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Perspectives Towards AI and ML.

Guoquan Wu¹, Zhe Wu²

¹Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore. wu.guoquan@u.nus.edu.

Advances in Biochemical Engineering/Biotechnology

|April 16, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) offer new predictive capabilities for biomolecular crystallization. This research demonstrates how ML can effectively address practical challenges in these complex processes.

Keywords:

Artificial intelligence Crystallization Machine learning Model predictive control Process modeling

Related Experiment Videos

Area of Science:

Biochemistry
Computational Biology
Crystallography

Background:

Biomolecular crystallization is crucial for structure determination but faces practical challenges.
Artificial intelligence (AI) and machine learning (ML) have broad applications but are underexplored in biomolecular crystallization.
Existing research lacks predictive models for biomolecular crystallization using ML.

Purpose of the Study:

To demonstrate the effective application of machine learning (ML) approaches to biomolecular crystallization processes.
To highlight the potential of AI and ML in improving the understanding and predictive capabilities of crystallization.
To introduce ML methods applicable to crystallization modeling.

Main Methods:

Review of AI and ML applications in crystallization.
Introduction to various ML methods.
Presentation of three ML approaches for modeling crystallization processes.

Main Results:

AI and ML offer novel insights and predictive power for biomolecular crystallization.
Proposed ML algorithms show effectiveness in modeling crystallization processes.
Sensor data transformation enables similar ML application for biomolecules and small molecules.

Conclusions:

ML techniques can be effectively applied to address practical challenges in biomolecular crystallization.
AI and ML provide powerful tools for advancing our understanding of crystallization.
This work lays the foundation for future predictive modeling in the field.