COMBATING CANCER THROUGH AI: DEEP LEARNING FOR RATIONAL DESIGN OF SMALL MOLECULE COMBINATION THERAPIES

Abstract

The complexity and adaptability of cancer demand innovative therapeutic strategies beyond traditional monotherapies. Artificial intelligence (AI), particularly deep learning, is transforming the landscape of oncology by enabling the rational design of small molecule combination therapies. These models leverage large-scale pharmacogenomic and multi-omics data to predict synergistic drug pairs tailored to individual tumor profiles. Deep learning frameworks, including convolutional and graph neural networks, are uncovering intricate relationships between molecular features and therapeutic response, offering a more precise and personalized approach to treatment. Additionally, explainable AI enhances the interpretability of these predictions, supporting clinical decision-making and accelerating translation. This communication highlights the promise of AI-driven approaches in redefining cancer treatment paradigms and advancing precision oncology.