A unified ML framework for solubility prediction across organic solvents-Reference-Cited by-同舟云学术

A unified ML framework for solubility prediction across organic solvents

Published:2023 Issue:2 Volume:2 Page:356-367
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Vassileiou Antony D.¹^ORCID,Robertson Murray N.²^ORCID,Wareham Bruce G.³,Soundaranathan Mithushan³,Ottoboni Sara²,Florence Alastair J.²,Hartwig Thoralf⁴,Johnston Blair F.¹²⁵^ORCID

Affiliation:

1. EPSRC ARTICULAR, University of Strathclyde, Glasgow, G1 1RD, UK

2. EPSRC CMAC Future Manufacturing Hub, University of Strathclyde, Glasgow, G1 1RD, UK

3. Doctoral Training Centre in Continuous Manufacturing and Crystallisation, University of Strathclyde, Glasgow, G1 1RD, UK

4. GlaxoSmithKline, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY, UK

5. National Physical Laboratory, Teddington, TW11 0LW, UK

Abstract

A generic framework for enhancing an initial solubility prediction with ML, even with simple methods and a modestly sized, sparse dataset. We dissect the setup to show the model “locking on” to the target system as more data are made available.

Funder

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00024E

Reference39 articles.

1. Solubility Correlations of Common Organic Solvents

2. Toward a More Holistic Framework for Solvent Selection

3. Model-Based Solvent Selection during Conceptual Process Design of a New Drug Manufacturing Process

4. High throughput solubility measurement in drug discovery and development

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