MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction

Liu, Liwei; Zhang, Qi; Wei, Yuxiao; Zhang, Shengli; Liao, Bo

doi:10.1101/2023.09.19.558555

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…The output of these three modules is finally concatenated to estimate the final prediction score of DTA. DeepGS [ 27 ] in another graph-based approach, which consider both local chemical context and the molecular structure in DTA prediction task. With the help of some embedding techniques such as Smi2Vec and Prot2Vec, DeepGS encodes the amino acid sequences of proteins as well as the atoms of drug molecules to distributed representations.…”

Section: Related Work: Deep Learning For Drug–target Affinity Predictionmentioning

confidence: 99%

BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing

Yousefi

Yazdani-Jahromi

Tayebi

et al. 2023

Briefings in Bioinformatics

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While research into drug–target interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning (DL)-based framework, called BindingSite-AugmentedDTA, which improves drug–target affinity (DTA) predictions by reducing the search space of potential-binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein-binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of four widely used evaluation metrics, including concordance index, mean squared error, modified squared correlation coefficient ($r^2_m$) and the area under the precision curve. We also contribute to three benchmark drug–traget interaction datasets by including additional information on 3D structure of all proteins contained in those datasets, which include the two most commonly used datasets, namely Kiba and Davis, as well as the data from IDG-DREAM drug-kinase binding prediction challenge. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. The relatively high agreement between computationally predicted and experimentally observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.

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Section: Related Work: Deep Learning For Drug–target Affinity Predictionmentioning

confidence: 99%

BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing

Yousefi

Yazdani-Jahromi

Tayebi

et al. 2023

Briefings in Bioinformatics

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“…DeepGS encodes SMILES strings into a 100 × 100 matrix using Smi2Vec and then utilizes a 23 × 23 convolutional kernel for feature extraction. However, the use of a fixed convolutional kernel size results in cutting SMILES characters, which can damage their specific semantic information.…”

Section: Introductionmentioning

confidence: 99%

ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction

Han,

Kang,

Guo

2024

ACS Omega

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BASE: a web service for providing compound-protein binding affinity prediction datasets with reduced similarity bias

Son,

Lee,

Kim

et al. 2024

BMC Bioinformatics

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Background Deep learning-based drug-target affinity (DTA) prediction methods have shown impressive performance, despite a high number of training parameters relative to the available data. Previous studies have highlighted the presence of dataset bias by suggesting that models trained solely on protein or ligand structures may perform similarly to those trained on complex structures. However, these studies did not propose solutions and focused solely on analyzing complex structure-based models. Even when ligands are excluded, protein-only models trained on complex structures still incorporate some ligand information at the binding sites. Therefore, it is unclear whether binding affinity can be accurately predicted using only compound or protein features due to potential dataset bias. In this study, we expanded our analysis to comprehensive databases and investigated dataset bias through compound and protein feature-based methods using multilayer perceptron models. We assessed the impact of this bias on current prediction models and proposed the binding affinity similarity explorer (BASE) web service, which provides bias-reduced datasets. Results By analyzing eight binding affinity databases using multilayer perceptron models, we confirmed a bias where the compound-protein binding affinity can be accurately predicted using compound features alone. This bias arises because most compounds show consistent binding affinities due to high sequence or functional similarity among their target proteins. Our Uniform Manifold Approximation and Projection analysis based on compound fingerprints further revealed that low and high variation compounds do not exhibit significant structural differences. This suggests that the primary factor driving the consistent binding affinities is protein similarity rather than compound structure. We addressed this bias by creating datasets with progressively reduced protein similarity between the training and test sets, observing significant changes in model performance. We developed the BASE web service to allow researchers to download and utilize these datasets. Feature importance analysis revealed that previous models heavily relied on protein features. However, using bias-reduced datasets increased the importance of compound and interaction features, enabling a more balanced extraction of key features. Conclusions We propose the BASE web service, providing both the affinity prediction results of existing models and bias-reduced datasets. These resources contribute to the development of generalized and robust predictive models, enhancing the accuracy and reliability of DTA predictions in the drug discovery process. BASE is freely available online at https://synbi2024.kaist.ac.kr/base . Supplementary Information The online version contains supplementary material available at 10.1186/s12859-024-05968-3.

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MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction

Cited by 3 publications

References 45 publications

BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing

BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing

ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction

BASE: a web service for providing compound-protein binding affinity prediction datasets with reduced similarity bias

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