ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2107.08987
  4. Cited By
Analysis of training and seed bias in small molecules generated with a
  conditional graph-based variational autoencoder -- Insights for practical
  AI-driven molecule generation

Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation

19 July 2021
Seung-gu Kang
Joseph A. Morrone
J. Weber
Wendy D. Cornell
ArXivPDFHTML

Papers citing "Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation"

2 / 2 papers shown
Title
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative
  Small-Molecule Creation
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation
Seung-gu Kang
J. Weber
Joseph A. Morrone
Leili Zhang
T. Huynh
Wendy D. Cornell
DiffM
21
3
0
05 Apr 2022
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
239
1,340
0
12 Feb 2018
1