Abstract: The development of in vitro cytotoxicity assays has been driven by the need to rapidly evaluation of potential toxicity of large numbers of compounds, to reduce animal experimentation, and to save time and material resources. The large number of experimental results reported by different groups worldwide has lead to the accumulation of huge amounts of ontology-like data in large public databases as in ChEMBL. Conversely, many drugs have been assayed only for some selected tests. In this context, High-throughput multi-target Quantitative Structure-Activity (High-throughput mt-QSAR) techniques may become an important tool to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and the software STATISTICA to seek a Linear Discriminant Analysis (LDA) function. This model correctly classifies 8,258 out of 9,000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 μg/mL and Cytotoxicity = 23.6 % for the anti-microbial / anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1,251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). Both experimental and theoretical results point to a low macrophage cytotoxicity of G1. The results obtained are very important because they complement the toxicological studies of this important drug. This work opens a new door for the "in silico" multiplexing screening of large libraries of compounds.