A large dataset curation and benchmark for drug target interaction
This work addresses the problem of inconsistent and incomparable research in computational drug discovery for researchers in the field, though it is incremental as it focuses on data curation rather than new methods.
The authors tackled the lack of standardization in drug target interaction (DTI) prediction by curating a large dataset from multiple public sources and establishing a benchmark with defined train/validation/test splits and an evaluation protocol, demonstrating its usefulness through experimental studies with an existing neural network model.
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.