Identification of new analgesic candidates through virtual in silico screening and in vivo experimental test.

Currently, pain is closely linked to pathologies of high incidence worldwide. The in silico methods encompass all computer-aided techniques used in the design of compounds with desired properties, avoiding the high costs for the current tasks of synthesis and bioassays. In this sense, the fundamental objective of the present work is the identification of new analgesic candidates through virtual in silico screening using classification trees. For this purpose, a database of the literature is initially collected, and analgesic activity has been reported experimentally. Through the DRAGON software, a series of molecular descriptors were calculated and a Hierarchical Conglomerate Analysis (CAs) was performed in the STATISTICA software, allowing the separation of the initial database in training series and prediction series. Then we proceeded to obtain and validate the model used (Tree J48) through the WEKA software. Of these three compounds were evaluated experimentally in vivo with excellent results as analgesic drugs. In general, we can conclude that the use of these computational tools generates a great saving of resources with respect to traditional methods of analysis and also allows a rapid identification of compounds with a high probability that they are potential analgesics.