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Generate ChEMBL Predictions

Generate predictions with ONNX

This workfow uses a multitask neural network built using PyTorch and saved to the ONNX format to generate predictions.

The network generates predictions for bioactivity against 560 targets. The results are visually presented in a bisorted heatmap.

The workflow also looks up whatever information is available in ChEMBL itself about the activity of the prediction compounds on the 560 targets and presents that in a second interactive view.

NOTE that in order to use this workflow you also need the contents of the Data folder, which contains the saved network and information about the targets that are being predicted.

Input Predictions Lookup what's known Display results Compare to known results Read moleculesto be predictedRead target idsNode 100Node 101 TensorFlowNetwork Executor RDKit Fingerprint Expand Bit Vector Display predictionsas heatmap Column Resorter Display predictionsand measurements Reformat withbisorting Retrieve ChEMBLdata when present Generate statisticsfor known compounds File Reader(Complex Format) File Reader(Complex Format) Joiner TensorFlowNetwork Reader Input Predictions Lookup what's known Display results Compare to known results Read moleculesto be predictedRead target idsNode 100Node 101TensorFlowNetwork Executor RDKit Fingerprint Expand Bit Vector Display predictionsas heatmap Column Resorter Display predictionsand measurements Reformat withbisorting Retrieve ChEMBLdata when present Generate statisticsfor known compounds File Reader(Complex Format) File Reader(Complex Format) Joiner TensorFlowNetwork Reader

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