AI-Powered Prediction of Molecular Properties of Phytochemicals

For thousands of years, nature remained a source of medical substances, as plants have long been used as medicine to cure different health issues, while their phytochemicals have inspired the design, discovery, and development of new drugs but also present formidable obstacles for computational and experimental assessment. Conventional analyses remain expensive, time-consuming, and poorly scalable for large natural product libraries, despite the importance of accurately predicting key molecular properties, such as solubility, lipophilicity, permeability, bioavailability, and toxicity, in evaluating their therapeutic relevance. The emergence of artificial intelligence (AI), especially machine learning and deep learning, has enabled effective and data-driven modeling of phytochemical properties using both representation-learning techniques and descriptor-based regression frameworks. These include transformer-based chemical language models that extract long-range structural dependencies from molecular strings as well as graph and message passing neural networks that simulate molecular structures as atom–bond graphs. This chapter focuses on important AI approaches and platforms like DeepChem and Chemprop, with a view of multitask learning, transfer learning, and rigorous validation techniques, to guarantee generalization across various phytochemical scaffolds. Challenges related to data scarcity, interpretability, and distributional bias are discussed alongside emerging solutions, including federated learning, foundation models, and next-generation ADMET platforms. This chapter aims to highlight a step forward toward mechanistically informed and scalable prediction frameworks, integrating AI with multi-omics and systems biology, for accelerating phytochemical-driven drug discovery.