Topological data analysis for antibody-drug conjugate payload discovery: a computational framework for mechanistic classification and target validation.

Antibody-drug conjugate (ADC) payload discovery remains constrained by reliance on traditional molecular descriptors that inadequately capture three-dimensional geometric features governing target recognition and mechanism of action. Topological data analysis (TDA) offers a mathematical framework for characterizing molecular shape through persistent homology, potentially revealing mechanistic relationships invisible to conventional approaches. We developed a comprehensive TDA framework analyzing 22 FDA-approved ADC payloads across 1,471 clinical trial records, computing 31 topological descriptors encompassing Betti numbers, persistence statistics, and complexity metrics. Hierarchical clustering, principal component analysis, and correlation network analysis were employed for dimensionality reduction and cluster validation, with molecular docking studies validating TDA-derived classifications. TDA-based clustering identified eight distinct payload classes with excellent separation. Principal components captured 79.8% of topological variance, with Betti numbers and persistence lifetime as dominant features. Three major mechanistic clusters emerged: vinca alkaloids (tubulin inhibitors), camptothecins (topoisomerase I poisons), and DNA alkylators. Molecular docking demonstrated high performance within-cluster binding consistency and significant cross-cluster discrimination. We establish the first validated TDA framework for ADC payload discovery, demonstrating that persistent homology captures biologically meaningful mechanistic classifications suitable for rational payload design and mechanism-of-action prediction in precision oncology.