This invention discloses a method for identifying effective small molecules in
traditional Chinese medicine (TCM) prescriptions by integrating EGA and PPO algorithms. It relates to the field of
small molecule identification technology for TCM decoctions, and includes constructing a molecular seed
library containing known components from TPACD and structurally similar phytochemicals. EGA simulates the selection, transformation, and recombination mechanisms during the TCM decoction process, while PPO leverages the
sequential decision-making optimization capabilities of
reinforcement learning. Three parallel modules—Basic Multi-Objective
Genetic Algorithm (BMGA), Enhanced Multi-Objective
Genetic Algorithm (EMGA), and Intelligent Multi-Objective
Genetic Algorithm (IMGA)—are built. Through
molecular evolution,
targeted screening,
iterative refinement, and closed-loop feedback, anti-CRC leader molecules with
efficacy, stability, and
drug-like properties are screened. The molecules generated by this invention significantly outperform the original TPACD components in terms of
drug-likeness,
structural stability, and anti-CRC activity. The IMGA module shows the best overall performance, providing an efficient and interpretable new strategy for mining active ingredients in TCM compound prescriptions.