Khushboo Gupta / Pranjul Shrivastava
AI and machine learning (ML) are increasingly transforming the field of radiopharmaceutical development, offering new ways to design, optimize, and evaluate these critical compounds. Radiopharmaceuticals, which are used for both diagnostic imaging and targeted therapy, require precise formulation and testing to ensure safety and efficacy. AI and ML technologies can accelerate this process by analyzing vast amounts of complex data, identifying patterns, and making predictions that were previously time-consuming or difficult.Machine learning algorithms, such as deep learning and reinforcement learning, are used to model molecular interactions, predict the behavior of radiopharmaceuticals within the body, and optimize the selection of radioisotopes for specific targeting. AI-driven tools also enable faster drug discovery by simulating and predicting the outcomes of radiopharmaceutical interactions with biological systems, thus reducing the need for extensive trial and error.
