3 (2Ig-B7-H3 as off-target counterscreens), DLL3 (and counterscreens DLL1/DLL4) underwent orthogonal biophysical characterization by SPR and Spectral shift assay (SPS). Hit triangulation employed: (1) Deep sequencing-driven consensus motif analysis, (2) AlphaFold3 multimer modeling of peptide-target complexes, and (3) Parallel SPR/spectral shift assays (10-7 M affinity threshold). Top candidates were Cy5-labeled for real-time binding and internalization kinetics in engineered tumor lines. Results: Discovery: 15/40 phage clones demonstrated target binding - AI optimization: AlphaFold3 predictions revealed a conserved β-turn motif in 7/15 top hits that anchors to a cryptic pocket in targets - Validation: Hit peptide were synthesized and showed: (i) KD 8.2×10-7 M (SPR), (ii) 3-fold selectivity over homologous target proteins (iii) showed binding on cells Conclusions: This platform addresses critical challenges in RDC development by providing a streamlined solution for hit discovery, optimization, and validation. The integration of AI-driven structural prediction with high-throughput experimental validation compresses traditional hit discovery timelines This platform will significantly accelerate the RDC development pipeline—especially for hit binder identification for neo tumor antigens or other validated surface proteins like GPCRs, transporters, to accelerate RDC drug discovery using cyclic peptide as the modality. Citation Format: Tj (Tiejun) Bing. AI-accelerated discovery of B7-H3 and DLL3-targeted cyclic peptide radioligands: From library design to preclinical validation abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6409.