AI-driven computational drug design: tools, workflow and challenges

design approaches, such as REINVENT and RANC, and multi-task ADMET predictors, such as ADMETlab 2.0, are all parts of the AI-based drug discovery platform. In addition, AlphaFold has recently predicted more than 200 million protein structures, substantially expanding the pool of accessible drug targets. This review focuses on the AI-based approaches for target discovery, virtual screening, molecular generation, lead optimization, retro-synthesis, and structural modeling while also addressing issues of dataset bias, reproducibility, and real-world applicability. In this review, we discuss the emerging trends of AI-based drug discovery computational tools, which might change the face of medicinal chemistry. This review provides a balanced overview of AI-based drug discovery, including the limitations and challenges, to provide a framework to move this emerging field of science to a more robust, reproducible and clinically applicable platform.