Table 2: The results of the multiple linear regression analysis for antiretroviral drugs versus MRTD. In the training, dataset only two of the six molecular descriptors showed a statistically signiﬁcant correlation with therapeutic dose, that is, P[BD] (P = 0.0349) and ASol (P = 0.0140). A multiple regression model equation coeﬃcients are listed with there standard errors, coeﬃcient of determination (R2 = 0.5970), multiple correlation coeﬃcient (MCC= 0.7727) and residual standard deviation (RSD = 33.89). Multiple linear regression independent variables Table 3: The results of the neural network analysis for antiretroviral drugs versus MRTDs. All molecular descriptors show weak correlation with MRTD except for ASol, OxidHL, and P[BD]. The 6-2-1 neural network model predicted training set MRTD values with high accuracy (R2 = 0.992, MAX= 13.64, P < 0.001). No multicollinearity between independent variables was observed in the training set. 6-2-1 neural network Model versus clinical MRTD Independent variables n = 23 R2 = 0.992 P < 0.001 Correlation coeﬃcients MAX= 13.64 RMSE = 5.53 Learning rate = 0.700 R2 Computational and Mathematical Methods in Medicine R2 = 0.5970 SE 6.6976 0.7697 8.0377 0.1015 2.8478 20.1746 RSD = 33.89 P-value 0.0873 0.0140 0.0654 0.0494 0.0643 0.0349 P = 0.013 MCC= 0.7727 Rz −0.221 0.476 −0.071 −0.116 0.448 0.427 n = 23 Coeﬃcient −34.3303 −12.1995 2.1239 15.9000 0.2159 5.6589 46.5133 F-ratio = 3.9507 −0.221 0.476 −0.071 −0.116 0.448 0.427 F-ratio = 1340.73 4 Constant AlogP ASol logBioHL MW OxidHL P[BD] ANOVA AlogP ASol logBioHL MW OxidHL P[BD] ANOVA Theresultsoftheneuralnetworkmodelforantiretroviral MRTDs is shown in Table 3. All molecular descriptors show weak correlation with MRTD except ASol, OxidHL, and P[BD]. The 6-2-1 neural network predicted training set MRTD values with high accuracy (R2 = 0.992, MAX = 13.64,P < 0.001).Nomulticollinearitybetweenindependent variables was observed in the training set. SPSS code for the 6-2-1 neural network may be executed as follows: COMPUTE Var1 = ((OxidHL ∗ (−0.543086558961242)) + 3.63976611815824) + ((Pr BD ∗ (−5.67413921537257)) + 2.81153598121711) + ((LogBioHL ∗ (0.40828801067156)) + 0.747514104338025) + ((ALogP ∗ (−0.848110602763694)) + 1.43330691867064) + ((Sol ∗ (−2.55399028293542E−02)) + 0.517313285798853) + ((MW ∗ (1.40343495376914E−02)) − 5.94322423917294) + 3.86733992096867. 0.049 0.226 0.005 0.013 0.201 0.182 P < 0.001 COMPUTE Var2 = ((OxidHL ∗ (8.23491975851945E−02)) - 0.551904322215974) + ((Pr BD ∗ (3.12529587401663)) - 1.54858410557524) + ((LogBioHL ∗ (0.431493610228662)) + 0.790000076287146) + ((ALogP ∗ (−0.04107624611641)) + 6.94188559367329E−02) + ((Sol ∗ (4.12997852313067E−02)) − 0.836531279838639) + ((MW ∗ (1.05078568602304E−03)) − 0.444983569959981) + 9.28741349960935E-02. COMPUTE Var3 = −0.535249116383622. Execute. COMPUTE Var1 = 0.108990429907616 ∗ Var1. COMPUTE Var2 = 1.44376333322051 ∗ (Exp(Var2) − Exp(-Var2)) / (Exp(Var2) + Exp(−Var2)). COMPUTE Var3 = Var3.