Advancing cancer drug discovery through the integration of machine learning and high-throughput screening
Abstract
Cancer drug discovery is a complex process that requires identifying compounds that selectively target malignant cells. While high-throughput screening (HTS) is essential for testing large libraries, it generates vast datasets that are difficult to interpret. Recently, the integration of artificial intelligence (AI), particularly deep learning (DL), has significantly accelerated drug candidate selection. This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization. These methods streamline preclinical research by enabling rapid multi-omics analysis and prediction of drug-target interactions. However, challenges regarding data quality, model interpretability, and ethics persist. Emerging paradigms like Explainable AI and federated learning aim to enhance transparency and collaboration while safeguarding privacy. Ultimately, overcoming these barriers through AI-HTS integration holds transformative potential to reduce development costs and improve clinical outcomes for cancer patients.
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Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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