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CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation

DOI: 10.1039/D2DD00071G DOI Help

Authors: Ruben Sanchez-Garcia (University of Oxford) , Dávid Havasi (Mcule.com Kft; Budapest University of Technology and Economics) , Gergely Takács (Mcule.com Kft; Budapest University of Technology and Economics) , Matthew C. Robinson (PostEra Inc) , Alpha Lee (PostEra Inc; University of Cambridge) , Frank Von Delft (University of Oxford; Diamond Light Source; University of Johannesburg) , Charlotte M. Deane (University of Oxford, Oxford)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Digital Discovery , VOL 10

State: Published (Approved)
Published: November 2022

Open Access Open Access

Abstract: Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available molecules has dramatically increased over the last decade, redefining the problem of compound accessibility as a matter of budget. In this paper we show that if compound prices are the desired proxy for compound availability, then synthetic accessibility scores are not effective strategies for us in selection. Our approach, CoPriNet, is a retrosynthesis-free deep learning model trained on 2D graph representations of compounds alongside their prices extracted from the Mcule catalogue. We show that CoPriNet provides price predictions that correlate far better with actual compound prices than any synthetic accessibility score. Moreover, unlike standard retrosynthesis methods, CoPriNet is rapid, with execution times comparable to popular synthetic accessibility metrics, and thus is suitable for high-throughput experiments including virtual screening and de novo compound generation. While the Mcule catalogue is a proprietary dataset, the CoPriNet source code and the model trained on the proprietary data as well as the fraction of the catalogue (100 K compound/prices) used as test dataset have been made publicly available at https://github.com/oxpig/CoPriNet.

Subject Areas: Information and Communication Technology, Medicine


Technical Areas:

Added On: 23/12/2022 10:22

Documents:
d2dd00071g.pdf

Discipline Tags:

Artificial Intelligence Health & Wellbeing Information & Communication Technologies Data processing Drug Discovery Life Sciences & Biotech

Technical Tags: