  Accurate prediction of drug-target interactions is critical for advancing
drug discovery. By reducing time and cost, machine learning and deep learning
can accelerate this discovery process. Our approach utilises the powerful
Barlow Twins architecture for feature-extraction while considering the
structure of the target protein, achieving state-of-the-art predictive
performance against multiple established benchmarks. The use of gradient
boosting machine as the underlying predictor ensures fast and efficient
predictions without the need for large computational resources. In addition, we
further benchmarked new baselines against existing methods. Together, these
innovations improve the efficiency and effectiveness of drug-target interaction
predictions, providing robust tools for accelerating drug development and
deepening the understanding of molecular interactions.
