ArticleMolecules (Basel, Switzerland)2021
Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Graph-based transformer to predict the octanol-water partition coefficient.Journal of cheminformatics · 2026Article
- Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.Journal of chemical information and modeling · 2023Review
- LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP.Journal of cheminformatics · 2023Article
- Green Synthesis and Characterization of Novel Silver Nanoparticles UsingNanomaterials (Basel, Switzerland) · 2023Article
- Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.Journal of chemical information and modeling · 2023Article
- Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity.ACS omega · 2023Article
- Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.ACS omega · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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, such as 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.
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Registered trials
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