2nd Edition of Pharma R&D and Drug Discovery World Conference 2026

Abstract

Castration-resistant prostate cancer (CRPC) poses a major therapeutic challenge due to its resistance to androgen deprivation and progression driven by constitutively active androgen receptor variants (AR-Vs). Using a systems biology approach, our group identified BUB1B, a mitotic serine/threonine kinase, as a master regulator within a gene signature associated with CRPC progression. BUB1B is upregulated in resistant tumors and promotes AR-independent growth, but currently lacks selective small molecule inhibitors or a resolved crystal structure, limiting therapeutic development. To address this, we generated predictive structural models of the BUB1B kinase domain using a combination of homology modeling, AlphaFold, and IntFold. We applied a multi-step computational strategy to identify potential ATP-competitive inhibitors, integrating deep learning-based compound prioritization, ensemble molecular docking across multiple conformations, and post-docking validation using MM-GBSA analysis and molecular dynamics (MD) simulations. This workflow prioritized compounds with high stability, binding site fidelity, and engagement of key ATP-binding motifs. From this integrated analysis, we identified a subset of structurally diverse compounds that consistently demonstrated strong binding affinities and stable interactions in the kinase active site. These compounds engaged conserved hinge residues and catalytic loop contacts essential for ATP-pocket binding. Additionally, we flagged compounds with strong interactions in specific conformational frames, acknowledging the dynamic flexibility of kinase domains. Importantly, our findings align with biological evidence showing that BUB1B overexpression confers resistance to androgen receptor antagonists and enhances tumor growth in CRPC models. Top-ranked compounds are now being prioritized for in vitro validation using kinase inhibition and cell viability assays to determine their ability to suppress BUB1B activity and reduce CRPC cell proliferation. This work highlights a computationally driven framework for drug discovery targeting kinases without resolved crystal structures. By combining advanced protein modeling, artificial intelligence–based screening, and multi-frame docking analysis, we present a tractable shortlist of BUB1B inhibitor candidates. These findings offer a promising foundation for developing first-in-class therapies against therapy-resistant prostate cancer.