Literature DB >> 25489863

How accurately can we predict the melting points of drug-like compounds?

Igor V Tetko1, Yurii Sushko, Sergii Novotarskyi, Luc Patiny, Ivan Kondratov, Alexander E Petrenko, Larisa Charochkina, Abdullah M Asiri.   

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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Year:  2014        PMID: 25489863      PMCID: PMC4702524          DOI: 10.1021/ci5005288

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  32 in total

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3.  Associative neural network.

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Journal:  Altern Lab Anim       Date:  2014-03       Impact factor: 1.303

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Authors:  Igor V Tetko; Iurii Sushko; Anil Kumar Pandey; Hao Zhu; Alexander Tropsha; Ester Papa; Tomas Oberg; Roberto Todeschini; Denis Fourches; Alexandre Varnek
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8.  Development of dimethyl sulfoxide solubility models using 163,000 molecules: using a domain applicability metric to select more reliable predictions.

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Journal:  J Chem Inf Model       Date:  2013-07-15       Impact factor: 4.956

9.  Using beta binomials to estimate classification uncertainty for ensemble models.

Authors:  Robert D Clark; Wenkel Liang; Adam C Lee; Michael S Lawless; Robert Fraczkiewicz; Marvin Waldman
Journal:  J Cheminform       Date:  2014-06-22       Impact factor: 5.514

10.  Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM).

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Journal:  J Cheminform       Date:  2013-01-15       Impact factor: 5.514

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  13 in total

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2.  Transformer-CNN: Swiss knife for QSAR modeling and interpretation.

Authors:  Pavel Karpov; Guillaume Godin; Igor V Tetko
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3.  Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments.

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4.  CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

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Journal:  Environ Health Perspect       Date:  2016-02-23       Impact factor: 9.031

5.  Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective.

Authors:  Michael Withnall; Hongming Chen; Igor V Tetko
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6.  Deep reinforcement learning for de novo drug design.

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7.  The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS.

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Journal:  J Cheminform       Date:  2016-01-22       Impact factor: 5.514

8.  BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.

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9.  ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model.

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Journal:  Chem Res Toxicol       Date:  2016-04-27       Impact factor: 3.739

10.  Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds.

Authors:  Elena S Salmina; Norbert Haider; Igor V Tetko
Journal:  Molecules       Date:  2015-12-23       Impact factor: 4.411

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