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The article reports on AI BioDesign, a project using artificial intelligence and experiments to create molecules not found in nature, led by Nobel laureate David Baker, aiming to advance medicine and technology.
Adaptyv Bio and Anthropic have partnered to launch a global Protein Design Competition, using AI to design new drug candidates with over $2 million in support and open data sharing.
Anthropic optimized inference for over 30 open-source biomolecular models using Claude, achieving an average 4x speedup, and is open-sourcing the optimization code. They are also partnering with Adaptyv Bio on a protein design competition.
OpenAI has shut down a protein design project, prompting the user to turn to open weight models for biological research.
ABLE is a benchmark for evaluating LLM agents' ability to use biological AI models like ProteinMPNN and AlphaFold3 in protein design workflows. It assesses 15 frontier models, finding that Claude Sonnet 4 and Gemini 3 Pro achieve the highest scores, while some models refuse all tasks.
GPT-6 Astra, an AI model, is viral for its diverse applications in gaming, protein design, sports tracking, and 3D world building, highlighted by 10 stunning cases.
SimpleDesign introduces a joint model for protein sequence and structure codesign, trained end-to-end in data space using a single-stage objective, achieving competitive performance on co-design and generation benchmarks.
MIT researchers have developed PottsMPNN, a machine-learning framework that incorporates physical principles to improve protein sequence generation and stability prediction, enabling the design of novel proteins beyond native sequences.
This paper explores using large language models to generate ranking policies for shortlisting protein binders from candidate pools, showing modest improvements over baseline methods in de novo design workflows.
Anthropic's Claude AI autonomously designs disease-targeting proteins with a 35% success rate in wet-lab validation, surpassing the human average of 10-15%.
DeepSCan is a new AI model that learns the rules of cell-surface display and generates novel membrane display modules, several outperforming strong natural sequences, marking a step toward programmable cellular engineering.
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.