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PharmQSAR workflow

Input Data: - Training and test (SDF) - Reference molecule (SDF) - Activity Data (CSV) - Molecules to predict the activity (SDF)In Training and test file, test molecules will beplaced at the bottom of the SDF file.Activity Data is formed by two columns. Firstcolumn (ID) contains the name of the molecules.Second column (Activity) contains the activityvalue. Preparation of SDF files.Calculation of molecular fields for each of the datasources. Although PharQSAR can calculate 3Dconformations, it is recommended that 3D structuresproceed from X-Ray or docking calculations. Molecular alignment:Training / Test sets are aligned to reference molecule.In parallel, molecules to predict should be also alignedto the reference.The usage of a bioactive conformation for thereference is recommended for 3D-QSAR methods.See the impact of reference conformation in thispaper:https://link.springer.com/article/10.1007/s10822-024-00561-5 Model generation:Generation of predictive model. Many models can begenerated by PLS using different components. Test molecules are indicated here. This is why theyshould be located at the bottom of the Training andtest file. Field generation:Generates the fields that help you todevelop novel compounds. You canopen them with tools like Pymol orVMD. Property prediction:Predicts the property (i.e. activity fora new set of molecules that hasbeen parametrized and alignedrespect a reference molecule or arethe results of a docking campaign. Real application of PharmQSAR models:Screening and Biological Evaluation of Soluble Epoxide Hydrolase Inhibitors:Assessing the Role of Hydrophobicity in the Pharmacophore-Guided Search of NovelHitshttps://pubs.acs.org/doi/10.1021/acs.jcim.3c00301 Training & Test(T&T) preparationReferencepreparationNew moleculespreparationT&T / refalignmentNew molecules / refalignmentPredictive model(best Q2)3D coefficient mapsActivity prediction PharmQSAR inputs New Molecules Moleculespreparation Moleculespreparation Moleculespreparation Molecular Alignment Molecular Alignment Model Generation PharmQSAR reference(Optional) Fields Generator Predictor Input Data: - Training and test (SDF) - Reference molecule (SDF) - Activity Data (CSV) - Molecules to predict the activity (SDF)In Training and test file, test molecules will beplaced at the bottom of the SDF file.Activity Data is formed by two columns. Firstcolumn (ID) contains the name of the molecules.Second column (Activity) contains the activityvalue. Preparation of SDF files.Calculation of molecular fields for each of the datasources. Although PharQSAR can calculate 3Dconformations, it is recommended that 3D structuresproceed from X-Ray or docking calculations. Molecular alignment:Training / Test sets are aligned to reference molecule.In parallel, molecules to predict should be also alignedto the reference.The usage of a bioactive conformation for thereference is recommended for 3D-QSAR methods.See the impact of reference conformation in thispaper:https://link.springer.com/article/10.1007/s10822-024-00561-5 Model generation:Generation of predictive model. Many models can begenerated by PLS using different components. Test molecules are indicated here. This is why theyshould be located at the bottom of the Training andtest file. Field generation:Generates the fields that help you todevelop novel compounds. You canopen them with tools like Pymol orVMD. Property prediction:Predicts the property (i.e. activity fora new set of molecules that hasbeen parametrized and alignedrespect a reference molecule or arethe results of a docking campaign. Real application of PharmQSAR models:Screening and Biological Evaluation of Soluble Epoxide Hydrolase Inhibitors:Assessing the Role of Hydrophobicity in the Pharmacophore-Guided Search of NovelHitshttps://pubs.acs.org/doi/10.1021/acs.jcim.3c00301 Training & Test(T&T) preparationReferencepreparationNew moleculespreparationT&T / refalignmentNew molecules / refalignmentPredictive model(best Q2)3D coefficient mapsActivity prediction PharmQSAR inputs New Molecules Moleculespreparation Moleculespreparation Moleculespreparation Molecular Alignment Molecular Alignment Model Generation PharmQSAR reference(Optional) Fields Generator Predictor

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