mlsanityData to 14 Oct 2026

Accelerating Antimicrobial Peptide Discovery with Latent Structure

arXiv 2212.09450 ↗First posted 28 Nov 2022q-bio.BM cs.CE cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits multi-scale vector quantization in the latent space to represent secondary structures (e.g. alpha helix and beta sheet). By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of antimicrobial activity. Our wet laboratory experiments verified that two of the 21 candidates exhibit strong antimicrobial activity. The code is released at https://github.com/dqwang122/LSSAMP.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

16 of 54 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017Neural Discrete Representation Learningcited by 4,712
  4. 2013Auto-Encoding Variational Bayescited by 4,053
  5. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  6. 2015Generating Sentences from a Continuous Spacecited by 612
  7. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 305
  8. 2017Toward Controlled Generation of Textcited by 272
  9. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  10. 2018Fast Decoding in Sequence Models using Discrete Latent Variablescited by 104
  11. 2018Learning Multimodal Graph-to-Graph Translation for Molecular Optimizationcited by 88
  12. 2019Generating Sentences from Disentangled Syntactic and Semantic Spacescited by 59
4 more
  1. 2017A Deep Generative Framework for Paraphrase Generationcited by 54
  2. 2021Non-Autoregressive Translation by Learning Target Categorical Codescited by 25
  3. 2019Generative Models for Automatic Chemical Designcited by 23
  4. 2020Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamicscited by 7

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025PepCompass: Navigating peptide embedding spaces using Riemannian Geometrycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
  2. 2026Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3
  3. 2024HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design
  4. 2026MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design
  5. 2025A deep reinforcement learning platform for antibiotic discovery
  6. 2026ProDCARL: Reinforcement Learning-Aligned Diffusion Models for De Novo Antimicrobial Peptide Design
  7. 2026Scalable Peptide Design via Memory-Efficient Equivariant Transformer
  8. 2024Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization