Multi-Objective Generative Optimization of Protein–Ligand Binding Affinity Using Bayesian Inference and Molecular Simulation
Keywords:
protein–ligand binding affinity, generative optimization, Bayesian inference, molecular simulation, multi-objective optimization, uncertainty quantification, drug discovery, socio-technical systemsAbstract
The optimization of protein–ligand binding affinity is a central problem in drug discovery, chemical biology, and protein engineering, yet it remains difficult because affinity is governed by coupled thermodynamic, kinetic, and conformational factors that cannot be reduced to a single static score. Recent advances in generative artificial intelligence, Bayesian uncertainty modeling, and molecular simulation have created an opportunity to treat affinity optimization not as a narrow prediction task but as a multi-objective socio-technical system spanning data generation, model architecture, simulation fidelity, laboratory validation, and governance. This paper develops a system-level account of multi-objective generative optimization for protein–ligand binding affinity using Bayesian inference and molecular simulation. It argues that the most consequential challenges are not solely algorithmic but architectural and institutional: integrating heterogeneous data sources, calibrating uncertainty across simulation and assay scales, managing trade-offs among potency, selectivity, safety, synthesizability, and sustainability, and deploying optimization loops in ways that are reproducible, fair, and robust. The discussion connects molecular generative models, equivariant graph representations, diffusion and flow-based generation, free-energy simulation, active learning, and human-in-the-loop decision-making. It also examines infrastructure requirements, including high-performance computing, federated data governance, automated laboratories, audit trails, and model documentation. Through cross-domain comparisons with materials discovery and protein design, the paper identifies design principles for trustworthy generative optimization systems. It concludes that progress will depend on coupling Bayesian decision theory with physical simulation and on building governance structures that make uncertainty, trade-offs, and limitations explicit to researchers, regulators, and affected communities.
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