Generative AI in Cancer: Improving Therapies Against Common Oncogenic Drivers

As the cases of early-onset cancer continue to rise, the need for innovative therapeutic approaches in targeted therapy grows. Generative AI has emerged as a powerful tool for de novo drug design, showing great promise in creating targeted therapies against challenging cancer driver mutations. These mutations, including TP53, KRAS, and EGFR, often confer gain-of-function effects that drive cancer progression and are notoriously difficult to target due to their unique biochemical properties. This review summarizes the shift from conventional drug design towards newfound generative AI models, highlighting their ability to optimize binding affinity, anticancer properties, and generate novel molecules against previously "undruggable" targets. This review explores generative AI’s role in developing effective personalized medicine, revolutionizing anticancer treatment against prevalent cancer driver mutations.