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Updated: Jun 9, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Published on: February 23, 2024

Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives.

Rodrigo Quiroga¹, Marcos Villarreal¹

¹Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Instituto de Investigaciones en Físico-Química de Córdoba, Universidad Nacional de Córdoba, INFIQC-CONICET, Cordoba, Argentina.

Current Medicinal Chemistry

|November 1, 2024

Summary

This summary is machine-generated.

Developing accurate scoring functions (SFs) is crucial for structure-based drug discovery. This review highlights best practices for training SFs, focusing on data quality and robust methodologies to enhance drug-target binding affinity predictions.

Keywords:

Molecular docking computational drug discovery deep learning.machine learning scoring function virtual screening

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Area of Science:

Computational chemistry
Medicinal chemistry
Pharmacology

Background:

Structure-based drug discovery relies on molecular docking and virtual screening.
Scoring Functions (SFs) are essential for predicting protein-ligand binding affinity.

Purpose of the Study:

To review challenges and best practices in training novel scoring functions.
To improve the accuracy and generalizability of SFs for predicting binding affinities.

Main Methods:

Review of existing literature on scoring function training.
Emphasis on data quality, bias mitigation, and overfitting prevention.
Exploration of hybrid empirical and machine-learning approaches.

Main Results:

Effective SF training requires high-quality, unbiased datasets.
Robust training strategies are needed for consistent and generalizable SFs.
Hybrid methods show potential for superior performance and versatility.

Conclusions:

Addressing biases and overfitting is critical for reliable SFs.
High-quality data is paramount for successful SF development.
Innovative hybrid scoring functions offer promising advancements in drug discovery.