Computational and Mathematical Methods in Medicine results here did not indicate AlogP or lipophilic character as having any inﬂuence on ARV dose. The aqueous solubility of a compound signiﬁcantly aﬀects its absorption and distribution characteristics. Typically, low solubility goes along with a poor absorption and, therefore, its systemic toxicity reduced, however, local irritability may develop and/or reduced elimination rates, both are characteristic of drugs with low aqueous solubility. For highly potent drugs, increasing solubility usually enhances the elimination rate and lowers systemic half life, [44]. This means that for ARVs with low aqueous solubility, some correlation with maximum therapeutic dose (as we has shown) is likely to be observed. Any chemical (even water) can produce toxic side eﬀects in the body if allowed to accumulate to suﬃciently large concentrations. While much of the eﬀort in bioaccumulation modeling [43, 45] has been initiated by scientists estimating the equilibrium distribution of chemicals between organ- isms and their environments (e.g., ﬁsh-water, plants-soil), eﬀective physicochemical property estimation routines (i.e., PERs) have resulted from these studies that may be applied to similar biodistribution problems in pharmaceutical research. For example, oxidation half life (OxidHL) is an estimate of the molecules ability to form stable hydroxyl radicals or to interact with such moiety under ambient conditions. Formation of hydroxyl-radicals is often associated with dose- limiting toxicities. Although in our studies, oxidation half life was not statistically signiﬁcant in the prediction of MRTD. Another bioaccumulation descriptor, biotransformation half life (logBioHL), is the (linearized) fraction of drug mass in the whole body that has been metabolized per day. Our estimates do not account for the formation of speciﬁc metabolites which may be toxic nor do they identify speciﬁc pathways in the process (i.e., phase I redox reactions or phase II type conjugation reactions). Consequently, logBioHL was not correlated with MRTD in the present study and was a weaker bioaccumulation property descriptor in comparison to oxidation half life or biodegradation probability P[BD]. The probability of biodegradation attempts to combine both oxidative and biotransformation susceptibility of the structure to give an estimate of overall persistence. P[BD] estimates are based upon molecular fragments [46–48] and in our investigation showed only moderate correlation with MRTD in both multiple linear regression and neural network models. Each of the models was evaluated for predictive accuracy and by statistical comparison. In terms of predictability, root mean squared errors were larger for the multiple linear regression than the neural network model. Some advantages of ANN over MLR models were illustrated in the current study. Artiﬁcial neural networks (ANNs) are biologically inspir- ed data-mining algorithms which work by detecting the patterns and relationships in data. We used the back prop- agation rule in which the neural network is trained to map a set of input data by iterative adjustment of the weights. A tangent sigmoid transfer function on the ﬁrst layer and two neurons with a nonlinear transfer function on the hidden layer were minimalistic structures used to reduce overﬁtting. Our training processes for TNN were allowed to run until no 7 change in RMSE was observed for 20 minutes, at which point the model was saved. This learning method is commonly used for neural network predictive models given dose- response type data. However, ANNs have several limitations, a major theoretical concern is the “black box” nature of the output, that is, conclusions are generated without mecha- nistic explanations. ANNs also are limited by the quality of their data and may need to be retrained periodically if its performance changes over time. This is not necessarily counterproductive, since it indicates robustness in the model which adapts to changes in the predictive criteria. Real-time monitoring of the training process is also important since overtraining can easily occur, especially when the datasets are small in size. This is may be one of the unique advantages of real-time visualization of the data-mining process allowing the investigator to make “intermediate evaluations” of model predictability and then continue training until the reliability and accuracy required of the predictions are met. In conclusion, antiretroviral drugs are a chemically di- verse class of compounds in terms of both physicochemical properties and bioaccumulation potential. However, com- mercial ARVs may be categorized for predictive modeling purposes into two groups based on aqueous solubility and lipophilic character, in which hydrophilic compounds may be administered at higher doses (MRTD) and prediction of their MRTD value may be possible using simple mul- tiple linear regression models. In contrast, the prediction of MRTD values for antiretrovirals with poorer aqueous solubility would be the most eﬀective when the neural network approach is used and when both physicochemical and bioaccumulation property descriptors are available for training. With regard to future studies, ANN represents a promising tool for predicting maximum therapeutic dose, especially for antiretroviral drugs with narrow therapeutic index in the treatment of AIDS. Abbreviations AlogP: Calculated octanaol/water partition coeﬃcient ASol: Aqueous solubility logBioHL: Biotransformation half life CV: Coeﬃcient of variation R2: Coeﬃcient of determination MLR: Multiple linear regression MRTD: Maximum recommended therapeutic dose MAX: maximum absolute error MW: Molecular weight MCC: Multiple correlation coeﬃcient OxidHL: Oxidation half life P[BD]: Biodegradation probability TNN: Tiberius Neural Network QSPR: Quantitative Structure Property Relationships RSD: Residual standard deviation RMSE: Root mean squared error SMILES: Simpliﬁed molecular input line entry system SE: Squared error SD: Standard deviation Rz: Zero order correlation coeﬃcient.