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4D-QSAR Analyses for EGFR Inhibitors Based on CDDA-OPS-GA Method
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Epidermal growth factor receptor (EGFR) is a preferred target for treating cancer. Compared to 3D-QSAR, 4D-QSAR has the feature of conformational flexibility and free alignment for individual ligands. In present studies, the 4D-QSAR of 131 analogs of 4-anilino quinazoline for EGFR inhibitors was built. The GROMACS package was employed to yield the conformational ensemble profile. The field descriptors of Coulomb and Lennard−Jones potentials were calculated by LQTA-QSAR (Laboratory of Theoretical and Applied Chemometrics, QSAR). The filter descriptors and variable selection is very important, which was performed by means of comparative distribution detection algorithm (CDDA), ordered predictors selection (OPS) and genetic algorithm (GA) method. Best 4D-QSAR model yielded satisfactory statistics (R2 = 0.71), good performance in internal (Q2 LOO = 0.60) and external prediction (R2 pred = 0.69, k = 0.97, k′ = 1.01). The 4D-QSAR was shown to be robust (Q2 LMO = 0.59) and was not built by chance (R2 YS = 0.17, Q2 YS = −0.25). The model has a good potential for rational design new EGFR inhibitors
Title: 4D-QSAR Analyses for EGFR Inhibitors Based on CDDA-OPS-GA Method
Description:
Epidermal growth factor receptor (EGFR) is a preferred target for treating cancer.
Compared to 3D-QSAR, 4D-QSAR has the feature of conformational flexibility and free alignment for individual ligands.
In present studies, the 4D-QSAR of 131 analogs of 4-anilino quinazoline for EGFR inhibitors was built.
The GROMACS package was employed to yield the conformational ensemble profile.
The field descriptors of Coulomb and Lennard−Jones potentials were calculated by LQTA-QSAR (Laboratory of Theoretical and Applied Chemometrics, QSAR).
The filter descriptors and variable selection is very important, which was performed by means of comparative distribution detection algorithm (CDDA), ordered predictors selection (OPS) and genetic algorithm (GA) method.
Best 4D-QSAR model yielded satisfactory statistics (R2 = 0.
71), good performance in internal (Q2 LOO = 0.
60) and external prediction (R2 pred = 0.
69, k = 0.
97, k′ = 1.
01).
The 4D-QSAR was shown to be robust (Q2 LMO = 0.
59) and was not built by chance (R2 YS = 0.
17, Q2 YS = −0.
25).
The model has a good potential for rational design new EGFR inhibitors.
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