AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities?

Abstract

Cheminformatics and chemical databases are essential to drug discovery. However, machine learning (ML) and artificial intelligence (AI) methodologies are changing the way in which chemical data is used. How will the use of chemical data change in drug discovery moving forward? How do the new ML methods in molecular property prediction, hit and lead and target identification and structure prediction differ and compare with previous computational methods? Will new ML methodologies improve chemical diversity in ligand design, and offer computational enhancements. There are still many advantages to physics based methods and they offer something lacking in ML/ AI based methods. Additionally, ML training methods often give the best results when experimental assay measurements are fed back into the model. Often modeling and experimental methods are not diametrically opposed but offer the greatest advantage when used complementary.