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Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

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One class classification as a practical approach for accelerating π-π co-crystal discovery.

Aikaterini Vriza^1,2, Angelos B Canaj¹, Rebecca Vismara¹

¹Department of Chemistry and Materials Innovation Factory, University of Liverpool 51 Oxford Street Liverpool L7 3NY UK M.S.Dyer@liverpool.ac.uk.

Chemical Science

|June 24, 2021

Summary

This study introduces one-class classification to address imbalanced datasets in materials design, accelerating the discovery of novel polyaromatic hydrocarbon co-crystals by focusing synthetic efforts.

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Area of Science:

Computational Materials Science
Machine Learning in Chemistry
Crystallography

Background:

Machine learning models are transforming materials design, but data-driven approaches struggle with imbalanced datasets due to a lack of negative synthetic data.
Unsuccessful synthesis attempts often go unreported, creating biases in datasets used for predictive modeling in materials discovery.

Purpose of the Study:

To apply one-class classification methodology to overcome limitations posed by imbalanced datasets in materials design.
To identify an effective workflow for discovering emerging materials, specifically weakly bound polyaromatic hydrocarbon co-crystals, which represent a small, well-defined class.
To accelerate materials discovery by reducing the search space and guiding synthetic efforts.

Main Methods:

An extensive study of various one-class classification algorithms was conducted to identify the most suitable workflow.
A two-step approach was developed: training on known co-crystal data from the Cambridge Structural Database and scoring potential new pairs from the ZINC15 database.
Interpretability techniques were employed to understand molecular properties driving co-crystallization.

Main Results:

The one-class classification approach effectively handles imbalanced datasets in materials design.
The workflow successfully identified high-ranking potential co-crystal pairs from a vast chemical space.
Two novel co-crystals, pyrene-6H-benzo[c]chromen-6-one (1) and pyrene-9,10-dicyanoanthracene (2), were discovered and validated.

Conclusions:

One-class classification is a powerful tool for discovering materials within small, specific classes, even with limited negative data.
The developed methodology significantly accelerates materials discovery by efficiently screening potential candidates.
Understanding the molecular drivers of co-crystallization enhances the predictive power and applicability of machine learning in chemistry.