ArticleCommunications chemistry2021
Exploring the octanol-water partition coefficient dataset using deep learning techniques and data augmentation.
Article in Communications chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Prediction of Solute Descriptors for Linear Solvation Energy Relationships Using K‑Nearest Neighbors, Group Contributions, and Graph-Convolutional Neural Networks.ACS environmental Au · 2026Article
- From static compromise to dynamic design: a polarity modulation paradigm for multiclass veterinary drug residue analysis in complex foods.Food chemistry: X · 2026Review
- graphpancake: a Python package for representing organic molecules as molecular graphs utilizing electronic structure theory.Journal of cheminformatics · 2026Article
- The potential of plant-derived triterpenoids as biological nitrification inhibitors.Applied microbiology and biotechnology · 2026Article
- 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
- Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025Article
- Target-aware 3D molecular generation based on guided equivariant diffusion.Nature communications · 2025Article
- Prediction of Melting Points of Chemicals with a Data Augmentation-Based Neural Network Approach.ACS omega · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset.Journal of cheminformatics · 2025Article
- Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.Pharmaceutical research · 2025Article
- Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity.Environmental science & technology · 2025Article
- Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach.Molecules (Basel, Switzerland) · 2025Article
- FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.Molecular pharmaceutics · 2025Article
- Recent Advances on Starch-Based Adsorbents for Heavy Metal and Emerging Pollutant Remediation.Polymers · 2024Review
- Serotype switching in Pseudomonas aeruginosa ST111 enhances adhesion and virulence.PLoS pathogens · 2024Article
- Machine learning model to predict rate constants for sonochemical degradation of organic pollutants.Ultrasonics sonochemistry · 2024Article
- Beware of extreme calculated lipophilicity when designing cyclic peptides.Nature chemical biology · 2024Article
- Assessment of the Lipophilicity of Indole Derivatives of Betulin and Their Toxicity in a Zebrafish Model.Molecules (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.