Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction

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Publication date
04.06.2020
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01A - Journal article
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Molecular Pharmaceutics
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Volume
17
Issue / Number
7
Pages / Duration
2660-2671
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American Chemical Society
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Abstract
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Modeling Pro), compared to a classical group contribution approach (Joback and Reid method), to estimate melting points and enthalpy of fusion values. A broad data set of market compounds was gathered from the literature, together with new data measured by differential scanning calorimetry for drug candidates. The highest prediction accuracy was achieved by QSPR using stochastic gradient boosting. The model deviations were discussed, particularly the implications on thermodynamic solubility modeling, as this typically requires estimation of both melting point and enthalpy of fusion. The results suggested that despite considerable advancement in prediction accuracy, there are still limitations especially with complex drug candidates. It is recommended that in such cases, melting properties obtained in silico should be used carefully as input data for thermodynamic solubility modeling. Future research will show how the prediction limits of thermophysical drug properties can be further advanced by even larger data sets and other ML algorithms or also by using molecular simulations.
Keywords
quantitative structure property relationship, machine learning, enthalpy of fusion, melting point, drug
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1543-8384
1543-8392
Language
English
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Yes
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Published
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Citation
WYTTENBACH, Nicole, Andreas NIEDERQUELL und Martin KUENTZ, 2020. Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction. Molecular Pharmaceutics. 4 Juni 2020. Bd. 17, Nr. 7, S. 2660–2671. DOI 10.1021/acs.molpharmaceut.0c00355. Verfügbar unter: https://irf.fhnw.ch/handle/11654/32432