mlsanityData to 7 Oct 2026

Graph Energy-based Model for Substructure Preserving Molecular Design

arXiv 2102.04600 ↗First posted 9 Feb 2021physics.chem-ph cs.LGcited by 4

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Abstract (arXiv)

It is common practice for chemists to search chemical databases based on substructures of compounds for finding molecules with desired properties. The purpose of de novo molecular generation is to generate instead of search. Existing machine learning based molecular design methods have no or limited ability in generating novel molecules that preserves a target substructure. Our Graph Energy-based Model, or GEM, can fix substructures and generate the rest. The experimental results show that the GEMs trained from chemistry datasets successfully generate novel molecules while preserving the target substructures. This method would provide a new way of incorporating the domain knowledge of chemists in molecular design.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

26 of 40 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 3.

4 papers.

  1. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  2. 2022Evaluating Self-Supervised Learning for Molecular Graph Embeddingscited by 22
  3. 2022Gradient-Guided Importance Sampling for Learning Binary Energy-Based Modelscited by 6
  4. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1