Machine Learning Approaches to Identify and Optimize Plant-Based Bioactive Compounds for Targeted Cancer Treatments

Abstract

Machine learning (ML) represents a breakthrough in drug discovery, markedly increasing efficiency in the search for plant-derived bioactive compounds with anticancer activity. While compounds derived from plants like vincristine and taxol are historical pillars of oncology, the emerging novel therapeutic agents aim to overcome limitations associated with classical therapies, such as toxicity and resistance. Some of the important ML algorithms in this context include decision trees, support vector machines, neural networks, and ensemble learning which allow predictions about bioactivity by managing complicated biological data and determining the effectiveness of different compounds while also optimizing therapeutic profiles. For anticancer compound discovery, supervised as well as unsupervised learning is used whereby activity can be predicted from known properties or compounds just clustered in huge phytochemical databases. Moreover, deep learning models are particularly adept at processing high-dimensional data like multi-omics data and discovering non-linear relationships which furthers our understanding of bioactive compounds at a systems level. While optimizing bioactive compounds, QSAR modeling alongside generative models helps in fine-tuning the molecular design for improved activity and reduced toxicity. ADMET profiling also ensures that the molecules are within the limits of pharmacokinetic and safety standards, thus smoothing out the passage from in silico predictions to experimental validation. The discussion closes with the consideration of some challenges, such as data integration, interpretability of models, computationally intensive tasks, and regulatory demands to be followed versus the promise of the future through cooperative platforms, accessible ML tools together with personalized medicine. Further emphasis is given on the need for continued research interdisciplinary collaborations as well as investments that will help harness the full potential of ML in plant-based anticancer drug discovery to improve treatment outcomes while minimizing adverse effects.