Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions

QSAR model as a random event: A case of rat toxicity

“…16 There are criteria for validation of the QSPR/QSAR models which were suggested during last decade. [17][18][19][20][21] However, in fact, all suggested criteria of the predictability of QSPR/QSAR models are based on the analysis of the geometry of data points in the plot of experimental versus calculated values of an endpoint and typically are obtained for the experiments in the training and test sets. 17- 21 We deem that the split into the training and test sets has significant influence on the statistical quality of a model for both the training and the test sets.…”

Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method

“…Having data for a series of runs of the Monte Carlo optimization with different splits, one can extract promoters of pIC 50 increase (positive correlation weights for all runs) and promoters of pIC 50 decrease (negative correlation weights for all runs) [11]. Examples of promoters of increase for the pIC 50 are (i) EC1 ¼9,10,13 for carbon atoms; (ii) presence of three aromatic six-members rings without heteroatoms; (iii) presence of one seven-members ring with heteroatoms.…”

Monte Carlo–based quantitative structure–activity relationship models for toxicity of organic chemicals to Daphnia magna

“…The measure of influence of a feature F k for possible predictive potential of a model can be estimated via defect of F k , defect(F k ) calculated as the following [31,32]:…”