Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation-Reference-Cited by-同舟云学术

Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation

Published:2022 Issue:41 Volume:13 Page:12087-12099
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Avila Claudio¹²^ORCID,Cassani Carlo³,Kogej Thierry⁴,Mazuela Javier⁴,Sarda Sunil¹^ORCID,Clayton Adam D.²^ORCID,Kossenjans Michael³,Green Clive P.¹,Bourne Richard A.²^ORCID

Affiliation:

1. Sample Management, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, CB4 0WG, UK

2. Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK

3. Research and Early Development, Medicinal Chemistry Respiratory & Immunology, Göteborg, Sweden

4. MolecularAI, Discovery Sciences, R&D, AstraZeneca, Göteborg, Sweden

Abstract

Combining experimental stopped flow automation with machine learning strategies can deliver optimised conditions and enhanced predictions, enabling more efficient design, make, test and analysis (DMTA) cycles.

Funder

Engineering and Physical Sciences Research Council

Royal Academy of Engineering

AstraZeneca

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC03016K

Reference45 articles.

1. Diversity-oriented synthesis as a tool for the discovery of novel biologically active small molecules