AI-DRIVEN DRUG DISCOVERY FOR CANCER THERAPY: MACHINE LEARNING MODELS FOR IDENTIFYING NOVEL TARGETS AND OPTIMIZING CHEMOTHERAPY REGIMENS

Abstract

Transforming drug discovery into an AI for cancer therapy, which uses machine learning (ML) models to identify novel drug targets and optimize chemotherapy regimens, can achieve this. AI is also enabling high-throughput screening, predictive modeling, and biomarker discovery, thereby improving precision medicine strategies. This review contemplates the involvement of AI in drug development, its applications in treating cancer, challenges, and future directions. V AI-powered systems are transforming cancer drug discovery in target identification, high-throughput screening, and chemotherapy regimen optimization. Machine-learning enhances the predictions about early-phase drug development, thus hastening it, and creates precision oncology by means of developing algorithms for predictive modeling. The key to the unfolding full potential of AI for oncology therapeutics lies in overcoming the current problems and creating. The dazzling AI-powered systems are going to change the whole drug discovery process from pure target identification, high throughput-screening, and optimization of chemotherapy regimens. Machine learning strengthens most predictions regarding early drug development and brings speed to drug development and precision oncology through the making of an algorithm for predictive accuracy. Overcoming current challenges and building interdisciplinary collaborations will be key to unleashing the full potential of AI in oncology therapeutics.