# AI-Assisted Molecular Design

This review systematically surveys the current landscape of artificial intelligence and machine learning in lead optimization, covering advances in graph neural networks, transformer architectures, diffusion models, and chemical foundation models for molecular design and property prediction, and discusses emerging concepts including data-centric AI, uncertainty quantification, trustworthy AI, the AI optimization paradox, and the shift from molecular prediction toward scientific decision-making, concluding that despite increasing industrial adoption, AI remains dependent on high-quality experimental data, model generalizability, and rigorous experimental validation, and that future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve

This review systematically surveys the current landscape of artificial intelligence and machine learning in lead optimization, covering advances in graph neural networks, transformer architectures, diffusion models, and chemical foundation models for molecular design and property prediction, and discusses emerging concepts including data-centric AI, uncertainty quantification, trustworthy AI, the AI optimization paradox, and the shift from molecular prediction toward scientific decision-making, concluding that despite increasing industrial adoption, AI remains dependent on high-quality experimental data, model generalizability, and rigorous experimental validation, and that future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve