mlsanityData to 7 Oct 2026

Molecule Generation for Drug Design: a Graph Learning Perspective

arXiv 2202.09212 ↗First posted 18 Feb 2022cs.LG cs.AIcited by 13

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Abstract (arXiv)

Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on de novo drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: i) all-at-once, ii) fragment-based, and iii) node-by-node. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 68 of its 144 references that are in the map. First posted on arXiv 18 Feb 2022.

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68 of 144 references.

References · 68

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Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 3.

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  1. 2025A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimizationcited by 12
  2. 2024Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unificationcited by 8
  3. 2023MAGNet: Motif-Agnostic Generation of Molecules from Shapescited by 7
  4. 2023The power of motifs as inductive bias for learning molecular distributionscited by 6
  5. 2025Graph Guided Diffusion: Unified Guidance for Conditional Graph Generationcited by 5
  6. 2024A Review on Fragment-based De Novo 2D Molecule Generationcited by 3
  7. 2026Prior-Informed Flow Matching for Graph Reconstructioncited by 3
  8. 2025DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platformcited by 1
  9. 2025Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMscited by 1
  10. 2024Graph Residual based Method for Molecular Property Predictioncited by 0
  11. 2025Characterizing the Multiclass Learnability of Forgiving 0-1 Loss Functionscited by 0
  12. 2026Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generationcited by 0
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  1. 2026SWING: Unlocking Implicit Graph Representations for Graph Random Featurescited by 0