Advancements in breast cancer therapy: Integrating AI tools for drug discovery and clinical trials

Breast cancer remains the most prevalent cancer among women worldwide, posing significant challenges in treatment due to drug resistance and adverse side effects. Traditional drug discovery approaches are resource-intensive, requiring extensive time, human effort, and financial investment. Computer-Aided Drug Design (CADD) has emerged as a promising computational approach to speed up drug development while reducing costs. Conventional CADD techniques, which depend heavily on computationally intensive methods like quantum mechanics and Molecular Dynamics (MD), often face limitations due to their high resource demands and time inefficiencies. However, the integration of advanced computing technologies and Artificial Intelligence (AI), especially deep learning, has significantly enhanced the efficiency and scope of CADD by addressing these challenges. Machine learning, a subfield of AI, employs algorithms and statistical models to analyze data, predict outcomes, and enhance decision-making processes. Deep Neural Networks (DNN), a core component of AI, have shown exceptional potential in modelling generalized structure-activity relationships, enabling more accurate drug efficacy and safety predictions. This review explores the evolution of CADD methodologies and the integration of AI in the context of breast cancer therapy. Emphasizing the transformative role of AI-guided approaches and their ability to overcome the constraints of traditional methods and foster the development of innovative and effective therapeutic options. This article also explores the role of AI in breast cancer diagnosis and treatment, supported by relevant case studies. By leveraging advancements in AI and machine learning, the future of breast cancer drug discovery holds significant promise for improving treatment outcomes, early diagnosis, and addressing unmet medical needs.