ArticleJournal of computer-aided molecular design2020
A deep learning approach for the blind logP prediction in SAMPL6 challenge.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- Synergistic optimizations of efficacy and membrane permeability of IRAK4 inhibitors: identifying new lead compounds for anti-inflammatory therapeutics.Journal of enzyme inhibition and medicinal chemistry · 2025Article
- Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.Pharmaceutical research · 2025Article
- Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches.International journal of molecular sciences · 2024Review
- Machine learning in preclinical drug discovery.Nature chemical biology · 2024Review
- Integrating multiscale and machine learning approaches towards the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2024Article
- Efficient screening of protein-ligand complexes in lipid bilayers using LoCoMock score.Journal of computer-aided molecular design · 2023Article
- MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs.Current issues in molecular biology · 2022Article
- Evaluation of log P, pKJournal of computer-aided molecular design · 2021Article
- Exploring the octanol-water partition coefficient dataset using deep learning techniques and data augmentation.Communications chemistry · 2021Article
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning.Frontiers in pharmacology · 2020Article
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2 authors.
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Abstract
Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed using molecular fingerprints and a deep neural network. The machine learning model was trained on a dataset of 12,000 molecules and tested on 2000 molecules. In this article, we present our results for the blind prediction of logP for the SAMPL6 challenge. While the best submission achieved a RMSE of 0.41 logP units, our submission had a RMSE of 0.61 logP units. Overall, we ranked in the top quarter out of the 92 submissions that were made. Our results show that the deep learning model can be used as a fast, accurate and robust method for high throughput prediction of logP of small molecules.
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