Working Paper Article Version 2 This version is not peer-reviewed

Improved Lipophilicity and Aqueous Solubility Prediction With Composite Graph Neural Networks

Version 1 : Received: 30 August 2021 / Approved: 30 August 2021 / Online: 30 August 2021 (14:29:43 CEST)
Version 2 : Received: 1 October 2021 / Approved: 1 October 2021 / Online: 1 October 2021 (14:29:01 CEST)

A peer-reviewed article of this Preprint also exists.

Wieder, O.; Kuenemann, M.; Wieder, M.; Seidel, T.; Meyer, C.; Bryant, S.D.; Langer, T. Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks. Molecules 2021, 26, 6185. Wieder, O.; Kuenemann, M.; Wieder, M.; Seidel, T.; Meyer, C.; Bryant, S.D.; Langer, T. Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks. Molecules 2021, 26, 6185.

Journal reference: Molecules 2021, 26, 6185
DOI: 10.3390/molecules26206185

Abstract

The accurate prediction of molecular properties such as lipophilicity and aqueous solubility are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods like graph-based neural networks (GNNs) have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.

Keywords

AI; deep-learning; neural-networks; graph neural-networks; cheminformatics; molecular property; machine-learning; computational chemistry; lipophilicity; solubility

Comments (1)

Comment 1
Received: 1 October 2021
Commenter: Oliver Wieder
Commenter's Conflict of Interests: Author
Comment: Refined several key parts to sharpen the main message of this manuscript
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