Papyrus - A large scale curated dataset aimed at bioactivity predictions

This repository contains the Papyrus dataset, an aggregated dataset of small molecule bioactivities, as described in the manuscript "Papyrus - A large scale curated dataset aimed at bioactivity predictions" (Work in Progress).With the recent rapid growth of publicly available ligand-protein bioactivity data, there is a trove of viable data that can be used to train machine learning algorithms. However, not all data is equal in terms of size and quality, and a significant portion of researcher’s time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. As an answer to that, we have constructed the Papyrus dataset, comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with smaller datasets containing high quality data. This aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways, and also perform some rudimentary quantitative structure-activity relationship and proteochemometrics modeling. Our ambition is to create a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.

本仓库收录了Papyrus数据集——一款小分子生物活性数据聚合数据集，正如手稿《Papyrus——面向生物活性预测的大规模整理数据集》（工作进行中）所阐述的那样。近年来，公开可获取的配体-蛋白质生物活性数据迅猛增长，蕴藏着海量可用于训练机器学习算法的可用数据资源。然而，这些数据在规模与质量层面参差不齐，研究人员往往需要耗费大量时间对数据进行适配以满足自身研究需求。除此之外，针对特定研究问题筛选合适的数据本身，也常常是一项颇具挑战的工作。针对上述痛点，我们构建了Papyrus数据集，其涵盖约6000万条数据点。该数据集整合了ChEMBL、ExCAPE-DB等多个大型公开数据集，以及若干高质量小型数据集。所有聚合后的数据均已完成标准化与归一化处理，以适配机器学习应用场景。我们演示了如何通过多种方式对数据进行筛选，并开展了初步的定量构效关系 (quantitative structure-activity relationship) 与蛋白化学计量学 (proteochemometrics) 建模研究。我们的目标在于构建一套可用于构建预测模型的基准数据集，同时为相关领域的研究提供坚实的基准参照。