<i>De novo</i> molecule design towards biased properties <i>via</i> a deep generative framework and iterative transfer learning-Reference-Cited by-同舟云学术

De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning

Published:2024 Issue:2 Volume:3 Page:410-421
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Sattari Kianoosh¹^ORCID,Li Dawei¹,Kalita Bhupalee²^ORCID,Xie Yunchao¹^ORCID,Lighvan Fatemeh Barmaleki³,Isayev Olexandr²,Lin Jian¹⁴⁵^ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering, USA

2. Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA

3. Department of Biological Sciences, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA

4. Department of Electrical Engineering and Computer Science, USA

5. Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211, USA

Abstract

The RRCGAN, validated through DFT, demonstrates success in generating chemically valid molecules targeting energy gap values with 75% of the generated molecules have RE of <20% of the targeted values.

Funder

Engineer Research and Development Center

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00210A

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