Computational and Mathematical Methods in Medicine Table 4: Test dataset Goodness of ﬁt comparisons. Clinical MRTD values from 8 ARVs were using as and external test dataset the validate model predictability. Model performance is characterized here in terms of root means squared error (RMSE), Kendall’s correlation coeﬃcient (tau), and Type II error probability (P-value). SE 12.3474 0.3996 106.0900 382.9849 252.1172 16.4025 601.2304 126.7876 RMSE = 13.67 5 Drug Abacavir Emtricitabine Raltegravir Nevirapine Efavirenz Fosamprenavir Atazanavir Lopiravir Mean Kendall’s tau P-value MRTD 10.00 4.00 30.07 3.00 10.10 46.70 5.35 53.33 20.32 MLR −10.6738 −23.9239 0.0628 −54.1862 −10.2863 44.0515 11.4360 42.6361 −0.1105 0.714 0.019 Execute. Execute. COMPUTE Tiberius MRTD = ((((Var1 + Var2 + Var3)/2.0) + 0.5) ∗ 199.9625) + 0.0375. 3.2. Model Validation and Statistical Comparisons. Each of the models was then validated using external TEST MRTD dataset and a cross-validation procedure. Model “goodness of ﬁt” and predictability are summarized in Table 4. RMSE of the 6-2-1 neural network (RMSE = 13.67) was substantially less the multiple linear regression model (RMSE = 27.27). Comparison of model predictivity was conﬁrmed with TNN having maximum squared error (SE = 601.23) compared to MLR (SE = 3270.26). Model Figure 3 correlation with the clinical MRTD values using Kendall’s correlation coeﬃcient was, however, greater for the MLR (tau = 0.714, P = 0.019) than the resultant 6-2-1 neural network model (tau = 0.643, P = 0.035). Bland Altman plots for methods comparison further illustrate the predictive value of the neural network (TNN) and multiple linear regression (MLR) models. The upper and lower limits of agreement for the MLR (58.3, −17.4) and TNN (29.8, −27.3) are illustrated in Figure 2. While it can be seen that all of the diﬀerences lie between these limits, MLR model limits of agreement were wider than the TNN model, which were more narrow and nearly symmetrical. 4. Discussion MRTD values and SMILES codes for antiretroviral drugs were collected from the FDA MRTD database which is a highly reliable source pharmacologic activity based on extensive clinical evidence (http://www.fda.gov/cder/). The ALOGPS+ program was used for the calculation of molecular mass (MW), solubility (ASol), and lipophilicity (AlogP) without alteration. The precision and robustness of these tools are well established in the pharmaceutical and modeling community and its predictive power concerning SE 427.4060 779.7441 900.4320 3270.2614 415.6012 7.0145 37.0393 114.3594 RMSE = 27.27 80 60 40 D TR 20 M d c e 0 t i d e r P−20 −40 −60 TNN 6.4861 3.3678 19.7700 −16.5700 −5.7782 50.7500 29.8700 64.5900 19.06 0.643 0.035 3 4 5.35 MLR TNN 10 10.1 30.07 46.7 53.33 Clinical MRTD Figure 2: Method comparisons graph. MRTD values predicted by multiple linear regression (hollow squares, ) nearly traced those values predicted by the 6-2-1 neural network (solid squares, ) as shown. Multiple linear regression estimates for MRTD were consis- tently lower that those predicted by the neural network model. the input parameters in question are published on the company website (http://www.vcclab.com/) and cited in the manuscript [28, 29, 33]. Independent validation of the parameters calculated with ALOGPS has been published [33, 34] and the estimates were found to be appropriately accurate for the use intended. The bioaccumulation input parameters, oxidation half life (oxidHL), log biotransfor- mation half life (logBioHL), and biodegradation probability (P[BD]) were calculated using the EPI Suite software which is publically available and has been validated in hundreds of modeling experiments for the estimated parameters. EPI Suite software is continuously updated and the predictive power of its latest version is always available at the website (http://www.epa.gov/oppt/sf/tools/methods.htm). None of