A Scoring Scheme for Discriminating between Drugs and Nondrugs

TLDR

The authors developed a scoring scheme for rapid, automatic classification of molecules as drugs or nondrugs. The scheme extracts knowledge from large drug and nondrug databases, encodes molecular structures with atom‑type descriptors, and trains a feedforward neural network to classify molecules. The method accurately classified 83 % of ACD and 77 % of WDI molecules, revealing discriminative features and providing a valuable, unbiased tool that can replace laborious manual screening.

Abstract

A scoring scheme for the rapid and automatic classification of molecules into drugs and nondrugs was developed. The method is a valuable new tool that can aid in the selection and prioritization of compounds from large compound collections for purchase or biological testing and that can replace a considerable amount of laborious manual work by a more unbiased approach. It is based on the extraction of knowledge from large databases of drugs and nondrugs. The method was set up by using atom type descriptors for encoding the molecular structures and by training a feedforward neural network for classifying the molecules. It was parametrized and validated by using large databases of drugs and nondrugs (169 331 molecules from the Available Chemicals Directory, ACD, and 38 416 molecules from the World Drug Index, WDI). The method revealed features in the molecular descriptors that either qualify or disqualify a molecule for being a drug and classified 83% of the ACD and 77% of the WDI adequately.