Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
04.06.2020
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Molecular Pharmaceutics
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
17
Ausgabe / Nummer
7
Seiten / Dauer
2660-2671
Patentnummer
Verlag / Herausgebende Institution
American Chemical Society
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
quantitative structure property relationship, machine learning, enthalpy of fusion, melting point, drug
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1543-8384
1543-8392
1543-8392
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Lizenz
Zitation
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