Recent advancements in AI-driven protein structure prediction, particularly with AlphaFold2 (AF2), are transforming drug discovery and development, especially in cancer research. This review explores how AI technologies, including AF2, ESMFold, and others, are improving the identification and design of drug targets, enhancing the accuracy and speed of drug development processes. AF2 has significantly advanced the ability to predict protein structures from sequence data, enabling more precise drug target identification and design. The review also discusses the broader applications of these technologies, such as predicting protein complex structures and generating novel proteins. AI tools like AF2, ESMFold, RoseTTAFold, and OpenFold each have distinct strengths and limitations. AF2 excels in predicting protein structures but may struggle with orphan proteins and single-point mutations. ESMFold, based on a BERT-like architecture, offers faster predictions and is particularly effective for orphan proteins. RoseTTAFold can predict protein-nucleic acid complexes, while OpenFold provides an open-source, scalable alternative to AF2. These tools collectively enhance the efficiency and accuracy of protein structure prediction, supporting drug discovery and development. Generative AI is also playing a crucial role in cancer drug development, enabling the design of novel proteins and drug candidates. AI-driven approaches have been successfully applied in various areas, including the discovery of new antibiotics, the development of drugs for neurological and autoimmune disorders, and the identification of potential treatments for diseases like idiopathic pulmonary fibrosis. These advancements highlight the transformative potential of AI in accelerating drug discovery and improving therapeutic outcomes. Despite these advancements, challenges remain, including the need for substantial computational resources, regulatory hurdles, and the complexity of protein dynamics. However, ongoing research and development are expected to further enhance the accuracy and applicability of AI tools in drug discovery, ultimately leading to more effective and efficient therapeutic strategies.Recent advancements in AI-driven protein structure prediction, particularly with AlphaFold2 (AF2), are transforming drug discovery and development, especially in cancer research. This review explores how AI technologies, including AF2, ESMFold, and others, are improving the identification and design of drug targets, enhancing the accuracy and speed of drug development processes. AF2 has significantly advanced the ability to predict protein structures from sequence data, enabling more precise drug target identification and design. The review also discusses the broader applications of these technologies, such as predicting protein complex structures and generating novel proteins. AI tools like AF2, ESMFold, RoseTTAFold, and OpenFold each have distinct strengths and limitations. AF2 excels in predicting protein structures but may struggle with orphan proteins and single-point mutations. ESMFold, based on a BERT-like architecture, offers faster predictions and is particularly effective for orphan proteins. RoseTTAFold can predict protein-nucleic acid complexes, while OpenFold provides an open-source, scalable alternative to AF2. These tools collectively enhance the efficiency and accuracy of protein structure prediction, supporting drug discovery and development. Generative AI is also playing a crucial role in cancer drug development, enabling the design of novel proteins and drug candidates. AI-driven approaches have been successfully applied in various areas, including the discovery of new antibiotics, the development of drugs for neurological and autoimmune disorders, and the identification of potential treatments for diseases like idiopathic pulmonary fibrosis. These advancements highlight the transformative potential of AI in accelerating drug discovery and improving therapeutic outcomes. Despite these advancements, challenges remain, including the need for substantial computational resources, regulatory hurdles, and the complexity of protein dynamics. However, ongoing research and development are expected to further enhance the accuracy and applicability of AI tools in drug discovery, ultimately leading to more effective and efficient therapeutic strategies.