Abstract
Molecular optimization plays a pivotal role in many domains since it holds promise for improving the properties of lead molecules. The advent of artificial intelligence (AI)-driven molecular optimization has revolutionized lead optimization workflows, which have significantly accelerated the development of drug candidates. However, AI models are also confronted with new challenges in practical molecular optimization, such as high-dimensional chemical space and data sparsity issues. This paper initially highlights the inherent benefits of molecular optimization in terms of optimizing the properties and maintaining the structural similarity of lead molecules, thereby highlighting its critical role in drug discovery. The next section systematically categorizes and analyzes existing AI-aided molecular optimization methods, comprising iterative search in discrete chemical space, end-to-end generation in continuous latent space, and iterative search in continuous latent space methods. Finally, we discuss the key challenges in AI-aided molecular optimization methods, including molecular representations, dataset selection, the properties to be optimized, and optimization algorithms, while proposing potential solutions and future research directions. In summary, this review provides a comprehensive analysis of existing representative AI-aided molecular optimization methods, thereby offering guidance for future research directions.