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QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study
Thomas Ferrari1, Anna Lombardo2, Emilio Benfenati1
1IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Laboratory of Environmental Chemistry and Toxicology, Via La Masa 19, 20159 Milan, Italy.
The Science of the Total Environment
|May 27, 2018
Summary
QSARpy v1.0 is a novel QSAR modeling tool that uses molecular dissimilarity to predict properties. It accurately estimates the n-octanol/water partition coefficient (log Kow), a key hydrophobicity measure.
Area of Science:
- Computational chemistry
- Quantitative Structure-Activity Relationship (QSAR) modeling
Background:
- Quantitative Structure-Activity Relationship (QSAR) models are crucial for predicting chemical properties and reducing experimental testing.
- Existing automated QSAR development methods rely on atom presence, molecular similarity, or descriptors.
- A novel approach is needed to enhance the accuracy and efficiency of QSAR model development.
Purpose of the Study:
- To introduce QSARpy v1.0, a new QSAR modeling tool utilizing a dissimilarity-based approach.
- To develop and validate a QSAR model for predicting the n-octanol/water partition coefficient (log Kow) using QSARpy v1.0.
- To evaluate the performance of QSARpy v1.0 against existing QSAR modeling tools.
Main Methods:
- QSARpy v1.0 fragments training set molecules to identify 'modulators' – structural fragments associated with property differences.
- The tool predicts a target molecule's property by adjusting training set molecule properties based on shared structures and identified modulators.
- The method was applied to predict the n-octanol/water partition coefficient (log Kow), a measure of hydrophobicity.
Main Results:
- QSARpy v1.0 achieved accurate performance in predicting log Kow.
- The model demonstrated a Root Mean Square Error (RMSE) of 0.43 and an R-squared (R²) of 0.94 on the external test set.
- Performance favorably compared with other existing QSAR modeling programs.
Conclusions:
- QSARpy v1.0 offers a novel and effective dissimilarity-based approach for automated QSAR model development.
- The tool provides accurate predictions for log Kow, a critical parameter in environmental risk assessment.
- QSARpy v1.0 is freely available, promoting wider use in chemical property prediction and cost reduction.
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