What question did this study set out to answer?

The research aims to improve the drug discovery process for breast cancer by utilizing AI frameworks for solubility prediction and lead selection.

April 22, 2026Open Access

Title: Learning, Simulating, and Inhibiting: A Multi-Scale AI Framework for Breast Cancer Drug Discovery

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MHM Aljohani Hashim

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Implication

Demonstrates accurate solubility predictions and binding leads for breast cancer using AI and molecular simulations, suggesting promising drug candidates.

Key Points

The research aims to improve the drug discovery process for breast cancer by utilizing AI frameworks for solubility prediction and lead selection.
Implemented machine learning models (RF, XGBoost, LightGBM, ANN) for solubility prediction with over 80% accuracy.
Conducted virtual screening against estrogen receptor alpha ligand binding domain Y537S with selected hits.
Performed molecular dynamics simulations and computed binding free energies using MMGBSA and MMPBSA.
Top hits showed favorable binding free energy: Hit-1 at -111.34 kcal/mol and Hit-3 at -112.1 kcal/mol.
ADMET predictions indicated potential for the selected compounds as promising leads.
The protein-ligand complexes demonstrated stability and favorable dynamics during molecular simulations.

Cite This Study

M Aljohani Hashim (2026) studied this question.

synapsesocial.com/papers/69e866c96e0dea528ddeb2e0 https://doi.org/10.5281/zenodo.19657593

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Implication

Demonstrates accurate solubility predictions and binding leads for breast cancer using AI and molecular simulations, suggesting promising drug candidates.

Title: Learning, Simulating, and Inhibiting: A Multi-Scale AI Framework for Breast Cancer Drug Discovery

Discussion

Member takes

Implication

Key Points

Cite This Study

Title: Learning, Simulating, and Inhibiting: A Multi-Scale AI Framework for Breast Cancer Drug Discovery

Discussion

Member takes

Implication

Key Points

Cite This Study

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