Attention-based approach to predict drug-target interactions across seven target superfamilies
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Motivation
Drug-target interactions (DTIs) hold pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary, and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets. Yet, most FDA-approved drugs bind with only a small fraction of disease targets.
Results
This study introduces an attention-based method to predict drug-target bioactivities for all human proteins across seven superfamilies. Nine different descriptor sets were meticulously examined to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P<0.001) for six out of seven superfamilies. The proposed method outperformed nine state-of-the-art deep learning and graph-based methods, and importantly, maintained relatively high performance for most target superfamilies when tested in independent sources of bioactivity data. We computationally validated 185,676 drug-target pairs from ChEMBL-V33, that were not available in model training, with a reasonable performance of Spearman correlation > 0.57 (P<0.001) for most superfamilies. This justifies the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing activities among 3,492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications.
Availability and implementation
The codes and training datasets to reproduce and extend the results are available at https://github.com/AronSchulman/MMAtt-DTA .
