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How Accurately Can We Predict the Melting Points of Drug-like Compounds?
[Image: see text] 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 show...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702524/ https://www.ncbi.nlm.nih.gov/pubmed/25489863 http://dx.doi.org/10.1021/ci5005288 |
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author | Tetko, Igor V. Sushko, Yurii Novotarskyi, Sergii Patiny, Luc Kondratov, Ivan Petrenko, Alexander E. Charochkina, Larisa Asiri, Abdullah M. |
author_facet | Tetko, Igor V. Sushko, Yurii Novotarskyi, Sergii Patiny, Luc Kondratov, Ivan Petrenko, Alexander E. Charochkina, Larisa Asiri, Abdullah M. |
author_sort | Tetko, Igor V. |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-4702524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-47025242016-01-19 How Accurately Can We Predict the Melting Points of Drug-like Compounds? Tetko, Igor V. Sushko, Yurii Novotarskyi, Sergii Patiny, Luc Kondratov, Ivan Petrenko, Alexander E. Charochkina, Larisa Asiri, Abdullah M. J Chem Inf Model [Image: see text] 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. American Chemical Society 2014-12-09 2014-12-22 /pmc/articles/PMC4702524/ /pubmed/25489863 http://dx.doi.org/10.1021/ci5005288 Text en Copyright © 2014 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Tetko, Igor V. Sushko, Yurii Novotarskyi, Sergii Patiny, Luc Kondratov, Ivan Petrenko, Alexander E. Charochkina, Larisa Asiri, Abdullah M. How Accurately Can We Predict the Melting Points of Drug-like Compounds? |
title | How Accurately Can We Predict the Melting Points of
Drug-like Compounds? |
title_full | How Accurately Can We Predict the Melting Points of
Drug-like Compounds? |
title_fullStr | How Accurately Can We Predict the Melting Points of
Drug-like Compounds? |
title_full_unstemmed | How Accurately Can We Predict the Melting Points of
Drug-like Compounds? |
title_short | How Accurately Can We Predict the Melting Points of
Drug-like Compounds? |
title_sort | how accurately can we predict the melting points of
drug-like compounds? |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702524/ https://www.ncbi.nlm.nih.gov/pubmed/25489863 http://dx.doi.org/10.1021/ci5005288 |
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