Generation and validation of the first predictive pharmacophore model for cyclin-dependent kinase 9 inhibitors

Identification of xanthine oxidase inhibitors through hierarchical virtual screening

“…[17][18][19][20][21][22][23][24][25] The diversity sampling method described previously were utilized to rationally divide the dataset into training and test sets. 26 Accordingly, a training set of twenty-six compounds was generated, and the remaining sixty-three compounds were classied into a test set. Each molecule in the whole data set was then plotted as a discrete point in a threedimensional space by PCA ( Fig.…”

“…Randomization test was also performed to check whether Hypo 1 was derived from chance correlation, 26 and thirty random pharmacophore models were generated accordingly. As shown in Fig.…”

“…To guarantee sufficient chemical and biological diversity of the training set, a diversity sampling method was applied to select training sets rationally, 26 and the rest compounds in the data set were taken as test set. The distribution of training set compounds in the three-dimensional space was examined by principal component analysis (PCA) to ensure the training set was representative enough.…”

Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors

“…If any of the randomized pharmacophore hypotheses resulted with similar or better cost or correlation value than the original hypothesis, then the original hypothesis is considered to be generated by chance [21]. Finally, in order to determine the capability of the pharmacophore hypotheses to discriminate active compounds from other molecules in virtual screening [27], E value and other statistical parameters were calculated using a small database containing 168 known c-Met inhibitors and 5827 randomly sampled compounds. The randomly sampled set served as decoys, which were obtained from a collection offered by drugbank (subset of random FDA-approved small molecule drug structures without biological activities on cMet reported) [28].…”

SAR study on N2,N4-disubstituted pyrimidine-2,4-diamines as effective CDK2/CDK9 inhibitors and antiproliferative agents

“…In our previous efforts, a 3D-QSAR pharmacophore derived from a diverse set of known CDK9 inhibitors was constructed. 15 Guided by the pharmacophoric features revealed and structural requirements of the ATP sites of CDK2 and CDK9, a series of novel N 2 , N 4 -diphenylpyrimidine-2,4-diamines were designed and synthesized, and compound 1 ( Fig. 2 ) was identified as the most potent inhibitor against CDK2 and CDK9.…”