Skip to content

Author

Jeffrey J. Gray

We have 3 of 48 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Review Open access Jul 2026

Protein engineering: status report

Abstract With this status report, we aim to provide a timely snapshot of the protein engineering field as a broad and rapidly advancing discipline that integrates computational, molecular biology, structure-guided, evolutionary, and synthetic approaches to create new and improved proteins with tailored structures and useful functions. The report is organized into eight thematic areas spanning core methodologies and major application domains, including enzymes, therapeutics, detection, synthetic biology, and materials. Contributions from experts across these areas highlight both the historical foundations and recent advances in their respective fields, with particular emphasis on the growing influence of machine learning and artificial intelligence-based methods. Emerging from this broad overview is a central message: protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain. Looking ahead, the continued integration of computational and experimental strategies is poised to further accelerate the impact of protein engineering across an expanding range of economically and societally important sectors, from therapeutics and molecular imaging to diagnostics, plastic recycling, and industrial chemistry.

Hui-wang Ai, Frances H. Arnold, Doug Barrick et al. · 0 citations
Open access Aug 2026

A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.

The AIntibody challenge shows that AI can optimize antibodies in defined, biologically grounded regimes, in addition to highlighting critical gaps including affinity prediction and library-inspired antibody design and cross-task generalization.

M. Erasmus, Daniel Bedinger, Elizabeth Hopkins et al. · 0 citations
Jul 2026

Deep Learning for Proteins Notebook Series Teaches AI for Biomolecular Structure Prediction and Design

Deep Learning for Proteins: a series of 10 interactive notebook modules that introduce fundamental machine-learning concepts, guide users through training machine-learning models for protein-related tasks, and ultimately present cutting-edge protein structure prediction and design pipelines are developed.

Michael Chungyoun, G. Au, Britnie Carpentier et al. · 0 citations