Bioactivity data plays a key role in drug discovery and repurposing. The
resource-demanding nature of \textit{in vitro} and \textit{in vivo}
experiments, as well as the recent advances in data-driven computational
biochemistry research, highlight the importance of \textit{in silico} drug
target interaction (DTI) prediction approaches. While numerous large public
bioactivity data sources exist, research in the field could benefit from better
standardization of existing data resources. At present, different research
works that share similar goals are often difficult to compare properly because
of different choices of data sources and train/validation/test split
strategies. Additionally, many works are based on small data subsets, leading
to results and insights of possible limited validity. In this paper we propose
a way to standardize and represent efficiently a very large dataset curated
from multiple public sources, split the data into train, validation and test
sets based on different meaningful strategies, and provide a concrete
evaluation protocol to accomplish a benchmark. We analyze the proposed data
curation, prove its usefulness and validate the proposed benchmark through
experimental studies based on an existing neural network model.

通过从多个公共数据源中创建和有效展示大规模数据集、基于不同有意义的策略将数据划分为训练、验证和测试集，并提供具体评估方案进行基准测试，本文提出了标准化已有数据资源的方法，并通过对现有神经网络模型的实验研究证明了其实用性和可行性。