AUTHOR=Chakravarti Suman K. , Alla Sai Radha Mani TITLE=Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks JOURNAL=Frontiers in Artificial Intelligence VOLUME=2 YEAR=2019 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2019.00017 DOI=10.3389/frai.2019.00017 ISSN=2624-8212 ABSTRACT=

Current practice of building QSAR models usually involves computing a set of descriptors for the training set compounds, applying a descriptor selection algorithm and finally using a statistical fitting method to build the model. In this study, we explored the prospects of building good quality interpretable QSARs for big and diverse datasets, without using any pre-calculated descriptors. We have used different forms of Long Short-Term Memory (LSTM) neural networks to achieve this, trained directly using either traditional SMILES codes or a new linear molecular notation developed as part of this work. Three endpoints were modeled: Ames mutagenicity, inhibition of P. falciparum Dd2 and inhibition of Hepatitis C Virus, with training sets ranging from 7,866 to 31,919 compounds. To boost the interpretability of the prediction results, attention-based machine learning mechanism, jointly with a bidirectional LSTM was used to detect structural alerts for the mutagenicity data set. Traditional fragment descriptor-based models were used for comparison. As per the results of the external and cross-validation experiments, overall prediction accuracies of the LSTM models were close to the fragment-based models. However, LSTM models were superior in predicting test chemicals that are dissimilar to the training set compounds, a coveted quality of QSAR models in real world applications. In summary, it is possible to build QSAR models using LSTMs without using pre-computed traditional descriptors, and models are far from being “black box.” We wish that this study will be helpful in bringing large, descriptor-less QSARs to mainstream use.