Description:
Regression model with 2 QuBiLS-MIDAS descriptors used for the logarithmic values prediction of the minimum effective dose (Log(MED)) in µmol/cm2 to trigger Aedes aegypti repellency.

The training was performed with the SMOreg algorithm (with Pearson Universal Kernel (PUK)) in Weka 3.9.4 with 10-fold cross-validation. The 2 QuBiLS-MIDAS descriptors are namely:

MIC_SD_Tr_AB_nCi_3_M21(M13)_SS0_T_KA_psa-e-v_MID
GV[1]_K_TrB_AB_nCi_3_M22(M1)_SS7_A_LG3L[6-7]_LGL[6-7]_p-s_MID

Training set:
71 compounds extracted from 10.1371/journal.pone.0064547

Test set:
8 carboxamides proposed by Oliferenko et al. (10.1371/journal.pone.0064547) were used for external validation.

Performance:
For a 10-fold cross-validation, the statistical parameters (performance without applicability domain) are R = 0.812, MAE = 0.3895, RMSE = 0.486, RAE = 61.3891 %, and RRSE = 58.0682 %.

Classification Breakpoint:
The breakpoint is -0.82 µmol/cm2. Values lower than the breakpoint will elicit a repellent response in the Aedes aegypti mosquito. Values greater than or equal to is -0.82 µmol/cm2 represent certain actions occurring, however, these are not enough to activate a repellent reaction in the mosquito.

Reference:
Oliferenko et al. Promising Aedes aegypti Repellent Chemotypes Identified through Integrated QSAR, Virtual Screening, Synthesis, and Bioassay. PLOS ONE. 2013. 8(9): e64547. DOI: 10.1371/journal.pone.0064547