On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods-Reference-Cited by-同舟云学术

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On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods

Published:2022-03-10 Issue:6 Volume:62 Page:1388-1398
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Bolcato Giovanni¹,Heid Esther²^ORCID,Boström Jonas³^ORCID

Affiliation:

1. Molecular Modeling Section, University of Padova, 35131 Padova, Italy

2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, 02139 Massachusetts, United States

3. Medicinal Chemistry, Early CVRM, BioPharmaceuticals R&D, AstraZeneca, 431 50 Mölndal, Sweden

Funder

Austrian Science Fund

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology

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.1c01535

Reference82 articles.

1. Finding the sweet spot: the role of nature and nurture in medicinal chemistry

2. Deep learning for molecular design—a review of the state of the art

3. JCIM Special Issue on Generative Models for Molecular Design

4. Inverse molecular design using machine learning: Generative models for matter engineering

5. Computer-based de novo design of drug-like molecules

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