Predicting pharmaceutical crystal morphology using artificial intelligence-Reference-Cited by-同舟云学术

Predicting pharmaceutical crystal morphology using artificial intelligence

Published:2022 Issue:43 Volume:24 Page:7545-7553
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Wilkinson Matthew R.¹²³^ORCID,Martinez-Hernandez Uriel⁴^ORCID,Huggon Laura K.¹^ORCID,Wilson Chick C.²⁵^ORCID,Castro Dominguez Bernardo¹²³^ORCID

Affiliation:

1. Department of Chemical Engineering, University of Bath, Claverton Down, Bath, BA2 7AY, UK

2. EPSRC Future Continuous Manufacturing and Advanced Crystallisation Research Hub (CMAC), University of Bath, Claverton Down, Bath, BA2 7AY, UK

3. Centre for Sustainable and Circular Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, UK

4. Centre for Autonomous Robotics (CENTAUR), Department of Electronic and Electrical Engineering, University of Bath, Claverton Down, Bath, BA2 7AY, UK

5. Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK

Abstract

We present the use of artificial intelligence to predict the morphology of crystallizing active pharmaceutical ingredients, first using publicly available data, and then using our own screening efforts to address the limitations we identified.

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CE/D2CE00992G

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