TCM-navigator, a deep learning-based workflow for generation and evaluation of traditional Chinese medicine-like compounds for drug development.

Abstract

Traditional Chinese Medicine (TCM) has long been regarded as a valuable resource for modern drug discovery. However, the limited availability of recorded entities and information, the complexity and sparsity of the herb-ingredient-target-disease network, and inconsistencies in data representation hinder the effectiveness of high-throughput screening approaches. While some therapeutically valuable compounds from TCM have been discovered through manual experimental screening, such methods are time-consuming and require substantial human resources. To address these challenges, we developed a data-driven and deep learning-based workflow, TCM-navigator, which enables the in-silico generation, quality control, and physics-based evaluation of TCM-like molecules. The generation is done by TCM-Generator, a transfer learning- and Long Short-Term Memory (LSTM)-based chemical language model that generates standardized, hierarchically structured, and high-throughput-friendly datasets of TCM-like molecules. In this study, we generated a target-nonspecific dataset comprising 3.7 million TCM-like molecules, expanding the number of entities in existing TCM datasets by more than 100-fold. The workflow also enables flexible, goal-driven molecule generation customized for specific targets, yielding three target-specific datasets and multiple high-potential target-ligand pairs. The quality control is done by TCM-Identifier, the first quantitative model specifically designed to capture unique characteristics of TCM, using an AttentiveFP framework with message passing neural networks. TCM-Identifier is expected to serve as an essential evaluation and guidance tool for TCM-related drug development. Our workflow bridges cutting-edge data science-including deep learning-with biomedical research to tackle longstanding challenges in target identification and molecular design. Its adaptable framework is also transferable to interdisciplinary innovation beyond drug development.