De Novo Molecular Generation Using Deep Learning for Prioritizing Synthesizability-Reference-Cited by-同舟云学术

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De Novo Molecular Generation Using Deep Learning for Prioritizing Synthesizability

Published:2023-10-06 Issue: Volume:3 Page:1-5
ISSN:
Container-title:2023 IEEE MIT Undergraduate Research Technology Conference (URTC)
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Author:

Inamdar ¹,Jakhotiya R.²,Jakhotiya K.³

Affiliation:

1. Secaucus High School STEM Academy, Secuaucus High School,Secaucus,United States of America

2. Secaucus High School STEM Academy, Secaucus High School,Secuacus,United States of America

3. Rutgers University,Rutgers Department of Biomedical Engineering,New Brunswick,United States of America

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10534894/10534885/10535017.pdf?arnumber=10535017

Reference22 articles.

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

2. Virtual screening strategies in drug discovery

3. Molecular de-novo design through deep reinforcement learning

4. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

5. ChEMBL: a large-scale bioactivity database for drug discovery

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