Optimization of the drug-likeness of chemical libraries

摘要

A scoring scheme for the classification of molecules into drugs and non-drugs was established. It was set up by using atom type descriptors for encoding the molecular structures and by training a feed-forward neural network for classifying the molecules. The approach was parameterized by using large databases of drugs and non-drugs-the Available Chemicals Directory (ACD) with 169 331 molecules and the World Drug Index (WDI) with 38 416 molecules. It was able to reveal features in the molecular descriptors that either qualify or disqualify a molecule for being a drug. The method classified about 80% of the ACD and the WDI correctly. It was extended to the aspplication for crop protection compounds and can be used to prioritize compunds for synthesis, purchase, or biological testing. An enhancement allows to optimize the drug character of combinatorial libraries.