Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks.
A set of topological and structural descriptors has been used to discriminate general pharmacological activity. To that end, we selected a group of molecules with proven pharmacological activity including different therapeutic categories, and another molecule group without any activity. As a method for pharmacological activity discrimination, an artificial neural network was used, dividing molecules into active and inactive, to train the network and externally validate it. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval, and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the identification of drug and nondrug molecules. The results confirmed the discriminative capacity of the topological descriptors proposed.
D003626:Data Display; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D010600:Pharmacology / Q000379:methods*; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations