Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

Abstract

The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.

Virtual Screening of Indonesian Herbal Database as Murine Double Minute-2 (MDM2) Inhibitor

“…A high value of EF 1%-2% signifies a positive control compound that has the high binding energy value in the virtual screening, so it is in the upper section in the order of binding energy value. 30 Based on the docking result, the best data is on the 70 x 70 x 70 grid box. This result is seen from the emergence of the first positive control data on the first 56 ligands (EF 5%) so that EF values start to increase in EF 10% and EF 20%.…”

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

“…Pharmacophore-based VS protocol was developed in Maestro 9.0 software package (https://www.schrodinger.com/) and used to screen SPECS database (http://www.specs.net) for potential FGFR1 inhibitors (Zhou et al, 2015). Database was previously filtered to extract only compounds with drug-like properties that comply with the Lipinski's rule of 5.…”

Conserved Water Molecule-dependent Docking Strategy and Atom-Based 3D QSAR Studies to Design Heat Shock Protein 90 Inhibitors

“…Subsequently, the test set was engaged in verifying the quality of the models using leave-one-out (LOO) method. Sometimes internal cross-validation is not sufficient to measure the accuracy of a model in predicting the activities of new compounds [35] . Hence, external validation with the help of Phase tool of Schrodinger software was performed which utilizes a true test set (radicicol, geldanamycin, tanespimycin, retaspimycin, alvespimycin and purine analogue B11021) whose chemical structures and The intensive comparison of the docking scores calculated by retaining the 4 prominent conserved water molecules (902, 903, 981 and 1026) were carried out among Schrodinger Glide XP and SP methodologies, SYBYL Surflex dock, Surflex dock Screen, Surflex dock Geom and Surflex dock Geom X methods.…”