Adaptive space search-based molecular evolution optimization algorithm

Abstract

Abstract Motivation In the drug development process, a significant portion of the budget and research time are dedicated to the lead compound optimization procedure to identify potential drugs. This procedure focuses on enhancing the pharmacological and bioactive properties of compounds by optimizing their local substructures. However, due to the vast and discrete chemical structure space and the unpredictable element combinations within this space, the optimization process is inherently complex. Various structure enumeration-based combinatorial optimization methods have shown certain advantages. However, they still have limitations. Those methods fail to consider the differences between molecules and struggle to explore the unknown outer search space. Results In this study, we propose an adaptive space search-based molecular evolution optimization algorithm (ASSMOEA). It consists of three key modules: construction of molecule-specific search space, molecular evolutionary optimization, and adaptive expansion of molecule-specific search space. Specifically, we design a fragment similarity tree in a molecule-specific search space and apply a dynamic mutation strategy in this space to guide molecular optimization. Then, we utilize an encoder–encoder structure to adaptively expand the space. Those three modules are circled iteratively to optimize molecules. Our experiments demonstrate that ASSMOEA outperforms existing methods in terms of molecular optimization. It not only enhances the efficiency of the molecular optimization process but also exhibits a robust ability to search for correct solutions. Availability and implementation The code is freely available on the web at https://github.com/bbbbb-b/MEOAFST.