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arXiv · 2011.12117

Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation

Nina Lukashina·Alisa Alenicheva·Elizaveta Vlasova·Artem Kondiukov·Aigul Khakimova·Emil Magerramov·Nikita Churikov·Aleksei Shpilman
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Abstract

Lipophilicity is one of the factors determining the permeability of the cell membrane to a drug molecule. Hence, accurate lipophilicity prediction is an essential step in the development of new drugs. In this paper, we introduce a novel approach to encoding additional graph information by extracting molecular substructures. By adding a set of generalized atomic features of these substructures to an established Direct Message Passing Neural Network (D-MPNN) we were able to achieve a new state-of-the-art result at the task of prediction of two main lipophilicity coefficients, namely logP and logD descriptors. We further improve our approach by employing a multitask approach to predict logP and logD values simultaneously. Additionally, we present a study of the model performance on symmetric and asymmetric molecules, that may yield insight for further research.

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Nina Lukashina, Alisa Alenicheva, Elizaveta Vlasova, Artem Kondiukov, Aigul Khakimova, Emil Magerramov, Nikita Churikov, Aleksei Shpilman. 2020-11-24. Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation. https://arxiv.org/abs/2011.12117

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