J Chem Inf Model - Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions.

Tópicos

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Resumo

We report a novel method called ADAN (Applicability Domain ANalysis) for assessing the reliability of drug property predictions obtained by in silico methods. The assessment provided by ADAN is based on the comparison of the query compound with the training set, using six diverse similarity criteria. For every criterion, the query compound is considered out of range when the similarity value obtained is larger than the 95th percentile of the values obtained for the training set. The final outcome is a number in the range of 0-6 that expresses the number of unmet similarity criteria and allows classifying the query compound within seven reliability categories. Such categories can be further exploited to assign simpler reliability classes using a traffic light schema, to assign approximate confidence intervals or to mark the predictions as unreliable. The entire methodology has been validated simulating realistic conditions, where query compounds are structurally diverse from those in the training set. The validation exercise involved the construction of more than 1000 models. These models were built using a combination of training set, molecular descriptors, and modeling methods representative of the real predictive tasks performed in the eTOX project (a project whose objective is to predict in vivo toxicological end points in drug development). Validation results confirm the robustness of the proposed assessment methodology, which compares favorably with other classical methods based solely on the structural similarity of the compounds. ADAN characteristics make the method well-suited for estimate the quality of drug predictions obtained in extremely unfavorable conditions, like the prediction of drug toxicity end points.

Resumo Limpo

report novel method call adan applic domain analysi assess reliabl drug properti predict obtain silico method assess provid adan base comparison queri compound train set use six divers similar criteria everi criterion queri compound consid rang similar valu obtain larger th percentil valu obtain train set final outcom number rang express number unmet similar criteria allow classifi queri compound within seven reliabl categori categori can exploit assign simpler reliabl class use traffic light schema assign approxim confid interv mark predict unreli entir methodolog valid simul realist condit queri compound structur divers train set valid exercis involv construct model model built use combin train set molecular descriptor model method repres real predict task perform etox project project whose object predict vivo toxicolog end point drug develop valid result confirm robust propos assess methodolog compar favor classic method base sole structur similar compound adan characterist make method wellsuit estim qualiti drug predict obtain extrem unfavor condit like predict drug toxic end point

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