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Cited by 4Computational Drug Discovery MethodsMachine Learning in BioinformaticsMachine Learning in Materials Science

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Deep learning-based classification model for GPR151 activator activity prediction

Open access
Xu, Huangchao, Zhang, Baohua, Liu, Qian·2023·BMC Bioinformatics
DOI 10.1186/s12859-023-05369-yPMC PMC10257328

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Abstract

Background: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming. Thus, the development of reliable activity classification model has become an essential way in the process of drug discovery, aiming to enhance the efficiency of virtual screening. Results: We propose a learning-based method based on feature extractor and deep neural network to predict the activity of GPR151 activators. We first introduce a new molecular feature extraction algorithm which utilizes the idea of bag-of-words model in natural language to densify the sparse fingerprint vector. Mol2vec method is also used to extract diverse features. Then, we construct three classical feature selection algorithms and three types of deep learning model to enhance the representational capacity of molecules and predict activity label by five different classifiers. We conduct experiments using our own dataset of GPR151 activators. The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. The svm classifier achieves the best accuracy of 0.92 and F1 score of 0.76 which indicates promising applications for our method in the field of activity prediction. Conclusion: The results suggest that the experimental design of this study is appropriate and well-conceived. The deep learning-based feature extraction algorithm established in this study outperforms traditional feature selection algorithm for activity prediction. The model developed can be effectively utilized in the pre-screening stage of drug virtual screening.

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Figures

Fig. 1 Classification of molecular activity prediction techniques based on artificial intelligence-assisted drug design
Fig. 1 Classification of molecular activity prediction techniques based on artificial intelligence-assisted drug designCC BY · PubMed Central
Fig. 2 The flowchart of GPR151 activators activity prediction pipeline. The pipeline contains two types of molecular features, deep neural networks and traditional feature selection algorithms for comparison, followed by different classifiers to predict activator activity labels
Fig. 2 The flowchart of GPR151 activators activity prediction pipeline. The pipeline contains two types of molecular features, deep neural networks and traditional feature selection algorithms for comparison, followed by different classifiers to predict activator activity labelsCC BY · PubMed Central
Fig. 3 The CNN network architecture used in this paper
Fig. 3 The CNN network architecture used in this paperCC BY · PubMed Central
Fig. 4 Iteration curves of accuracy and loss values for RDKFP-CNN (epoch=15), MorganFP-LSTM (epoch=50), Mol2vec-CNN (epoch=100) on train and test sets are from top to bottom. As iteration increases, the accuracy of train and test sets rises while loss value decreases in the fluctuation until the convergence
Fig. 4 Iteration curves of accuracy and loss values for RDKFP-CNN (epoch=15), MorganFP-LSTM (epoch=50), Mol2vec-CNN (epoch=100) on train and test sets are from top to bottom. As iteration increases, the accuracy of train and test sets rises while loss value decreases in the fluctuation until the convergenceCC BY · PubMed Central
Fig. 5 Confusion matrix of docking and predicted labels
Fig. 5 Confusion matrix of docking and predicted labelsCC BY · PubMed Central
Fig. 6 Visualization of rdk fingerprint feature extraction steps on best model CNN. From left to right are the features of raw rdk fingerprint, after encoding and padding, after embedding operation and extracted by CNN
Fig. 6 Visualization of rdk fingerprint feature extraction steps on best model CNN. From left to right are the features of raw rdk fingerprint, after encoding and padding, after embedding operation and extracted by CNNCC BY · PubMed Central
Fig. 7 Visualization of morgan fingerprint feature extraction steps on best model LSTM. From left to right are the features of raw morgan fingerprint, after encoding and padding, after embedding operation and extracted by LSTM
Fig. 7 Visualization of morgan fingerprint feature extraction steps on best model LSTM. From left to right are the features of raw morgan fingerprint, after encoding and padding, after embedding operation and extracted by LSTMCC BY · PubMed Central
Fig. 8 Visualization of Mol2vec feature extraction on LSTM and CNN model
Fig. 8 Visualization of Mol2vec feature extraction on LSTM and CNN modelCC BY · PubMed Central

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Metadata

Authors
Xu, H., Zhang, B., & Liu, Q.
Affiliations
Chinese Academy of Sciences (CN)
Corresponding author
Liu, Qian
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2023-06-09
Volume
24
Issue
1
Pages
245-245
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-023-05369-y
PMC
PMC10257328
OpenAlex
W4380085318
Cited by
4
References
3
Citations by year
2025: 4
FWCI
0.57
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Informatization Plan of Chinese Academy of Sciences (CAS-WX2021SF-0103-02)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
G protein-coupled receptors, feature selection, virtual screening, drug discovery, deep neural network, support vector machine
MeSH terms
Deep Learning, Algorithms, Drug Evaluation, Preclinical, Neural Networks, Computer, Drug Discovery

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CoChat. (2026). Citation check: Deep learning-based classification model for GPR151 activator activity prediction. https://app.cochat.ai/papers/10.1186/s12859-023-05369-y
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Xu, H., Zhang, B., & Liu, Q. (2023). Deep learning-based classification model for GPR151 activator activity prediction. BMC Bioinformatics. https://doi.org/10.1186/s12859-023-05369-y