De novo design of high-affinity protein binders with AlphaProteo
read the original abstract
Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization.
This paper has not been read by Pith yet.
Forward citations
Cited by 6 Pith papers
-
A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion
A-CODE presents a fully atomic one-stage multimodal diffusion model for protein co-design that claims superior unconditional generation performance over prior one- and two-stage models plus a tenfold success-rate gain...
-
ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
ProtDBench is a new evaluation benchmark that standardizes protein binder design assessment, reveals verifier-dependent bias in structure predictors, and compares generative methods under fixed 24-hour and diversity-a...
-
ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
ProtDBench standardizes protein binder design evaluation using wet-lab data, exposing verifier biases, metric dependencies, and trade-offs between success rate, speed, and structural diversity.
-
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
Proteo-R1 decouples an MLLM-based understanding expert that selects functional residues from a diffusion-based generation expert that builds protein structures under those explicit constraints.
-
Towards an AI co-scientist
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
-
ADIOS: Antibody Development via Opponent Shaping
ADIOS applies opponent shaping in a meta-learning setup to create antibodies that target current and future viral variants while biasing evolution toward weaker strains, demonstrated in Absolut! simulations.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.