Machine Learning-Guided Generation of Novel Pseudo-Natural Products: Applications to Accelerate Drug Discovery
Wenyu Lü, Xiaoqian Peng, Yan Huang, Zhe Zheng, Zhenzhen Zhu, Xunkai Yin, Wenjun Xu, Shulan Mei, Xiuhong Lu, Xia Zhang, Yue Wang, Lihong Hu, Jian Liu · Journal of Chemical Information and Modeling · 2025
Natural products (NPs) are a critical source for drug discovery, and artificial intelligence (AI) is utilized to improve the efficiency of NP-based drug discovery. However, the existing AI-driven models typically generate a library of pseudo-natural products that only covers a small portion of the chemical space and the compounds were also restricted by poor drug-likeness profiles. Herein, the GPT1 is developed to generate diverse pseudo-natural products with excellent validity, uniqueness, and novelty while retaining molecular features similar to the training set. Subsequently, the Augmented Hill-Climb (AHC) strategy is employed to generate synthetically accessible compounds with enhanced drug-likeness. Using the integrated NPDL-GEN model (GPT1 + AHC), compounds G1 – G5 were obtained, exhibiting significantly improved drug-likeness profiles. Furthermore, the pseudo-natural products H1 – H3 generated via transfer learning also possess potent anti-inflammatory activities. Thus, our developed machine learning models can accelerate NP-based drug discovery.