Yin-yang in drug discovery: rethinking de novo design and development of predictive models

Abstract

Chemical and biological data are the cornerstone of modern drug discovery programs. Finding qualitative yet better quantitative relationships between chemical structures and biological activity has been long pursued in medicinal chemistry and drug discovery. With the rapid increase and deployment of the predictive machine and deep learning methods, as well as the renewed interest in the de novo design of compound libraries to enlarge the medicinally relevant chemical space, the balance between quantity and quality of data are becoming a central point in the discussion of the type of data sets needed. Although there is a general notion that the more data, the better, it is also true that its quality is crucial despite the size of the data itself. Furthermore, the active versus inactive compounds ratio balance is also a major consideration. This review discusses the most common public data sets currently used as benchmarks to develop predictive and classification models used in de novo design. We point out the need to continue disclosing inactive compounds and negative data in peer-reviewed publications and public repositories and promote the balance between the positive (Yang) and negative (Yin) bioactivity data. We emphasize the importance of reconsidering drug discovery initiatives regarding both the utilization and classification of data.