基于神经场的统一全原子分子生成

摘要

基于结构的药物设计生成模型通常受限于特定模态，这限制了其更广泛的应用。为解决这一难题，我们提出FuncBind——一个基于计算机视觉的框架，能够跨原子系统生成靶点条件化的全原子分子。FuncBind通过神经场将分子表示为连续原子密度，并采用基于分数的生成模型，其现代架构源自计算机视觉领域。这种模态无关的表示方法使单一统一模型能够训练于从小分子到大分子的多样化原子系统，并可处理可变原子/残基数量（包括非标准氨基酸）。在计算机模拟中，FuncBind在靶向结构条件下生成小分子、大环肽和抗体互补决定区环状结构方面展现出卓越性能。通过从头设计两个选定共晶结构的互补决定区H3环，FuncBind还成功在实验室内生成新型抗体结合剂。作为最终贡献，我们提出了用于结构条件化大环肽生成的新数据集和基准测试平台。代码详见https://github.com/prescient-design/funcbind。

English

Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score-based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody complementarity-determining region loops, conditioned on target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complementarity-determining region H3 loop of two chosen co-crystal structures. As a final contribution, we introduce a new dataset and benchmark for structure-conditioned macrocyclic peptide generation. The code is available at https://github.com/prescient-design/funcbind.

基于神经场的统一全原子分子生成

Unified all-atom molecule generation with neural fields

摘要

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