Unsupervised Learning Based on Deep Learning Applied to the Identification of Applicability Domain of QSAR Models

MONICA F. DIAZ; MARÍA JIMENA MARTÍNEZ; IGNACIO PONZONI; FIORELLA CRAVERO

Buenos Aires

Congreso; fourth International Society for Computational Biology Latin America Bioinformatics Conference (ISCB-LA); 2016

ISCB-LA

For a QSAR/QSPR (Quantitative Structure-Activity Relationship) model is a key point to define their Applicability Domain (AD). Namely, know for which compounds the predictions will be reliable and which are not. Only compounds that fall within this domain could be evaluated reliably. Furthermore, if there is a new compound to predict, we could know whether this one will have a precision similar (or not) to that reported in the method validation. A strategy to define the applicability domain of a model is to organize the process in two stages. In the first stage, the compounds of the training set that are structurally similar to the new compound are identified, for this the data should be grouped by similarity. To identify these groups of data there are many methods, but in which we focus here is deep learning, which is a machine learning technique that combine different unsupervised methods. Finally, in the second stage of the process, it proceeds to evaluate the ability of the predictor on the new compound, applying statistical tests designed for this purpose.