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Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.

Serena Vittorio¹, Filippo Lunghini², Pietro Morerio³

¹Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy.

Computational and Structural Biotechnology Journal

|June 3, 2024

Summary

This summary is machine-generated.

Deep learning (DL) methods show promise in improving molecular docking accuracy for drug discovery. These advanced algorithms can better identify the correct binding pose compared to traditional scoring functions.

Keywords:

Artificial intelligence Deep learning Molecular docking Pose selection Scoring functions

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Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Molecular docking is essential for predicting ligand-target interactions in drug discovery.
Current scoring functions often struggle to accurately identify the native binding pose.
Correct pose selection is critical for successful drug optimization.

Purpose of the Study:

To review recent advances in deep learning (DL) for molecular docking pose selection.
To compare the performance of DL methods against classical scoring functions.
To introduce two novel DL-based pose selectors.

Main Methods:

Literature review of DL-based pose selection approaches.
Comparative analysis of classical scoring functions and DL methods.
Development and presentation of two new DL-based pose selectors.

Main Results:

Deep learning algorithms demonstrate potential in enhancing pose selection accuracy.
DL methods offer a promising alternative to traditional scoring functions in molecular docking.
The presented novel DL-based pose selectors show competitive performance.

Conclusions:

Deep learning is advancing molecular docking by improving binding pose prediction.
DL-based pose selection is crucial for efficient drug discovery and optimization.
Further research into DL applications can overcome current limitations in docking accuracy.