Predicting The Molecular Structure Relationship and The Biological Activity of DPP-4 Inhibitor Using Deep Neural Network with CatBoost Method as Feature Selection

Hamzah, Haris; Bustamam, Alhadi; Yanuar, Arry; Sarwinda, Devvi

doi:10.1109/icacsis51025.2020.9263204

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…CatBoost is a high-performance gradient-boosting algorithm designed for machine learning tasks. This method essentially involves constructing an ensemble predictor through gradient descent within a functional space [ 43 ]. One study used the feature selection recommended by the CatBoost method, combining the feature extraction methods of Extended-Connectivity Fingerprint (ECFP) with the deep neural network method to predict DPP-4 inhibitors [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

“…This method essentially involves constructing an ensemble predictor through gradient descent within a functional space [ 43 ]. One study used the feature selection recommended by the CatBoost method, combining the feature extraction methods of Extended-Connectivity Fingerprint (ECFP) with the deep neural network method to predict DPP-4 inhibitors [ 43 ]. In our study, the depth parameter was set to 6, the learning rate was set to 0.05, and the model was trained for 1,000 iterations to ensure convergence.…”

Section: Methodsmentioning

confidence: 99%

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Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Zhang,

Zhu,

Bao

et al. 2024

Research

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Dipeptidyl peptidase-IV (DPP-4) enzyme inhibitors are a promising category of diabetes medications. Bioactive peptides, particularly those derived from bovine milk proteins, play crucial roles in inhibiting the DPP-4 enzyme. This study describes a comprehensive strategy for DPP-4 inhibitory peptide discovery and validation that combines machine learning and virtual proteolysis techniques. Five machine learning models, including GBDT, XGBoost, LightGBM, CatBoost, and RF, were trained. Notably, LightGBM demonstrated superior performance with an AUC value of 0.92 ± 0.01. Subsequently, LightGBM was employed to forecast the DPP-4 inhibitory potential of peptides generated through virtual proteolysis of milk proteins. Through a series of in silico screening process and in vitro experiments, GPVRGPF and HPHPHL were found to exhibit good DPP-4 inhibitory activity. Molecular docking and molecular dynamics simulations further confirmed the inhibitory mechanisms of these peptides. Through retracing the virtual proteolysis steps, it was found that GPVRGPF can be obtained from β-casein through enzymatic hydrolysis by chymotrypsin, while HPHPHL can be obtained from κ-casein through enzymatic hydrolysis by stem bromelain or papain. In summary, the integration of machine learning and virtual proteolysis techniques can aid in the preliminary determination of key hydrolysis parameters and facilitate the efficient screening of bioactive peptides.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Zhang,

Zhu,

Bao

et al. 2024

Research

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“…Beyond its applications in regression and classification, CatBoost finds utility in diverse domains, including ranking, recommendation systems, forecasting, and notably, drug discovery. It has been employed as a feature selection method [31].…”

Section: Prediction Modelmentioning

confidence: 99%

An Evolved Transformer Model for ADME/Tox Prediction

Shao,

Huang

et al. 2024

Electronics

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Drug discovery aims to keep fueling new medicines to cure and palliate many ailments and some untreatable diseases that still afflict humanity. The ADME/Tox (absorption, distribution, metabolism, excretion/toxicity) properties of candidate drug molecules are key factors that determine the safety, uptake, elimination, metabolic behavior and effectiveness of drug research and development. The predictive technique of ADME/Tox drastically reduces the fraction of pharmaceutics-related failure in the early stages of drug development. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, artificial intelligence techniques such as Graphormer are showing increasing promise and usefulness to perform custom models for molecule modeling tasks. However, Graphormer and other transformer-based models do not consider the molecular fingerprint, as well as the physicochemicals that have been proved effective in traditional computational drug research. Here, we propose an enhanced model based on Graphormer which uses a tree model that fully integrates some known information and achieves better prediction and interpretability. More importantly, the model achieves new state-of-the-art results on ADME/Tox properties prediction benchmarks, surpassing several challenging models. Experimental results demonstrate an average SMAPE (Symmetric Mean Absolute Percentage Error) of 18.9 and a PCC (Pearson Correlation Coefficient) of 0.86 on ADME/Tox prediction test sets. These findings highlight the efficacy of our approach and its potential to enhance drug discovery processes. By leveraging the strengths of Graphormer and incorporating additional molecular descriptors, our model offers improved predictive capabilities, thus contributing to the advancement of ADME/Tox prediction in drug development. The integration of various information sources further enables better interpretability, aiding researchers in understanding the underlying factors influencing the predictions. Overall, our work demonstrates the potential of our enhanced model to expedite drug discovery, reduce costs, and enhance the success rate of our pharmaceutical development efforts.

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“…Catboost has been applied in various tasks, including molecular structure relationship and the biological activity prediction [77] and the identification of pyroptosis-related molecular subtypes of lung adenocarcinoma [78]. In this study, the parameters of Cat-Boost were set as default values.…”

Section: Catboostmentioning

confidence: 99%

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

Zhu

Wang

et al. 2022

BMC Bioinformatics

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Background Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. Results Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. Conclusions In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git.

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Predicting The Molecular Structure Relationship and The Biological Activity of DPP-4 Inhibitor Using Deep Neural Network with CatBoost Method as Feature Selection

Cited by 6 publications

References 15 publications

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

An Evolved Transformer Model for ADME/Tox Prediction

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

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