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A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy

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Published on: January 30, 2019

Filling the Gap in and Evaluation for Saturated Fluorine-Containing Derivatives With Machine Learning.

Oleksandr Gurbych^1,2, Petro Pavliuk^1,2, Dmytro Krasnienkov¹

¹Blackthorn AI Ltd., London, UK.

Journal of Computational Chemistry

|January 13, 2025

Summary

This study developed accurate machine learning models to predict lipophilicity (logP) and acidity/basicity (pKa) for fluorinated compounds. These models aid drug discovery by improving predictions for challenging chemical structures.

Keywords:

acidity basicity fluorine graph neural networks lipophilicity machine learning molecular design

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Application and Methodology of the Non-destructive 19F Time-domain NMR Technique to Measure the Content in Fluorine-containing Drug Products

Application and Methodology of the Non-destructive 19F Time-domain NMR Technique to Measure the Content in Fluorine-containing Drug Products

Published on: August 22, 2017

Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Lipophilicity (logP) and acidity/basicity (pKa) are critical physicochemical properties influencing drug behavior.
Accurate prediction of logP and pKa is essential for successful early-stage drug discovery.
Standard prediction methods struggle with saturated fluorine-containing compounds, limiting their utility.

Purpose of the Study:

To develop accurate predictive models for lipophilicity and acidity/basicity of saturated fluorine-containing compounds.
To overcome limitations of existing prediction methods for this specific chemical class.
To provide open-source tools for targeted molecular design.

Main Methods:

Compiled a unique dataset of fluorinated and non-fluorinated derivatives with experimental logP and pKa values.
Evaluated, trained, or fine-tuned over 40 machine learning models (linear, tree-based, neural networks).
Utilized substructure mask explanation (SME) to validate model findings and the role of fluorine.

Main Results:

Developed highly accurate machine learning models for predicting logP and pKa in saturated fluorine-containing compounds.
Confirmed the significant impact of fluorinated substituents on lipophilicity and acidity/basicity.
Demonstrated the consistency and reliability of the developed predictive models.

Conclusions:

The developed models offer an optimal approach for predicting lipophilicity and acidity/basicity in challenging fluorinated compounds.
Open-sourced resources (GitHub, pip, conda, KNIME) enable public access for targeted molecular design.
This work facilitates the development of novel fluorinated drug candidates.