Data-Driven Cheminformatics Models for Predicting Bioactivity of Natural Compounds in Oncology

Abstract

Advancements in data-driven cheminformatics have significantly transformed the early-stage discovery and optimization of oncology therapeutics derived from natural compounds. This review examines the integration of machine learning (ML) and quantitative structure–activity relationship phytochemicals and marine-derived agents. Emphasis is placed on the use of high dimensional molecular descriptors, fingerprinting techniques, and graph-based neural networks for feature extraction and predictive modeling. Public bioactivity databases such as ChEMBL, PubChem BioAssay, and BindingDB are explored as primary sources for curated compound-target interaction data, which underpin supervised learning frameworks. Furthermore, the review highlights recent breakthroughs in multi-task learning, deep generative models, and transfer learning paradigms that enhance generalizability across diverse chemical scaffolds and rare oncogenic targets. Challenges such as model interpretability, data sparsity, and bioavailability prediction are discussed, with proposed strategies including explainable AI (XAI) and hybrid mechanistic-ML models. This review highlights the transformative potential of cheminformatics in accelerating oncology drug discovery by reducing reliance on labor intensive wet-lab screening and enabling virtual prioritization of lead compounds from vast natural product libraries.