Response Surface Analysis as Alternative 3D-QSAR Tool: Human A3 Adenosine Receptor Antagonists as a Key Study

Abstract

3D-QSAR methodologies represent a useful, widespread tool in drug discovery and optimisation.Here we introduce an alternative QSAR tool for model generation: the non-linear Response Surface Analysis, that finds at present great application in Design of Experiment for optimizing drug production processes. Binding affinity estimation of human A 3 adenosine receptor antagonists is considered as key study.

Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

“…Furthermore, the introduction of the autocorrelation vector allows then for overcoming the MEP information inconvenience to be reliant on the spatial rotation and translation of the molecule. We have already demonstrated that the auto MEP vectors can be used as interesting molecular descriptors in different 3D-QSAR applications. − …”

“…In the present Article, we would like to demonstrate how a novel application of the multilabel classification approach by combining our well-performing autocorrelated molecular descriptors encoding for the molecular electrostatic potential ( auto MEP) vectors with support vector machine (SVM) analysis can represent a very powerful tool to simultaneously describe the hA 1 R, hA 2A R, hA 2B R, and hA 3 R potency profiles and identify the possible subtype selectivity for hAR antagonists. − SVM is widely applied as a supervised learning technique to solve both classification and regression problems. , In the last years, various classification results have been reported in several papers. − Very recently, we have developed an integrated SVM-SVR method by using the auto MEP molecular descriptors to discriminate A 2A R versus A 3 R antagonists and to predict the binding affinity to the corresponding receptor subtype . This work has clarified that the classification approach is a valuable tool to predict the receptor subtypes selectivity.…”

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

“…We have already demonstrated that the autoMEP vectors can be used as interesting molecular descriptors in different 3D-QSAR applications. [24][25][26][27][28] In this context, we have also reported that pyrazolotriazolo-pyrimidine is a versatile scaffold to cover a large spectrum of the adenosine receptor selectivity. In particular, pyrazolo-triazolo-pyrimidines bearing specific substitutions at the N 5 and N 8 positions have been described as highly potent and selective human A 3 R antagonists while the position N 7 shifts the selectivity profile to the human A 2A R subtype.…”

“…[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.…”

Support Vector Machine (SVM) as Alternative Tool to Assign Acute Aquatic Toxicity Warning Labels to Chemicals

“…[65][66][67][68][69] In fact, the topological and electrostatic complementarities are crucial aspects in the molecular recognition processes and MEP vectors have been investigated on the molecular surface as a particularly useful method for rationalizing the interactions between molecules and molecular recognition processes. [48][49][50] Furthermore, the autocorrelation function transforms the constitution of a molecule into a fixed length representation to overcome the dependence of MEP information on the spatial rotation and translation of the molecule.…”