"AI-Assisted Multi-Parameter Optimization in Medicinal Chemistry: Integrating Potency, ADMET, Selectivity and Synthetic Accessibility for Rational Drug Design"

AbstractThe increasing complexity of modern drug discovery has highlighted the limitations ofconventional medicinal-chemistry strategies that optimize molecular potency as a primaryobjective while considering pharmacokinetic, safety and synthetic properties at later stages.Artificial intelligence (AI)-assisted multi-parameter optimization (MPO) has emerged as apromising paradigm for addressing this limitation by enabling simultaneous optimization of targetpotency, absorption, distribution, metabolism, excretion and toxicity (ADMET), target selectivityand synthetic accessibility. This review provides a comprehensive and research-oriented overviewof the evolution of computer-aided drug design toward AI-driven multi-objective molecularoptimization. The principles of MPO and the interrelationships and trade-offs among key drug-like properties are discussed, followed by an evaluation of machine learning, deep learning, graphneural networks, transformers, foundation models, generative AI and reinforcement learningapproaches used in medicinal chemistry. Particular emphasis is placed on AI-assisted predictionand optimization of molecular potency, ADMET characteristics, off-target interactions,polypharmacology and synthetic feasibility. Multi-objective optimization strategies, includingPareto optimization, weighted utility functions, Bayesian optimization, evolutionary algorithmsand reinforcement learning, are examined for their ability to identify balanced drug candidatesrather than molecules optimized for a single endpoint. The review further presents an integratedAI workflow encompassing data curation, molecular representation, predictive modeling,molecular generation, virtual screening, retrosynthetic analysis, experimental validation andclosed-loop optimization. Key data resources, benchmarking strategies, reproducibilityrequirements and limitations associated with data bias, activity cliffs, model overfitting, chemicalextrapolation, interpretability and the prediction–reality gap are critically considered. Emergingdirections, including chemical foundation models, large language models, multimodal AI, physics-informed learning, autonomous laboratories, federated learning and human–AI collaboration, arealso discussed. Overall, AI-assisted MPO represents a transition from single-property optimizationtoward integrated and experimentally informed rational drug design, with the potential to improvethe efficiency of lead optimization and increase the probability of identifying potent, selective,pharmacokinetically favorable, safe and synthetically accessible drug candidates.