FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4319825745> ?p ?o ?g. }

• W4319825745 endingPage "19" @default.
• W4319825745 startingPage "1" @default.
• W4319825745 abstract "This research manuscript aims to find the most effective epidermal growth factor receptor (EGFR) inhibitors from millions of in house compounds through Machine Learning (ML) techniques. ML-based structure activity relationship (SAR) models were validated to predict biological activity of untested novel molecules. Six ML algorithms, including k nearest neighbour (KNN), decision tree (DT), Logistic Regression, support vector machine (SVM), multilinear regression (MLR), and random forest (RF), were used to build for activity prediction. Among these, RF classifier (accuracy for train and test set is 90% and 81%) and RF regressor (R2 and MSE for trainset is 0.83 and 0.29 and for test set, 0.69 and 0.46) showed good predictive performance. Also, the six most essential features that affect the biological activity parameter and highly contribute to model development were successfully selected by the variable importance technique. RF regression model was used to predict the biological activity expressed as pIC50 of nearly ten million molecules while RF classification model classifies those molecules into active, moderately active, and least active according to their predicted pIC50. Based on two models, thousand molecules from million molecules with higher predicted pIC50 values and classified as active were selected for molecular docking. Based on the docking scores, predicted pIC50, and binding interactions with MET769 residue, compounds, i.e., Zinc257233137, Zinc257232249, and Zinc101379788, were identified as potential EGFR inhibitors with predicted pIC50 7.72, 7.85, and 7.70. Dynamics studies were also performed on Zinc257233137 to illustrate that it has good binding free energy and stable hydrogen bonding interactions with EGFR. These molecules can be used for further research and proved to be the novel drugs for EGFR in cancer treatment.Communicated by Ramaswamy H. Sarma" @default.
• W4319825745 created "2023-02-11" @default.
• W4319825745 creator A5005551553 @default.
• W4319825745 creator A5017408430 @default.
• W4319825745 creator A5046656649 @default.
• W4319825745 creator A5048201075 @default.
• W4319825745 creator A5051025778 @default.
• W4319825745 creator A5054894084 @default.
• W4319825745 date "2023-02-10" @default.
• W4319825745 modified "2023-10-03" @default.
• W4319825745 title "Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR" @default.
• W4319825745 cites W1031578623 @default.
• W4319825745 cites W147281455 @default.
• W4319825745 cites W1563088657 @default.
• W4319825745 cites W1581492599 @default.
• W4319825745 cites W1966078827 @default.
• W4319825745 cites W1985372952 @default.
• W4319825745 cites W1985588649 @default.
• W4319825745 cites W1992258445 @default.
• W4319825745 cites W1993617475 @default.
• W4319825745 cites W1995816895 @default.
• W4319825745 cites W2000480518 @default.
• W4319825745 cites W2004691988 @default.
• W4319825745 cites W2007528734 @default.
• W4319825745 cites W2009927598 @default.
• W4319825745 cites W2024311391 @default.
• W4319825745 cites W2025816743 @default.
• W4319825745 cites W2079639517 @default.
• W4319825745 cites W2125373677 @default.
• W4319825745 cites W2130541969 @default.
• W4319825745 cites W2133051701 @default.
• W4319825745 cites W2154969724 @default.
• W4319825745 cites W2160544821 @default.
• W4319825745 cites W2163499160 @default.
• W4319825745 cites W2169678694 @default.
• W4319825745 cites W2216326501 @default.
• W4319825745 cites W2261059368 @default.
• W4319825745 cites W2342603028 @default.
• W4319825745 cites W2528045957 @default.
• W4319825745 cites W2732050253 @default.
• W4319825745 cites W2735767394 @default.
• W4319825745 cites W2754894271 @default.
• W4319825745 cites W2807410315 @default.
• W4319825745 cites W2884871339 @default.
• W4319825745 cites W2894639568 @default.
• W4319825745 cites W2908445743 @default.
• W4319825745 cites W2941030087 @default.
• W4319825745 cites W2959938226 @default.
• W4319825745 cites W3027270940 @default.
• W4319825745 cites W3084873012 @default.
• W4319825745 cites W3091865989 @default.
• W4319825745 cites W3094492244 @default.
• W4319825745 cites W3100593444 @default.
• W4319825745 cites W3116080267 @default.
• W4319825745 cites W3118299338 @default.
• W4319825745 cites W3120372417 @default.
• W4319825745 cites W3128646645 @default.
• W4319825745 cites W3131943919 @default.
• W4319825745 cites W3134968392 @default.
• W4319825745 cites W3153418506 @default.
• W4319825745 cites W3165143074 @default.
• W4319825745 cites W3175687624 @default.
• W4319825745 cites W3181803118 @default.
• W4319825745 cites W3186714300 @default.
• W4319825745 cites W3210935050 @default.
• W4319825745 cites W4200584507 @default.
• W4319825745 cites W4206938130 @default.
• W4319825745 cites W4213102919 @default.
• W4319825745 cites W4247641847 @default.
• W4319825745 cites W4289236186 @default.
• W4319825745 cites W4292486124 @default.
• W4319825745 doi "https://doi.org/10.1080/07391102.2023.2175263" @default.
• W4319825745 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36762704" @default.
• W4319825745 hasPublicationYear "2023" @default.
• W4319825745 type Work @default.
• W4319825745 citedByCount "0" @default.
• W4319825745 crossrefType "journal-article" @default.
• W4319825745 hasAuthorship W4319825745A5005551553 @default.
• W4319825745 hasAuthorship W4319825745A5017408430 @default.
• W4319825745 hasAuthorship W4319825745A5046656649 @default.
• W4319825745 hasAuthorship W4319825745A5048201075 @default.
• W4319825745 hasAuthorship W4319825745A5051025778 @default.
• W4319825745 hasAuthorship W4319825745A5054894084 @default.
• W4319825745 hasConcept C103697762 @default.
• W4319825745 hasConcept C105795698 @default.
• W4319825745 hasConcept C119857082 @default.
• W4319825745 hasConcept C12267149 @default.
• W4319825745 hasConcept C151956035 @default.
• W4319825745 hasConcept C154945302 @default.
• W4319825745 hasConcept C159110408 @default.
• W4319825745 hasConcept C164126121 @default.
• W4319825745 hasConcept C164923092 @default.
• W4319825745 hasConcept C169258074 @default.
• W4319825745 hasConcept C169903167 @default.
• W4319825745 hasConcept C185592680 @default.
• W4319825745 hasConcept C202444582 @default.
• W4319825745 hasConcept C33923547 @default.
• W4319825745 hasConcept C41008148 @default.

• next