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{ "status" "taskId": "success0", // Array of predictions (for each input molecule) "predictions" : [ { "depictionID" : 1000618459, "moleculeID" : 7868"1002136505", // Array of predictions for a given molecule. Normally, contains only one prediction. // Can contain multiple predictions for multi-models "predictions" : [ { "unit": "-log(mmol/L)", // Name of the predicted class for classification models. Same as "value" : 1.2020000219345093for regression models. "predictedValueString": "2.7139999866485596", "accuracy" : 0.5011452764639881, "dm" : 0.28780001401901245, "value": "2.7139999866485596", // Prediction value (round it as you find necessary) "dm": "0.8611000180244446", // The "distance to model" used for the accuracy estimation "insideAD" : true, // is this molecule inside the "unit" : "Log unitmodel's applicability domain? "property": "log(IGC50-1)", // The predicted property "property" "accuracy": "BCF0.7065203028344436", // The prediction accuracy (RMSE) "realValue" : "0.0" }], "predictedValueString" "depictionID": "1.20200002193450931000651576" } ] } ], "modelDescriptionUrl" "metaserverTaskId": "http://ochem.eu/model/231203920", "taskId" : 0 "status": "success", "metaserverTaskId" : 0 "modelDescriptionUrl": "http://ochem.eu/model/3" } |
Way 2: Using task IDs
Since a prediction is not instantaneous and can take several seconds to minutes, the prediction is performed asynchronously, that is in two steps:
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