Multi-constraint molecular generation using sparsely labelled training data for localized high-concentration electrolyte diluent screening-Reference-Cited by-同舟云学术

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Multi-constraint molecular generation using sparsely labelled training data for localized high-concentration electrolyte diluent screening

Published:2023 Issue:5 Volume:2 Page:1390-1403
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Mailoa Jonathan P.¹^ORCID,Li Xin¹,Qiu Jiezhong¹,Zhang Shengyu²

Affiliation:

1. Tencent Quantum Laboratory, Tencent, Shenzhen, Guangdong, China

2. Tencent Quantum Laboratory, Tencent, Hong Kong SAR, China

Abstract

We use a mixture of incomplete-labelled molecule property databases to conditionally generate new molecules with multiple property co-constraints.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00064H

Reference43 articles.

1. A deep generative model enables automated structure elucidation of novel psychoactive substances

2. Structure-based de novo drug design using 3D deep generative models

3. A de novo substructure generation algorithm for identifying the privileged chemical fragments of liver X receptorβ agonists

4. A de novo molecular generation method using latent vector based generative adversarial network

5. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

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