A neuro-fuzzy approach to virtual screening in molecular bioinformatics

Molecular bioinformatics is a transdisciplinary working area. One hot topic is the design of drugs using computers and intelligent algorithms. This is known as in silico approach. We use a new in silico approach for separating active ligand molecules from inactive ones for different drug targets. This kind of retrospective virtual screening is performed by using encoded molecule data and a neuro-fuzzy methodology for classification, feature selection, and rule generation. We generate rules in a retrospective screening process that identify regions, where clearly more active compounds can be found compared to their a priori probability. We show that our approach is superior to a common descriptor-based standard technique.