英伟达发布 RFD4-Proteina 蛋白质设计教程
NVIDIA 技术博客发布教程，介绍如何用 BioNeMo 开放模型 RFD4-Proteina 设计蛋白质结合体；该模型由 NVIDIA 与华盛顿大学医学院蛋白质设计研究所（Institute for Protein Design）共同开发。模型可同时设计蛋白质序列与全原子结构，并通过奖励引导的推理时搜索把算力集中在更有希望的候选上；教程以肿瘤靶点 PD-L1 的结合体候选设计为例，面向计算生物学家、机器学习工程师和药物研发人员。用户既可选择开放模型用于训练、推理、方法开发和对采样代码的直接控制，也可使用提供 HTTP 接口的 NIM 微服务；NVIDIA 称其比 RFD3 更快、更省内存，随附论文称其在多种分子任务上达到当前最优。
对 AI 生物领域而言，这份教程给出了从开放模型、NIM 接口到智能体技能的操作路径，可用于真实肿瘤靶点的结合体设计，但性能说法来自 NVIDIA 自身。
Protein design is a search problem. Choose amino acids for an eight-residue protein sequence and more than 25 billion combinations are possible. Choose amino acids for a 100-residue protein sequence and the number of possibilities exceeds the number of atoms in the observable universe. Somewhere in that space is a protein that opens a path to a new therapy. There are too many combinations to search exhaustively, so finding it depends on knowing where to look.
RFD4-Proteina, an NVIDIA BioNeMo open model co-developed with the Institute for Protein Design at the University of Washington’s School of Medicine, navigates that space using patterns learned from nature. It co-designs protein sequences and all-atom structures, and uses reward-guided inference-time search to focus compute on the most promising candidates.
This tutorial shows computational biologists, ML engineers and drug discovery researchers how to design protein binder candidates against PD-L1, an oncology target, using reward-guided inference-time search and introduces experimental validation results across design tasks.
You can access RFD4-Proteina in two ways:
- Choose the open model for training, inference, method development, and direct control over sampling code or
- Choose the NIM microservice for rapid inference deployment with an HTTP interface
#NVIDIA#BioNeMo#RFD4-Proteina#Institute for Protein Design#University of Washington School of Medicine#NIM#RFD3#ProteinMPNN#Proteina-Complexa#OpenFold3#BioNeMo Agent Toolkit