There is a huge lack of experimental data on ecotoxicity of pharmaceuticals, while existing resources are insufficient to gather these data against all possible environmental endpoints. Computational tools such as quantitative structure-toxicity relationship (QSTR) can help us to a great extent to overcome this problem through filling of data gaps. In the current study, QSTR models have been developed for toxicity of 260 diverse pharmaceuticals on three different trophic level species namely algae, daphnia and fish, using partial least squares (PLS) regression approach with 2D descriptors selected through a genetic algorithm approach in order to study underlying chemical features responsible for the observed acute toxicity. The final obtained statistically reliable QSTR models were extensively validated following the OECD guidelines. Interspecies quantitative structure-toxicity-toxicity (QSTTR) models were also developed using genetic algorithm followed by multiple linear regression (GA-MLR) approach to check for the pattern of responses observed as we move across the hierarchy of genetics in different taxonomical class. The obtained interspecies models were finally utilized to fill the data gaps for 260 pharmaceuticals, where experimental data were missing for at least one of the endpoints. Finally, a prioritized list for 7106 existing drug like substances was prepared by predicting their acute toxicity using developed QSTR models.