Drug likeness analysis is widely used in modem drug design, However, most drug likeness filters, represented by Lipinski's "Rule of 5", are based on drugs' simple structural features and some physiochemical properties. In this study, we conducted thorough structural analyses for two drug datasets. The first dataset, ADDS, is composed of 1240 FDA-approved drugs, and the second drug dataset, EDDS, is a nonredundant collection of FDA-approved drugs and experimental drugs in different phases of clinical trials from several drug databases (6932 entries). For each molecule, all possible fragments were enumerated using a brutal force approach. Three kinds of building blocks, namely, the drug scaffold, ring system, and the small fragment, were identified and ranked according to the frequencies of their occurrence in drug molecules. The major finding is that most top fragments are essentially common for both drug datasets; the top 50 fragments cover 52.6% and 48.6% drugs for ADDS and EDDS, respectively. The identified building blocks were further ranked according to their relative hit rates in the drug datasets and in a screening dataset, which is a nonredundant collection of screening compounds from many resources. In comparison with the previous reports in the field, we have identified many more high-quality building blocks. The results obtained in this study could provide useful hints to medicinal chemists in designing drug-like compounds as well as prioritizing screening libraries to filter out those molecules lack of functional building blocks.
ASJC Scopus subject areas
- Chemical Engineering(all)
- Computer Science Applications
- Library and Information Sciences