Fluorine pKa

For predicting are used a lot of generated molecule features using the most important and not correlated features from mordred (partial positive surface area, partial negative surface area, number of fluorine or carbon atoms etc.), rdkit (number of cycles, chiral centers etc) and custom calculation (dihedral angle, molecular weight and volume, dipole moment, sasa, functional group freedom etc). All this molecule features used for predicting pKa values using ML models.

Options

Execution time

How to execute feature generation.

Available options:

  • Fast/Inaccurate: Generates less conformers for feature generation. Results in faster prediction, but with less accurate results.
  • Slow/Accurate: Generates 3^(number of double bonds) conformers for feature generation. Results in slower prediction, but better accurate results.
  • Experimental - Fast/Accurate: Graph neural network with features that are generated very quickly is used for prediction. The results are even better than other modes.
SMILES column:

Select the column containing SMILES.

Input Ports

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Table with SMILES in 'string' of 'SMI' format

Output Ports

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Table with predicted pKa values

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Links

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Installation

To use this node in KNIME, install the extension Chem AI from the below update site following our NodePit Product and Node Installation Guide:

v5.2

A zipped version of the software site can be downloaded here.

Plugin provider: blackthorn.ai
Plugin version: 0.1.0.202407102147
On NodePit since: 2024-06-18
Last update: 2024-10-09
KNIME versions: From v5.2 to v5.2

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