GraphVF：基于变分流的可控蛋白质特异性3D分子生成
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
AI总结：
本文提出 GraphVF 框架，融合2D拓扑与3D几何的变分流方法，实现带定制子结构和理化性质约束的蛋白质特异性3D结合分子生成，并取得最优结合亲和力。
摘要：
设计能够与特定靶标蛋白结合的分子是药物发现中的一项基础任务。近期模型利用几何约束来生成与特定蛋白口袋紧密结合的配体分子。然而，这些模型无法有效生成带有2D骨架约束和性质约束的3D分子，而这些约束对药物效力和开发至关重要。为应对这一挑战，我们提出 GraphVF，这是一种基于变分流的框架，结合2D拓扑与3D几何，用于可控生成具有结合能力的3D分子。实验上，我们的方法在蛋白质特异性生成中取得了最先进的结合亲和力和真实的子结构布局。特别是，GraphVF 代表了首个可控的、具备几何感知能力的蛋白质特异性分子生成方法，能够生成具有定制子结构和物理化学性质的可结合3D分子。我们的代码可在 https://github.com/Franco-Solis/GraphVF-code 获取。
英文摘要：
Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent models leverage geometric constraints to generate ligand molecules that bind cohesively with specific protein pockets. However, these models cannot effectively generate 3D molecules with 2D skeletal curtailments and property constraints, which are pivotal to drug potency and development. To tackle this challenge, we propose GraphVF, a variational flow-based framework that combines 2D topology and 3D geometry, for controllable generation of binding 3D molecules. Empirically, our method achieves state-of-the-art binding affinity and realistic sub-structural layouts for protein-specific generation. In particular, GraphVF represents the first controllable geometry-aware, protein-specific molecule generation method, which can generate binding 3D molecules with tailored sub-structures and physio-chemical properties. Our code is available at https://github.com/Franco-Solis/GraphVF-code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
