Machine learning meets pKa
- Publication type:
- Journal article
- Metadata:
-
- Abstract
- <ns4:p>We present a small molecule pK<ns4:sub>a</ns4:sub> prediction tool entirely written in Python. It predicts the macroscopic pK<ns4:sub>a</ns4:sub> value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r<ns4:sup>2</ns4:sup> =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at <ns4:ext-link xmlns:ns3="http://www.w3.org/1999/xlink" ext-link-type="uri" ns3:href="https://github.com/czodrowskilab/Machine-learning-meets-pKa">https://github.com/czodrowskilab/Machine-learning-meets-pKa</ns4:ext-link>.</ns4:p>
- Autoren
- Marcel Baltruschat
- Paul Czodrowski
- DOI
- 10.12688/f1000research.22090.1
- eISSN
- 2046-1402
- Zeitschrift
- F1000Research
- Sprache
- en
- Online publication date
- 2020
- Paginierung
- 113 - 113
- Status
- Published online
- Herausgeber
- F1000 Research Ltd
- Herausgeber URL
- http://dx.doi.org/10.12688/f1000research.22090.1
- Datum der Datenerfassung
- 2020
- Titel
- Machine learning meets pKa
- Ausgabe der Zeitschrift
- 9
Data source: Crossref
- Other metadata sources:
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- Beziehungen:
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