The application of a 3D-QSAR (autoMEP/PLS) approach as an efficient pharmacodynamic-driven filtering method for small-sized virtual library: Application to a lead optimization of a human A3 adenosine receptor antagonist

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

“…Furthermore, spatial autocorrelation vectors offer the advantage of being independent of the 3D orientation of the molecules. On these bases, Moro and coworkers recently introduced the autocorrelation of molecular electrostatic surface properties combined with partial least square analysis (autoMEP/PLS) as an attractive alternative to CoMFA [36,[47][48][49]. The authors applied the novel strategy to the study of the activity of antagonists of the adenosine A 3 receptor.…”

Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A2A and A3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites

“…[19][20][21][22][23] Moving from these examples, we have implemented an integrated application of SVM-SVR approach, based on the use of our recently reported autocorrelated molecular descriptors encoding for the Molecular Electrostatic Potential (autoMEP), to simultaneously discriminate A 2A R versus A 3 R antagonists and to predict their binding affinity to the corresponding receptor subtype of a large dataset of known pyrazolo-triazolopyrimidine analogs. [24][25][26][27][28] To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine derivatives anticipating both A 2A R/A 3 R subtypes selectivity and receptor binding affinity profiles. The statistical quality of both training and validation models are very encouraging.…”

Exploring the QSAR of Pyrazolo[3,4‐b]Pyridine, Pyrazolo[3,4‐b]Pyridone and Pyrazolo[3,4‐b]Pyrimidine Derivatives as Antagonists for A1 Adenosine Receptor

“…Fiftythree compounds were taken in the calibration set (1 -2, 4 -7, 9 -13, 15, 18 -23, 25 -29, 31 -35, 37 -38, 40 -44, 46 -53, 55 -59, 61 -62, 65 -67) and fifteen compounds (3,8,14,16,17,24,30,36,39,45, 54, 60, 63, 64 and 68) were taken in the test set; four molecules of the test set are inactive, the remaining eleven have different levels of activity. Figure 2 represents the objects selected for the external test set (labelled with their index); Figure 2A shows the dispersion of these objects in the space of their structural variations (first two PCs of the autoscaled descriptors, representing 53.1% of total variance); the histogram of Figure 2B shows the same objects on the base of the value of their biological activity.…”

“…On the basis of the comparison of the Connolly surface of the most active compound of each series, we conclude that the antagonist should probably possess a suitable Y shape to properly interact with the A 1 receptor, as it is in the case of other GPCRs ligands [16]. These considerations are in agreement with our previous results [7] highlighting the importance of the interactions that the antagonist should perform, inside the AR receptor binding site, with three lipophilic pockets of different dimensions, which should be properly filled by the molecule (Figure 4) (3,8,14,16,17,24,30,36,39,45,54,60,63,64,68). In the last line the error correspondent to these external samples is reported.…”