Molecular optimization, aimed at improving binding affinity or other molecular properties, is a crucial task in drug discovery that often relies on the expertise of medicinal chemists. Recently, deep learning-based 3D generative models showed promise in enhancing the efficiency of molecular optimization. However, these models often struggle to adequately consider binding affinities with protein targets during lead optimization. Herein, we propose a 3D pocket-aware and affinity-guided diffusion model, named Diffleop, to optimize molecules with enhanced binding affinity. The model explicitly incorporates the knowledge of protein-ligand binding affinity to guide the denoising sampling for molecule generation with high affinity. The comprehensive evaluations indicated that Diffleop outperforms baseline models across multiple metrics, especially in terms of binding affinity.
View on arXiv@article{qiao2025_2504.21065,
title={ A 3D pocket-aware and affinity-guided diffusion model for lead optimization },
author={ Anjie Qiao and Junjie Xie and Weifeng Huang and Hao Zhang and Jiahua Rao and Shuangjia Zheng and Yuedong Yang and Zhen Wang and Guo-Bo Li and Jinping Lei },
journal={arXiv preprint arXiv:2504.21065},
year={ 2025 }
}