Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Kamboj, Sakshi; Rajput, Abhishek; Rastogi, Amber; Thakur, Ashwani Kumar; Kumar, Manoj

doi:10.1016/j.csbj.2022.06.060

Cited by 11 publications

(5 citation statements)

References 76 publications

(82 reference statements)

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“…MDS-generated plots show that anti-dengue chemicals are well distributed in the 2D and 3D chemical space. Binning clustering utilizes internally developed C++ implementation, and MDS uses the “cmdscale” function implemented in R. These methods showed that these chemicals are very dissimilar [ 20 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Gautam,

Thakur,

Rajput

et al. 2023

Viruses

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Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the “Anti-Dengue” algorithm that predicts dengue virus inhibitors using a quantitative structure–activity relationship (QSAR) and MLTs. Using the “DrugRepV” database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model’s reliability and robustness were assessed using William’s plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. “Anti-Dengue” may be beneficial in accelerating antiviral drug development against the dengue virus.

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Section: Discussionmentioning

confidence: 99%

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Gautam,

Thakur,

Rajput

et al. 2023

Viruses

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“…We have used 5-fold and 10-fold cross validation method to evaluate the performance of all the module. For 10-fold cross validation, the data set is randomly divided into 10-equally sized sets [34]. From the 10 sets, one set is used for testing while the remaining nine sets are considered for training.…”

Section: Cross Validationmentioning

confidence: 99%

Ion-pumping microbial rhodopsin protein classification by machine learning approach

et al. 2023

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Background Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype. Results This study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server (https://bioinfo.imtech.res.in/servers/rhodopred) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers. Conclusion The overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers.

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“…On the other hand, regression tasks in QSAR deal with predicting continuous numerical values, often associated with the degree or potency of a biological response. For instance, researchers may use regression models to estimate the IC50 (half-maximal inhibitory concentration) or EC50 (half-maximal effective concentration) values of compounds, which provide quantitative measures of their efficacy [33]. Regression-based QSAR models leverage mathematical algorithms to establish a relationship between the structural attributes of molecules and their corresponding quantitative biological activities [34].…”

Section: Explanation Of Qsarmentioning

confidence: 99%

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy,

Maulana,

Idroes

et al. 2023

Infolitika J. Data Sci.

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This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.

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Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Cited by 11 publications

References 76 publications

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Ion-pumping microbial rhodopsin protein classification by machine learning approach

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

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