Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore...

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Drug Discovery

Background:

• Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is a mitotic kinase.
• Elevated TTK levels are observed in various cancers, making it a therapeutic target.
• TTK inhibition represents a promising anti-cancer strategy.

Purpose of the Study:

• To augment training data for Quantitative Structure-Activity Relationship (QSAR) modeling using multiple docked poses of TTK inhibitors.
• To identify critical descriptors for predicting anti-TTK bioactivity and generate pharmacophore models.
• To discover novel TTK inhibitors through in silico screening and in vitro validation.

Main Methods:

• Utilized Ligand-Receptor Contacts Fingerprints and docking scores as descriptor variables.
• Employed machine learning algorithms (Random Forests, XGBoost) coupled with genetic algorithms and SHAP.
• Generated and screened pharmacophore hypotheses against the NCI database.
• Performed in vitro evaluation of identified hits for anti-TTK bioactivity.

Main Results:

• Successfully augmented QSAR modeling data using multiple docked poses.
• Identified key descriptors influencing anti-TTK bioactivity.
• Deduced three pharmacophore models leading to the identification of 14 potential hits.
• One novel chemotype inhibitor demonstrated significant anti-TTK activity with an IC50 of 1.0 μM.

Conclusions:

• Data augmentation with multiple docked poses is a valid approach for building effective machine learning models.
• The developed pharmacophore models are effective for identifying potential TTK inhibitors.
• This study successfully identified a novel TTK inhibitor candidate with therapeutic potential.