DrugOOD：分布外（OOD）数据集管理者和AI辅助药物发现的基准-专注于带有噪声注释的亲和力预测问题
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
摘要：
人工智能辅助药物发现（AIDD）越来越受欢迎，因为它有望使寻找新药更快，更便宜，更有效。尽管\emph{noise}在ADMET预测、虚拟筛选、蛋白质折叠和生成化学等许多领域有着广泛的应用，但在现实世界的AIDD应用中不可避免的分布外（OOD）学习问题方面，\emph{noise}却鲜有研究。在这项工作中，我们介绍了DrugOOD，一个系统的OOD数据集管理器和AI辅助药物发现的基准，它带有一个开源Python包，可以完全自动化数据整理和OOD基准测试过程。本论文主要研究AIDD中最关键的问题之一：药物靶点结合亲和力的预测，它涉及到大分子（蛋白质靶点）和小分子（药物化合物）。与仅提供固定数据集相比，DrugOOD提供自动化数据集管理器，具有用户友好的自定义脚本，与生物化学知识一致的丰富领域注释，逼真的噪声注释和最先进的OOD算法的严格基准测试。由于分子数据通常使用图神经网络（GNN）主干建模为不规则图，因此DrugOOD也可以作为\emph{graph OOD learning}问题的有价值的测试平台。广泛的实证研究表明，在分布和分布外的实验，这突出了需要开发更好的计划，可以允许OOD泛化噪声下的AIDD之间的显着性能差距。
英文摘要：
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its extensive use in many fields, such as ADMET prediction, virtual screening, protein folding and generative chemistry, little has been explored in terms of the out-of-distribution (OOD) learning problem with \emph{noise}, which is inevitable in real world AIDD applications. In this work, we present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery, which comes with an open-source Python package that fully automates the data curation and OOD benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction, which involves both macromolecule (protein target) and small-molecule (drug compound). In contrast to only providing fixed datasets, DrugOOD offers automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise annotations and rigorous benchmarking of state-of-the-art OOD algorithms. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for \emph{graph OOD learning} problems. Extensive empirical studies have shown a significant performance gap between in-distribution and out-of-distribution experiments, which highlights the need to develop better schemes that can allow for OOD generalization under noise for AIDD.
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。
