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Reimagining computational macromolecular modeling: AI-driven approaches.

E Sila Ozdemir¹, Hyunbum Jang², Ruth Nussinov³

¹Independent Researcher, Seattle, WA 98109.

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|November 22, 2025

Summary

Computational methods, especially artificial intelligence (AI) and machine learning (ML), are revolutionizing macromolecule modeling for drug discovery. These AI/ML approaches enhance structure prediction, molecule design, and interaction analysis, accelerating therapeutic development.

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Area of Science:

Computational biology
Biophysics
Drug discovery

Background:

Macromolecules like proteins and antibodies are vital for therapeutics and diagnostics.
Understanding macromolecular structure is crucial but challenging due to complexity.
Computational methods offer efficient alternatives to experimental techniques.

Purpose of the Study:

To review state-of-the-art computational methods for macromolecule modeling.
To focus on artificial intelligence (AI) and machine learning (ML) approaches.
To discuss AI/ML applications in therapeutic development and drug discovery.

Main Methods:

Review of advanced AI/ML techniques in macromolecule modeling.
Analysis of computational strategies for structure prediction and interaction modeling.
Assessment of AI/ML in designing novel therapeutics and cheminformatics.

Main Results:

AI/ML approaches significantly advance macromolecule structure prediction and interaction modeling.
These methods are transforming drug discovery pipelines, from molecule design to binding affinity estimation.
Current AI/ML tools offer faster and more cost-effective solutions compared to traditional methods.

Conclusions:

AI/ML methods are powerful tools for overcoming macromolecular complexity in drug development.
Further research is needed to address challenges like data integration, interpretability, and model validation.
This review provides a comprehensive overview of computational strategies for innovative drug development.