Machine learning methods to predict the crystallization propensity of small organic molecules-Reference-Cited by-同舟云学术

Machine learning methods to predict the crystallization propensity of small organic molecules

Published:2020 Issue:16 Volume:22 Page:2817-2826
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Pereira Florbela¹²³⁴⁵^ORCID

Affiliation:

1. LAQV and REQUIMTE

2. Departamento de Química

3. Faculdade de Ciências e Tecnologia

4. Universidade Nova de Lisboa

5. Caparica

Abstract

Machine learning algorithms were explored for the prediction of the crystallization propensity based on molecular descriptors and fingerprints generated from 2D chemical structures and 3D chemical structures optimized with empirical methods.

Funder

Fundação para a Ciência e a Tecnologia

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/CE/D0CE00070A

Reference30 articles.

1. Will it crystallise? Predicting crystallinity of molecular materials

2. Predicting the Crystallization Propensity of Drug-Like Molecules

3. Revisiting the Optimal Nano‐Morphology: Towards Amorphous Organic Photovoltaics

4. Crystallization Tendency of Active Pharmaceutical Ingredients Following Rapid Solvent Evaporation—Classification and Comparison with Crystallization Tendency from Under cooled Melts

5. Long-Term Amorphous Drug Stability Predictions Using Easily Calculated, Predicted, and Measured Parameters

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