mlsanityData to 7 Oct 2026

PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

arXiv 2302.07120 ↗First posted 14 Feb 2023cs.AI cs.LG q-bio.BMcited by 7

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Abstract (arXiv)

Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties? Although target-aware generative models have made significant advances in drug design, they do not consider chemistry conditions and cannot guarantee the desired chemical properties. Unfortunately, merging the target-aware and chemical-aware models into a unified model to meet customized requirements may lead to the problem of negative transfer. Inspired by the success of multi-task learning in the NLP area, we use prefix embeddings to provide a novel generative model that considers both the targeted pocket's circumstances and a variety of chemical properties. All conditional information is represented as learnable features, which the generative model subsequently employs as a contextual prompt. Experiments show that our model exhibits good controllability in both single and multi-conditional molecular generation. The controllability enables us to outperform previous structure-based drug design methods. More interestingly, we open up the attention mechanism and reveal coupling relationships between conditions, providing guidance for multi-conditional molecule generation.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 51 of its 84 references that are in the map. First posted on arXiv 14 Feb 2023.

Builds on

51 of 84 references.

References · 51

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
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38 more
  1. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  2. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  3. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
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  5. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 171
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  11. 2022Diffusion-based Molecule Generation with Informative Prior Bridgescited by 104
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  22. 2020Reinforcement Learning for Molecular Design Guided by Quantum Mechanicscited by 51
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  24. 2022Retrieval-based Controllable Molecule Generationcited by 44
  25. 2022AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBcited by 39
  26. 2020Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Modelscited by 33
  27. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
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  31. 2022Target-aware Molecular Graph Generationcited by 18
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50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  2. 2024From Words to Molecules: A Survey of Large Language Models in Chemistrycited by 14
  3. 2023Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinementcited by 11
  4. 2023Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designercited by 8
  5. 2024Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generationcited by 5
  6. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  7. 2026GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embeddingcited by 0