Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

Area of Science:

• Computational chemistry
• Cheminformatics
• Toxicology

Background:

• Estrogen receptors (ERα) are crucial targets in drug design and potential toxicity assessment.
• Predictive models like Quantitative Structure-Activity Relationship (QSAR) are vital for identifying ERα binding agents early.
• Existing QSAR models require enhancement for improved accuracy.

Purpose of the Study:

• To develop advanced predictive models for ERα binding agents.
• To integrate publicly available bioassay data with cheminformatics tools.
• To improve the accuracy of predicting chemical interactions with ERα.

Main Methods:

• Utilized a Tox21 Challenge dataset for initial ERα binding agent identification.
• Developed conventional QSAR models using chemical descriptors.
• Integrated public bioassay response profiles to create a biosimilarity score and nearest neighbor analysis.
• Developed a hybrid model combining QSAR and bioassay data.

Main Results:

• Conventional QSAR models showed moderate performance (cross-validation CCR=0.72, external prediction CCR=0.59).
• The hybrid model significantly improved prediction accuracy (cross-validation CCR=0.94, external prediction CCR=0.68).
• The hybrid model effectively addressed prediction errors caused by activity cliffs.

Conclusions:

• Publicly available chemical response profiles are valuable for enhancing predictive modeling.
• Combining chemical structure information with public big data resources improves the prediction of ERα binding agents.
• This approach offers a more robust method for evaluating potential drug candidates and chemical toxicity.