SIPF: Sampling Method for Inverse Protein Folding

Tianfan Fu, Jimeng Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Protein engineering has important applications in drug discovery. Among others, inverse protein folding is a fundamental task in protein design, which aims at generating protein's amino acid sequence given a 3D graph structure. However, most existing methods for inverse protein folding are based on sequential generative models and therefore limited in uncertainty quantification and exploration ability to the entire protein space. To address the issues, we propose a sampling method for inverse protein folding (SIPF). Specifically, we formulate inverse protein folding as a sampling problem and design two pretrained neural networks as Markov Chain Monte Carlo (MCMC) proposal distribution. To ensure sampling efficiency, we further design (i) an adaptive sampling scheme to select variables for sampling and (ii) an approximate target distribution as a surrogate of the unavailable target distribution. Empirical studies have been conducted to validate the effectiveness of SIPF, achieving 7.4% relative improvement on recovery rate and 6.4% relative reduction in perplexity compared to the best baseline.

Original languageEnglish (US)
Title of host publicationKDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450393850
StatePublished - Aug 14 2022
Event28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: Aug 14 2022Aug 18 2022

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining


Conference28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/TerritoryUnited States


  • drug discovery
  • inverse protein folding
  • protein design
  • protein engineering
  • sampling method

ASJC Scopus subject areas

  • Software
  • Information Systems


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