Could deep learning in neural networks improve the QSAR models?. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: Gini, G.; Zanoli, F.; Gamba, A.; Raitano, G.; Benfenati, E.
• Is Part Of: SAR and QSAR in environmental research. Volume 30:Issue 9(2019)
• Page Start: 617
• Page End: 642
• Publication Date: 2019-09-02
• Subjects: Classification -- feature generation -- deep neural networks -- Ames test -- mutagenicity; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2019.1650827
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Assessing chemical toxicity is a multidisciplinary process, traditionally involving in vivo, in vitro and in silico tests. Currently, toxicological goal is to reduce new tests on chemicals, exploiting all information yet available. Recent advancements in machine learning and deep neural networks allow computers to automatically mine patterns and learn from data. This technology, applied to (Q)SAR model development, leads to discover by learning the structural-chemical-biological relationships and the emergent properties. Starting from Toxception, a deep neural network predicting activity from the chemical graph image, we designed SmilesNet, a recurrent neural network taking SMILES as the only input. We then integrated the two networks into C-Tox network to make the final classification. Results of our networks, trained on a ~20K molecule dataset with Ames test experimental values, match or even outperform the current state of the art. We also extract knowledge from the networks and compare it with the available mutagenic structural alerts. The advantage over traditional QSAR modelling is that our models automatically extract the features without using descriptors. Nevertheless, the model is successful if large numbers of examples are provided and computation is more complex than in classical methods.