Interpretable Drug Target Prediction Using Deep Neural Representation

By Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, S. Dey, Ping Zhang. 2018

254 citations recorded by the source corpus.

The identification of drug-target interactions (DTIs) is a key task in drug discovery, where drugs are chemical compounds and targets are proteins. Traditional DTI prediction methods are either time consuming (simulation-based methods) or heavily dependent on domain expertise (similarity-based and feature-based methods). In this work, we propose an end-to-end neural network model that predicts DTIs directly from low level representations. In addition to making predictions, our model provides biological interpretation using two-way attention mechanism. Instead of using simplified settings where a dataset is evaluated as a whole, we designed an evaluation dataset from BindingDB following more realistic settings where predictions of unobserved examples (proteins and drugs) have to be made. We experimentally compared our model with matrix factorization, similarity-based methods, and a previous deep learning approach. Overall, the results show that our model outperforms other approaches without requiring domain knowledge and feature engineering. In a case study, we illustrated the ability of our approach to provide biological insights to interpret the predictions.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “However, some methods (Karimi et al., 2019; Gao et al., 2018) have found that using the protein sequence and the drug SMILES string already provide competitive predictions for binding afﬁnity without the need for 3D structural data.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “In recently published works [ 116–122], methods such as deep belief neural networks [118, 119], convolutional neural networks [120, 122] and multiple layer perceptrons [121, 122] were used to establish DTI prediction programs.”
GraphQA: protein model quality assessment using graph convolutional networks (2020, background): “…prediction (Niepert et al., 2016; Gilmer et al., 2017; Li et al., 2018a), drug-drug interaction (Zitnik et al., 2018), drug-target interaction (Gao et al., 2018) molecular optimization (Jin et al., 2019), and generation of proteins, molecules and drugs (Ingraham et al., 2019a; You et al.,…”
MolTrans: Molecular Interaction Transformer for drug–target interaction prediction (2020, background): “Previous works (Gao et al., 2018; Lee et al., 2019; Öztürk et al., 2018, 2019; VC The Author(s) 2020.”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, unknown): “The protein feature encoder consists of three 1D-CNN layers with the number of ﬁlters [128, 128, 128] and kernel sizes [3, 6, 9].”
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks (2018, methodology): “The GCNN part is adopting a very recently-developed method (Gao et al., 2018) for compound-protein interactions.”
Utilizing graph machine learning within drug discovery and development (2021, methodology): “[214] use two encoders to derive embeddings for proteins and drugs, respectively.”
Graph convolutional networks for computational drug development and discovery (2019, methodology): “Previous studies [39,67]successfullyshowedinteractioncomplexesbetweendrug and protein entities (DTI and PPI) using either attention mechanisms or node-pair scores.”
Graph Neural Networks and Their Current Applications in Bioinformatics (2021, result): “There are also some studies similar to the general thinking of the above-mentioned method but diﬀer in data processing (Gao et al., 2018; Nguyen et al., 2021).”
Predicting drug–protein interaction using quasi-visual question answering system (2019, methodology): “BindingDB We further choose the BindingDB datasets [Gilson et al., 2015] as the real-world dataset to evaluate our model.”