protein-design

A Nature research paper presents a rational design approach for intrinsically disordered proteins to investigate sequence–function relationships, introducing the open-source tool GOOSE and providing analysis scripts and data.

This bioRxiv preprint introduces Diff-Switch, a framework that uses diffusion-based ensemble sampling to generate conformational states for de novo protein switch design, improving the success rate of finding switch-compatible sequences.

AstraZeneca uses AI to generate and rank protein candidates, integrating models with experiments and robotics to speed up biologic drug discovery and tackle previously undruggable targets.

An AI tool called Raygun can scale natural proteins up or down without disrupting their structure or function, potentially advancing protein engineering.

Chamaileon introduces a framework for multi-target and multi-state protein binder design using contextualized sequence-structure co-modeling and mixed sampling, achieving adaptability across diverse conformational landscapes and multi-target requirements.

Introduces Generalized Poisson Flow (GPFlow), a variable-length generative framework for protein design that learns an inhomogeneous generalized Poisson process, enabling flexible length exploration and improving designability across structure, sequence, and peptide co-design tasks.

Design-CP introduces context-parallel inference strategies for RFdiffusion 3 that enable the all-atom design of large multimeric protein nanoparticles by distributing quadratic activations across multiple GPUs, making large-assembly protein design feasible on smaller GPU clusters.

Two Nature papers from Baker and Veesler labs describe AI-designed capsids that achieve quasi-symmetry, enabling larger protein shells than previously possible, clarifying that they designed architecture, not actual viruses.

This paper proposes a two-stage fine-tuning pipeline combining domain-adaptive fine-tuning and reinforcement learning to generate protein sequences that match a desired amino-acid composition profile while maintaining sequence quality.

A blog post discussing optimization techniques for constrained categorical probability distributions, using softmax reparameterization and log barrier methods, applied to protein binder design.

A Twitter user shares recommendations from others on the best accounts to follow for AI in biology, covering protein design, genomics, biosecurity, and policy.

Introduces MEET, a memory-efficient E(3) equivariant transformer for full-atom peptide design, integrated with a VAE and latent diffusion pipeline to achieve linear memory scaling and improved generation quality.

The Arc Institute introduces Proto, a high-level programming language for generative biology that allows programming of protein, DNA, and RNA systems via code or natural language AI prompts.

A Modal tutorial demonstrating how to scale protein binder design using ESMFold2 and ESMC models, with code for iterative optimization and autoscaling infrastructure.

ConTact introduces a contact-then-act architecture for antibody CDR design that explicitly decomposes the task into interface reasoning, contact prediction, and contact-gated sequence generation, achieving state-of-the-art structural quality and epitope awareness on the Chimera-Bench benchmark.

The article discusses growing concerns over AI tools' potential to design dangerous bioweapons, citing a recent Chinese study on conotoxin design as a flashpoint for debate between biosecurity risks and scientific benefits.

This PhD thesis introduces deep learning methods for protein complex prediction and design, including GLINTER for contact prediction, ESMPair for homolog pairing, and RedNet for binder design.

This article serves as a beginner's guide to protein lead optimization in drug design, specifically explaining the Cradle-1 pipeline and foundational concepts of protein structure and function.

TD3B is a sequence-based generative framework for designing allosteric binders with specific agonist or antagonist behaviors using transition-directed discrete diffusion. The paper introduces a method to control directional transitions in protein states, addressing limitations of static structure-based design.

This paper introduces SGRPO, a policy optimization framework that improves biomolecular generation by incorporating set-level diversity rewards alongside utility. It demonstrates improved utility-diversity trade-offs in tasks such as small-molecule and protein design.