Efficient Virtual Screening of BindingDB Ligands Against Key Breast Cancer Targets Using Machine Learning and Molecular Docking

Conceitos essenciais

Efficient identification of novel inhibitor molecules targeting EGFR, HER2, Estrogen, Progesterone, and NF-κB receptors for breast cancer treatment through machine learning-based virtual screening and molecular docking.

Resumo

This study aimed to develop efficient computational methods for virtual screening of chemical compounds against key breast cancer targets, including EGFR, HER2, Estrogen Receptor (ER), Progesterone Receptor (PR), and Nuclear Factor-kappa B (NF-κB).
The researchers first constructed binary and multi-class classification models using various machine learning techniques, including Genetic Algorithm (GA), Support Vector Machine (SVM), Random Forest (RF), and Quadratic Discriminant Analysis (QDA). The GA-SVM-SVM:GA-SVM-SVM pipeline emerged as the most effective, achieving an accuracy of 0.74 and an AUC of 0.94 for virtual screening.
This pipeline was then used to screen the BindingDB database, identifying 4454, 803, 438, and 378 new inhibitor molecules for EGFR+HER2, ER, NF-κB, and PR, respectively, with over 90% precision in both active/inactive and target prediction.
The binding energies of the selected molecules were evaluated through molecular docking, revealing a range of -15 to -5 kcal/mol, which is considered suitable for inhibiting the target receptors. Further prioritization of the new molecules was performed based on established medicinal chemistry rules and parameters, such as Lipinski, Pfizer, GSK, and golden triangle rules, as well as QED, SAscore, and MCE-18.
The study provides a comprehensive computational framework for efficient virtual screening and identification of novel breast cancer inhibitors, which can accelerate the early-stage drug discovery process.

Estatísticas

The binding energy range for the selected ligands against their respective targets was calculated to be between -15 and -5 kcal/mol.

Citações

"The GA-SVM-SVM:GA-SVM-SVM model was selected with an accuracy of 0.74, an F1-score of 0.73, and an AUC of 0.94 for virtual screening of ligands from the BindingDB database."
"This pipeline successfully identified 4454, 803, 438, and 378 ligands with over 90% precision in both active/inactive and target prediction for the classes of EGFR+HER2, ER, NF-κB, and PR, respectively, from the BindingDB database."

Principais Insights Extraídos De

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-kB receptors based on machine learning and molecular docking

by Parham Rezae... às arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00647.pdf

Perguntas Mais Profundas

What additional experimental validation steps could be taken to further assess the efficacy and selectivity of the identified novel inhibitor molecules

To further assess the efficacy and selectivity of the identified novel inhibitor molecules, additional experimental validation steps could be implemented. These steps could include:

Cell-Based Assays: Conducting cell-based assays to evaluate the inhibitory effects of the novel inhibitors on breast cancer cell lines. This would provide insights into the compounds' ability to inhibit cell proliferation, induce apoptosis, and impact cell signaling pathways.

Animal Studies: Performing in vivo studies using animal models of breast cancer to assess the efficacy of the inhibitors in a more complex biological system. This would help determine the compounds' pharmacokinetics, toxicity profiles, and overall therapeutic potential.

Target Engagement Studies: Utilizing techniques such as co-immunoprecipitation or proximity ligation assays to confirm the binding of the inhibitors to their intended targets (EGFR, HER2, ER, NF-κB, PR). This would validate the mechanism of action of the compounds.

Combination Studies: Investigating the synergistic effects of the novel inhibitors with standard chemotherapeutic agents or other targeted therapies commonly used in breast cancer treatment. This would help identify potential combination therapies for enhanced efficacy.

Biomarker Analysis: Assessing the impact of the inhibitors on specific biomarkers associated with breast cancer progression, such as Ki-67, HER2 expression, or NF-κB activity. This would provide valuable information on the compounds' effects on tumor growth and metastasis.

By incorporating these experimental validation steps, researchers can gain a more comprehensive understanding of the efficacy, selectivity, and potential clinical utility of the identified novel inhibitor molecules.

How could the virtual screening pipeline be extended to incorporate more comprehensive data sources and target receptors beyond the ones studied in this work

To extend the virtual screening pipeline and incorporate more comprehensive data sources and target receptors, the following strategies could be implemented:

Expanded Database Inclusion: Integrate additional chemical databases beyond BindingDB to access a broader range of compounds for screening. Databases like PubChem, ChEMBL, or DrugBank contain extensive collections of bioactive molecules that could enhance the diversity of compounds screened.

Incorporation of Structural Databases: Include structural databases such as the Protein Data Bank (PDB) to access 3D structures of target receptors for more accurate molecular docking studies. This would improve the precision of predicting ligand-receptor interactions.

Multi-Target Screening: Expand the pipeline to screen for inhibitors targeting a wider range of breast cancer-related receptors and pathways, including PI3K/Akt, mTOR, or VEGF. This would enable a more comprehensive analysis of potential therapeutic targets.

Machine Learning Optimization: Implement advanced machine learning algorithms to enhance the predictive accuracy of the screening pipeline. Techniques like deep learning or ensemble learning could improve the identification of novel inhibitors with specific target affinities.

Integration of Pharmacophore Modeling: Incorporate pharmacophore modeling to identify key structural features essential for ligand-target interactions. This would aid in the design of more potent and selective inhibitors for diverse breast cancer targets.

By expanding the virtual screening pipeline to include a wider array of data sources and target receptors, researchers can uncover novel inhibitors with enhanced therapeutic potential for breast cancer treatment.

What potential synergistic effects could be explored by combining the identified inhibitors targeting different breast cancer pathways, and how could this be investigated computationally

Exploring potential synergistic effects by combining inhibitors targeting different breast cancer pathways could lead to enhanced therapeutic outcomes. Computational investigations could be conducted to analyze these synergies:

Network Pharmacology Analysis: Utilize network pharmacology approaches to map out the interactions between the identified inhibitors and their respective targets. This analysis could reveal potential synergistic effects by targeting multiple pathways simultaneously.

Systems Biology Modeling: Develop systems biology models to simulate the impact of combining inhibitors on complex biological networks. This would provide insights into how the inhibitors interact at a systems level and their collective effects on breast cancer cell behavior.

Drug Combination Prediction: Employ computational algorithms to predict the most effective combinations of inhibitors based on their individual mechanisms of action and target profiles. This could help identify synergistic drug pairs with complementary effects.

Quantitative Structure-Activity Relationship (QSAR) Analysis: Perform QSAR studies to assess the interactions between the inhibitors and their targets when used in combination. This would aid in predicting the potency and selectivity of the combined therapies.

Molecular Dynamics Simulations: Conduct molecular dynamics simulations to study the binding modes of the inhibitors in complex with their targets when administered together. This would provide insights into the stability and efficacy of the combination therapies.

By investigating these potential synergistic effects computationally, researchers can identify promising combinations of inhibitors for further experimental validation and potential clinical translation in breast cancer treatment.