Several thousand biological activities are predicted simultaneously, including: mechanisms of action, pharmacological effects, toxic and side effects, interaction with antitargets, interaction with drug metabolizing enzymes (inhibition, induction, interaction as a substrate), interaction with transporter protein (inhibition, stimulation, interaction as a substrate), changing gene expression of individual genes (increase, decrease). Due to such extensive analysis, the general pharmacological potential of molecules under study is disclosed.
The user may select a particular pharmacotherapeutic field or a certain set of analyzed activities relevant to the purpose of the study. Such selection facilitates the analysis of the output information and reduce the computation time, which is particularly important in case of large chemical libraries.
Algorithm of prediction is statistically robust with respect to the incompleteness of the data in the training set. Since no one biologically active compound is tested against all known biological activities, it is necessary to obtain the best possible estimations even using the imperfect information.
The prediction is based on the structural formula of the compound that allows one to apply it to the virtual structures.
Evaluation of the pharmaceutical substances’ action on many pharmacological targets to identify the hidden pharmacological potential of the launched pharmaceuticals, provides the ideas regarding possible drug repurposing.
Prediction of the potential averse/toxic effects of drug-like compounds leads to their filtering out at the early stages of research.
Re-training of the program using the customer’s training set lead to unique proprietary SAR bases creation, which can be used in house for further predictions.
High rate of computation allows efficient multitarget virtual screening of large chemical libraries containing many millions structural formulae of drug-like compounds.
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