RT Journal Article
SR Electronic
T1 ABCnet : Self-Attention based Atom, Bond Message Passing Network for Predicting Drug-Target Interaction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.27.474154
DO 10.1101/2021.12.27.474154
A1 Segyu Lee
A1 Junil Bang
A1 Sungeun Hong
A1 Woojung Jang
YR 2021
UL http://biorxiv.org/content/early/2021/12/27/2021.12.27.474154.abstract
AB Drug-target interaction (DTI) is a methodology for predicting the binding affinity between a compound and a target protein, and a key technology in the derivation of candidate substances in drug discovery. As DTI experiments have progressed for a long time, a substantial volume of chemical, biomedical, and pharmaceutical data have accumulated. This accumulation of data has occurred contemporaneously with the advent of the field of big data, and data-based machine learning methods could significantly reduce the time and cost of drug development. In particular, the deep learning method shows potential when applied to the fields of vision and speech recognition, and studies to apply deep learning to various other fields have emerged. Research applying deep learning is underway in drug development, and among various deep learning models, a graph-based model that can effectively learn molecular structures has received more attention as the SOTA in experimental results were achieved. Our study focused on molecular structure information among graph-based models in message passing neural networks. In this paper, we propose a self-attention-based bond and atom message passing neural network which predicts DTI by extracting molecular features through a graph model using an attention mechanism. Model validation experiments were performed after defining binding affinity as a regression and classification problem: binary classification to predict the presence or absence of binding to the drug-target, and regression to predict binding affinity to the drug-target. Classification was performed with BindingDB, and regression was performed with the DAVIS dataset. In the classification problem, ABCnet showed higher performance than MPNN, as it does in the existing study, and in regression, the potential of ABCnet was checked compared to that of SOTA. According to experiments, for Binary classification ABCnet have an average performance improvement of 1% for higher performance on DTI task than other MPNN and for regresssion ABCnet have CI with an average 0.01 to 0.02 performance degradation compared to SOTA. https://www.overleaf.com/project/618a05533676801d8f68ccf6Competing Interest StatementAcknowledgement Source of financial support: This work was supported by the Technology development Program(S2837441) funded by the Ministry of SMEs and Startups(MSS, Korea).