MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning-Reference-Cited by-同舟云学术

MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning

Published:2023-04-14 Issue:9 Volume:63 Page:2667-2678
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Buterez David¹^ORCID,Janet Jon Paul²^ORCID,Kiddle Steven J.³,Liò Pietro¹

Affiliation:

1. Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, U.K.

2. Molecular AI, Discovery Sciences, R&D, AstraZeneca, 431 50 Gothenburg, Sweden

3. Data Science & Advanced Analytics, Data Science & Artificial Intelligence, R&D, AstraZeneca, Cambridge CB2 8PA, U.K.

Funder

AstraZeneca

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c01569

Reference31 articles.

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2. OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

3. A Deep Learning Approach to Antibiotic Discovery

4. MoleculeNet: a benchmark for molecular machine learning

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