发表机构
Changping Laboratory（昌平实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TorchCraft通过反转全原子结构预测器，统一优化序列逻辑值，生成多种格式粘合剂，实验验证有效，扩展了设计应用范围。
AI 中文摘要
全原子结构预测器能够建模多种分子相互作用，但利用其学习到的结构先验进行粘合剂设计仍具挑战性。在此，我们提出TorchCraft，一个统一的粘合剂设计框架，通过冻结的全原子预测器优化序列逻辑值。TorchCraft在TorchFold中实现，将置信度、接触、几何和序列先验目标整合到共享优化流程中，适用于迷你粘合剂、框架条件化VHH、环肽和配体结合蛋白。利用预训练的AlphaFold 3权重，TorchCraft生成了具有实验测量结合活性的代表性迷你粘合剂和VHH，每种格式针对四个靶标，无需事后序列重设计。计算基准进一步证明了该框架在环肽和配体条件化口袋设计中的适用性。TorchCraft将预测器反转扩展到多种粘合剂格式和分子情境，为在设计重用全原子结构先验提供了通用框架。
英文摘要
All-atom structure predictors model diverse molecular interactions, but using their learned structural priors for binder design remains challenging. Here we present TorchCraft, a unified binder-design framework that optimizes sequence logits through a frozen all-atom predictor. Implemented in TorchFold, TorchCraft combines confidence, contact, geometric, and sequence-prior objectives within a shared optimization procedure for minibinders, framework-conditioned VHHs, cyclic peptides, and ligand-binding proteins. Using pretrained AlphaFold 3 weights, TorchCraft generated representative minibinders and VHHs with experimentally measured binding across four targets in each format, without post hoc sequence redesign. Computational benchmarks further demonstrated the framework's applicability to cyclic peptides and ligand-conditioned pocket design. TorchCraft extends predictor inversion to multiple binder formats and molecular contexts, providing a common framework for reusing all-atom structural priors in design.
