ArticleJournal of chemical information and modeling2014
How accurately can we predict the melting points of drug-like compounds?
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed.
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- The use of an interpretable machine-learning approach to disclose the molecular assemblage on melting transition.Journal of molecular modeling · 2026Article
- Multi-Conformation Enhanced Equivariant Graph Neural Network: Advancing Melting Point Prediction Accuracy for Organic Small Molecules.ACS omega · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- Prediction of Melting Points of Chemicals with a Data Augmentation-Based Neural Network Approach.ACS omega · 2025Article
- Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.Chemical research in toxicology · 2025Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- Discovery of Crystallizable Organic Semiconductors with Machine Learning.Journal of the American Chemical Society · 2024Article
- Targeting highly resisted anticancer drugs through topological descriptors using VIKOR multi-criteria decision analysis.European physical journal plus · 2022Article
- Machine learning with physicochemical relationships: solubility prediction in organic solvents and water.Nature communications · 2020Article
- Transformer-CNN: Swiss knife for QSAR modeling and interpretation.Journal of cheminformatics · 2020Article
- Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges.Polymers · 2020Review
- Visual and Semantic Enrichment of Analytical Chemistry Literature Searches by Combining Text Mining and Computational Chemistry.Analytical chemistry · 2019Article
- Deep reinforcement learning for de novo drug design.Science advances · 2018Article
- Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective.ChemMedChem · 2018Article
- Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments.Chemosphere · 2018Article
- BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.Molecular informatics · 2016Article
- CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.Environmental health perspectives · 2016Article
- ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model.Chemical research in toxicology · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638.
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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.