CBGBench: 填补蛋白质-分子复合物结合图的空白
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
AI总结：
针对基于结构的药物设计领域缺乏标准化基准、任务单一的问题，提出CBGBench综合基准，将任务统一为3D复合物结合图补全，覆盖多类药物设计子任务并提供公平评估与预训练模型。
摘要：
基于结构的药物设计（SBDD）旨在生成能够与靶标蛋白结合的潜在药物，而人工智能技术在生成模型中的应用极大加速了这一进程。然而，由于实验设置多样、实现过程复杂、可复现性差以及任务单一，该领域仍缺乏系统性的认知。首先，缺乏标准化会导致不公平的比较和无法得出确定结论的洞见。为解决这一困境，我们提出CBGBench，一个面向SBDD的综合基准，它将该任务统一为生成式异构图补全，类似于3D复合物结合图的填空任务。通过根据现有方法的属性对其进行分类，CBGBench构建了一个模块化且可扩展的框架，可实现多种前沿方法。其次，仅针对从头分子生成的单一任务几乎无法反映这些模型的能力。为拓宽评估范围，我们将这些模型适配到药物设计中一系列关键任务上，这些任务都被视为图填空任务的子任务，包括以蛋白口袋结构为条件的从头分子、连接子、片段、骨架和侧链的生成设计。我们的评估秉持公平性，涵盖相互作用、化学性质、几何真实性和子结构有效性等综合维度。我们还提供了最先进模型的预训练版本，并通过实证研究分析给出了深刻洞见。CBGBench的代码库可在https://github.com/Edapinenut/CBGBench公开获取。
英文摘要：
Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse settings, complex implementation, difficult reproducibility, and task singularity. Firstly, the absence of standardization can lead to unfair comparisons and inconclusive insights. To address this dilemma, we propose CBGBench, a comprehensive benchmark for SBDD, that unifies the task as a generative heterogeneous graph completion, analogous to fill-in-the-blank of the 3D complex binding graph. By categorizing existing methods based on their attributes, CBGBench facilitates a modular and extensible framework that implements various cutting-edge methods. Secondly, a single task on \textit{de novo} molecule generation can hardly reflect their capabilities. To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks. These tasks include the generative designation of \textit{de novo} molecules, linkers, fragments, scaffolds, and sidechains, all conditioned on the structures of protein pockets. Our evaluations are conducted with fairness, encompassing comprehensive perspectives on interaction, chemical properties, geometry authenticity, and substructure validity. We further provide the pre-trained versions of the state-of-the-art models and deep insights with analysis from empirical studies. The codebase for CBGBench is publicly accessible at \url{https://github.com/Edapinenut/CBGBench}.
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。
