Issue 35, 2021

Virtual coformer screening by a combined machine learning and physics-based approach

Abstract

Cocrystals as a solid form technology for improving physicochemical properties have gained increasing popularity in the pharmaceutical, nutraceutical, and agrochemical industries. However, the list of potential coformers contains hundreds of molecules; far more than can be routinely screened and confirmed. Cocrystal screening experiments require significant amounts of active ingredients at an early project stage, and are expensive and time-consuming. Physics-based models and machine learning (ML) models have both been used to perform virtual cocrystal screening to guide experimental screening efforts, but both have certain limitations. Here, we present a combined ML/COSMO-RS fast virtual cocrystal screening method that proves to be significantly better than the sum of its parts in application to internal and external validation sets. To achieve that, we have defined the optimal threshold values of ML cocrystallization probability and COSMO-RS excess enthalpy of drug/coformer mixing for the combined coformer ranking. An approach to determine an applicability domain (AD) of the ML model has been implemented. The speed and accuracy of the new combined model allow it to be a good alternative to the physics-based CSP-based approach to support pharmaceutical projects with tight timeline and budget constraints.

Graphical abstract: Virtual coformer screening by a combined machine learning and physics-based approach

Supplementary files

Article information

Article type
Paper
Submitted
01 میٔ 2021
Accepted
02 جوٗلایی 2021
First published
02 جوٗلایی 2021

CrystEngComm, 2021,23, 6039-6044

Virtual coformer screening by a combined machine learning and physics-based approach

J. Yuan, X. Liu, S. Wang, C. Chang, Q. Zeng, Z. Song, Y. Jin, Q. Zeng, G. Sun, S. Ruan, C. Greenwell and Y. A. Abramov, CrystEngComm, 2021, 23, 6039 DOI: 10.1039/D1CE00587A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements