A new age in protein design empowered by deep learning

Hamed Khakzad, Ilia Igashov, Arne Schneuing, Casper Goverde, Michael Bronstein, Bruno Correia*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.

Original languageEnglish
Pages (from-to)925-939
Number of pages15
JournalCell Systems
Issue number11
StatePublished - 15 Nov 2023
Externally publishedYes


  • deep generative models
  • deep learning
  • protein design
  • protein language models


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