DeepDTA: deep drug–target binding affinity prediction

By Hakime Öztürk, Elif Ozkirimli Olmez, Arzucan Özgür. Bioinform., 2018

1,519 citations recorded by the source corpus.

Motivation The identification of novel drug‐target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein‐ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge‐bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep‐learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein‐ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). Results The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high‐level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state‐of‐the‐art method for DT binding affinity prediction. Availability and implementation https://github.com/hkmztrk/DeepDTA Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/da82e3010679795bda2b4b8552a71a3a4143b8aa

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

ATOM3D: Tasks On Molecules in Three Dimensions (2020, methodology): “As a 1D method for predicting ligand binding affinity, we choose DeepDTA [Öztürk et al., 2018]10, a 1DCNN based model that takes in pairs of ligand SMILES string and protein sequence as input.”
Do Large Scale Molecular Language Representations Capture Important Structural Information? (2021, methodology): “Learning on SMILES has been widely adopted for molecular property prediction Goh et al. (2017); Öztürk et al. (2018); Paul et al. (2018); Shin et al. (2019), as SMILES is generally more compact compared to most other methods of representing structure, including graphs.”
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “Recently, several deep learning-based models [31] utilize 1D convolutions and pooling to capture potential patterns from raw sequence information of both ligand and protein.”
Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model (2020, positive): “Neverthless, MT-DTI showed the best performance [12] when compared to a deep learning-based (DeepDTA) approach [21] and two traditional machine learning-based algorithms SimBoost [22], and KronRLS [23], with the KIBA [24] and DAVIS [25] data sets.”
Pre-training Molecular Graph Representation with 3D Geometry (2021, background): “4, 9, 16, 20 [60] Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli.”
Multi-Scale Representation Learning on Proteins (2022, methodology): “Previous methods typically only consider an individual subset within these scales, focusing on either sequence [Öztürk et al., 2018, Hou et al., 2018], three-dimensional structure [Hermosilla et al., 2021, Derevyanko et al., 2018] or surface [Gainza et al., 2020].”
Deep learning in drug discovery: an integrative review and future challenges (2022, methodology): “GCNs have been employed as 3D descriptors instead of SMILES strings in a lot of research, and it’s been discovered that these learned descriptors outperform standard descriptors in prediction tests and are easier to understand (Shin et al. 2019; Ozturk et al. 2018; Yu et al. 2019).”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “DL approaches such as DeepDTA (https:// github. com/ hkmzt rk/ DeepD TA) [314], and PADME [315] predict drug-target binding affinity, which depends on the 3-”
Artificial intelligence in drug discovery and development (2020, background): “By contrast, in similarity-based interaction, the similarity between drug and target is considered, and it is assumed that similar drugs will interact with the same targets [42].”
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery (2019, background): “…al. 2015; Xu et al. 2017), molecule generation and lead optimization (G´omez-Bombarelli et al. 2018; Jin, Barzilay, and Jaakkola 2018), retrosynthesis planning (Segler, Preuss, and Waller 2018; Schwaller et al. 2018), and compound-protein afﬁnity prediction ( ¨Ozt¨urk, ¨Ozg¨ur, and Ozkirimli 2018).”