Methods Mol Biol. 2024;2714:295-305. doi: 10.1007/978-1-0716-3441-7_16.
In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.