Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges

The review notes that lead optimization requires simultaneous optimization of potency, selectivity, pharmacokinetics, safety, and synthetic feasibility, while AI and ML have substantially advanced predictive modeling, virtual screening, de novo molecular design, and multiparameter optimization. Relative to prior reviews focused on a single algorithm or task, this work examines multi-objective optimization needs alongside multiple classes of AI methods in one framework, emphasizing the gap between computational benchmark performance and practical medicinal chemistry applications. This is a review article; its judgments rest on synthesis of existing literature rather than new experimental data or benchmarks.

Graph neural networks, transformer architectures, diffusion models, and chemical foundation models have expanded AI-assisted molecular design and property prediction. Presenting these recent architectural advances together shows that methodological innovation spans multiple stages from molecular representation to generative design. A review-level methodological synthesis; the text provides no specific datasets, sample sizes, or effect sizes.

Data-centric AI, uncertainty quantification, trustworthy AI, the AI optimization paradox, and the shift from molecular prediction toward scientific decision-making are identified as key determinants of successful implementation. The discussion shifts focus from model performance to data quality, trustworthiness, and decision support, proposing these concepts as core variables affecting real-world adoption. Conceptual discussion and literature synthesis, constituting an opinion-based summary rather than empirical testing.

Despite increasing industrial adoption, AI remains dependent on high-quality experimental data, model generalizability, and rigorous experimental validation; future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve scientific decision-making within iterative lead optimization workflows. Locating the bottleneck for translational success in data, generalizability, validation, and decision integration rather than algorithmic capability alone provides a directional judgment for future research and practice. A review conclusion based on synthesis of current advances and challenges, without new quantitative evidence.

This article is intended for readers seeking an overview of the AI-assisted lead optimization landscape, including researchers in medicinal chemistry, computational chemistry, and AI methods, as well as industrial practitioners focused on translation. Its conclusions apply to the setting of iterative lead optimization workflows, emphasizing that trustworthy AI systems may improve scientific decision-making when high-quality experimental data and rigorous validation conditions are available.

Readers should still watch: how different AI methods perform in real lead optimization projects, the availability of high-quality experimental data, model generalizability when extrapolating beyond chemical space, and how uncertainty quantification and trustworthy AI can be concretely embedded into decision processes. Because the current text is a fast parse lacking figures and specific cases, open questions about method comparisons and implementation details require further confirmation against the original article.