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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact

Milad Rayka¹, Mohammad Hossein Karimi-Jafari², Rohoullah Firouzi¹

¹Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran.

Molecular Informatics

|May 22, 2021

Summary

This study introduces ET-Score, a novel machine learning scoring function for molecular docking. ET-Score significantly improves drug discovery predictions with high accuracy and low computational cost.

Keywords:

PDBbind binding affinity prediction interatomic contacts machine learning scoring function

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Molecular docking simulations are crucial for drug discovery, with performance heavily reliant on scoring functions.
Machine learning (ML) has shown promise in enhancing the accuracy of these scoring functions.

Purpose of the Study:

To develop and evaluate a new ML-based scoring function, ET-Score, for improved molecular docking performance.
To assess ET-Score's predictive capabilities against existing ML and classical scoring functions.

Main Methods:

Developed ET-Score using distance-weighted interatomic contacts for protein-ligand complex featurization.
Employed the Extremely Randomized Trees algorithm for the training process.
Validated performance on the PDBbind 2016v core set.

Main Results:

ET-Score achieved a high Pearson's correlation of 0.827 and an RMSE of 1.332.
Demonstrated superior or comparable performance to established ML-based and classical scoring functions.
Exhibited significantly low computational cost.

Conclusions:

ET-Score represents a highly effective and computationally efficient ML-based scoring function for molecular docking.
The findings suggest ET-Score can advance drug discovery research through improved predictive accuracy.