A large dataset curation and benchmark for drug target interaction
Alex Golts Vadim Ratner Yoel Shoshan Moshe Raboh Sagi Polaczek Michal Ozery-Flato Daniel Shats Liam Hazan Sivan Ravid Efrat Hexter
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.