ArticleJournal of computer-aided molecular design2021
Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Graph-based transformer to predict the octanol-water partition coefficient.Journal of cheminformatics · 2026Article
- Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.Pharmaceutical research · 2025Article
- Expanded ensemble predictions of toluene-water partition coefficients in the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2025Article
- Predictive Machine Learning Models for Olfaction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Chemprop: A Machine Learning Package for Chemical Property Prediction.Journal of chemical information and modeling · 2024Article
- Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity.ACS omega · 2023Article
- Evaluation of log P, pKJournal of computer-aided molecular design · 2021Article
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2 authors.
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Abstract
Accurate prediction of lipophilicity-logP-based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps that were taken to construct a novel machine learning model that can predict and generalize well. This model is based on the recently described Directed-Message Passing Neural Networks (D-MPNNs). Further enhancements included: both the inclusion of additional datasets from ChEMBL (RMSE improvement of 0.03), and the addition of helper tasks (RMSE improvement of 0.04). To the best of our knowledge, the concept of adding predictions from other models (Simulations Plus logP and logD@pH7.4, respectively) as helper tasks is novel and could be applied in a broader context. The final model that we constructed and used to participate in the challenge ranked 2/17 ranked submissions with an RMSE of 0.66, and an MAE of 0.48 (submission: Chemprop). On other datasets the model also works well, especially retrospectively applied to the SAMPL6 challenge where it would have ranked number one out of all submissions (RMSE of 0.35). Despite the fact that our model works well, we conclude with suggestions that are expected to improve the model even further.
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