Generative models for molecular discovery: Recent advances and challenges-Reference-Cited by-同舟云学术

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Generative models for molecular discovery: Recent advances and challenges

Published:2022-03-05 Issue:5 Volume:12 Page:
ISSN:1759-0876
Container-title:WIREs Computational Molecular Science
language:en
Short-container-title:WIREs Comput Mol Sci

Author:

Bilodeau Camille¹,Jin Wengong²,Jaakkola Tommi²,Barzilay Regina²,Jensen Klavs F.¹^ORCID

Affiliation:

1. Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA

2. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA

Funder

Defense Advanced Research Projects Agency

Dow Chemical Company

Publisher

Wiley

Subject

Materials Chemistry,Computational Mathematics,Physical and Theoretical Chemistry,Computer Science Applications,Biochemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1608

Reference118 articles.

1. Estimation of the size of drug-like chemical space based on GDB-17 data

2. Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database

3. Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

4. GuacaMol: Benchmarking Models for de Novo Molecular Design

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

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1. DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search;Journal of Chemical Information and Modeling;2024-09-09

2. Large Language Models as Molecular Design Engines;Journal of Chemical Information and Modeling;2024-09-04

3. Navigating the frontier of drug-like chemical space with cutting-edge generative AI models;Drug Discovery Today;2024-09

4. CLigOpt: controllable ligand design through target-specific optimization;Bioinformatics;2024-09-01

5. Latent Diffusion for Conditional Generation of Molecules;2024-08-22

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