A Review on Integration of Scientific Experimental Data Through Metadata

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

“…The development of AI models for drug metabolism and excretion prediction holds great promise for improving drug R&D. Researchers are working hard to explore new ways to create and integrate experimental data, such as relying on metadata to improve data quality [123]. In addition, efforts are underway to improve the quality of experimental data through standardization of experimental protocols, and the use of quality control measures and rigorous validation procedures.…”

“…In the context of complex big data, DL methods are likely to prevail soon as they are easier to adapt to a wider range of chemical entities and modeling tasks and enable more efficient data mining. In addition to using existing explanatory AI methods such as feature attribution, instance-based, graph-convolution-based, and self-explanatory methods [123], efforts are being made to develop new methods to ensure transparency, safety, efficacy, and reliability in clinical settings, and maintain public trust in AI technology. In-depth knowledge of drug metabolism and excretion and AI techniques is very important to give a reasonable and useful explanation.…”