ArticleJournal of chemical information and modeling2017
In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.
Article in Journal of chemical information and modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed.
- Article
- Application of high-precision solubility prediction models in the assisted design of drug-like compounds.Molecular diversity · 2026Article
- Enhancing ADMET property predictions using cross-aligned multimodal attention mechanisms.Molecular diversity · 2026Article
- Geometry-aware lightweight convolutional network for efficient molecular property prediction.Scientific reports · 2025Article
- Chemosensory tobacco product toxicology part 2: toxicological testing, assays, and state of the science.Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
- Predicting cardiotoxicity in drug development: A deep learning approach.Journal of pharmaceutical analysis · 2025Article
- Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.Molecular diversity · 2025Article
- Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.Pharmaceutical research · 2025Article
- Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.AAPS PharmSciTech · 2025Article
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- Computational advances in the design and discovery of artemis inhibitors for radiosensitization in cancer therapy.Frontiers in chemistry · 2025Article
- Overview of Molecular Modeling in Drug Discovery with a Special Emphasis on the Applications of Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Assessing human exposure to organic contaminants in fish: A modeling approach integrating chemical bioconcentration and food thermal processing.Toxicology reports · 2024Article
- Advances in artificial intelligence-based technologies for increasing the quality of medical products.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Review
- Article
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- Correlation gas chromatography and two-dimensional volatility basis methods to predict gas-particle partitioning for e-cigarette aerosols.Aerosol science and technology : the journal of the American Association for Aerosol Research · 2024Article
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach.Amino acids · 2023Article
- Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants.Molecules (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the federal Tox21 research program, can generate biological data to inform models for predicting potential toxicity. However, physicochemical properties are also needed to model environmental fate and transport, as well as exposure potential. The purpose of the present study was to generate an open-source quantitative structure-property relationship (QSPR) workflow to predict a variety of physicochemical properties that would have cross-platform compatibility to integrate into existing cheminformatics workflows. In this effort, decades-old experimental property data sets available within the EPA EPI Suite were reanalyzed using modern cheminformatics workflows to develop updated QSPR models capable of supplying computationally efficient, open, and transparent HTS property predictions in support of environmental modeling efforts. Models were built using updated EPI Suite data sets for the prediction of six physicochemical properties: octanol-water partition coefficient (logP), water solubility (logS), boiling point (BP), melting point (MP), vapor pressure (logVP), and bioconcentration factor (logBCF). The coefficient of determination (R
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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.