Multi-Platform Quantum Computing Simulation Pipeline for ADC Drug Design: HER2–Trastuzumab Interface Analysis presents a quantum-classical hybrid framework for next-generation antibody-drug conjugate (ADC) discovery. This work integrates photonic quantum sampling, neutral-atom optimization, and variational quantum chemistry workflows with AI-assisted molecular design to analyze the HER2–Trastuzumab interface and generate optimized linker–conjugate candidates. The proposed pipeline addresses key limitations of conventional molecular dynamics (MD) and quantum mechanics (QM) simulations in large-scale protein interface analysis, introducing a scalable multi-platform quantum computing architecture for structure-based drug discovery. The study demonstrates a proof-of-concept workflow spanning target definition, quantum-derived residue prioritization, linker rule generation, conjugate assembly, docking validation, and downstream efficacy evaluation. Key highlights include:• 49 interface residues analyzed using photonic boson-sampling simulation• 162 conjugate candidates computationally generated and screened• Quantum-assisted ADC linker optimization workflow• HER2-targeted ADC feasibility validation using hybrid quantum-classical methods• Integration of AI, quantum computing, and molecular modeling for accelerated drug discovery This study represents an independent personal R&D initiative exploring the practical convergence of quantum computing and AI-driven pharmaceutical research, with applications in ADC design, automated protein preprocessing, and next-generation computational therapeutics.
Sungil Oh (Wed,) studied this question.