QFPR/QFAR

Development of the Latest Tools for Building up Nano-QSAR Quantitative Features—Property/Activity Relationships (QFPRs/QFARs)... 

After introduction of the classical approaches to predict properties and activities (endpoints) of the typical chemical substances essential parts of this chapter are devoted to (i) description of optimal descriptors which are translators of eclectic data into endpoint prediction (ii) discussion of predictive X)tential of models based on the optimal descriptors and (iii) discussion of possible ways to improve the optimal descriptors as a tool to build up QFPR/QFAR which are analogs of the traditional QSPR/QSAR for nanomaterials. 

Formally, the quasi-SMILES can replace the traditional SMILES in the above mentioned optimization procedure. However, in this case the new description should be used and the models should be named quantitative features — property/activity relationships (QFPRs/QFARs), since these models will be based on features which can be distant from the molecular strucmre (e.g. size, concentration, time exposure, etc.) that is used in classic QSPR/QSAR methods. 

Fig. 12.8 The essence of difference between QSPR/QSAR and QFPR/QFAR...

Though details of computational investigation of properties and activities of nanomaterials are still being developed the quantitative features — property/activity relationships (QFPRs/QFARs) methods discussed in this chapter provide eflicient tool to predict various characteristics of nanospecies. They are developed following a set of mles (i) All examined eclectic systems of data are translated into quasi-SMILES with further prediction of the above mentioned endpoints by the same approach (ii) budding up these models using the quasi-SMILES can be carried out by the same algorithm (software) which is used in... 

---

See also in sourсe #XX -- [ , , ]

---