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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development

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Putting Chemical Knowledge to Work in Machine Learning for Reactivity.

¹ETH Zürich. kjell.jorner@chem.ethz.ch.

|December 4, 2023

Summary

Chemistry-informed machine learning enhances deep neural networks for small datasets. Integrating chemical knowledge improves predictions in drug design and materials science.

Area of Science:

Computational chemistry
Machine learning
Cheminformatics

Background:

Machine learning (ML) is widely applied in chemistry, but deep neural networks (DNNs) require large datasets.
Traditional ML methods in chemistry often rely on descriptors derived from quantum-chemical properties.
Small datasets are common in many chemical research areas, limiting DNN performance.

Purpose of the Study:

To explore methods for improving ML model performance in low-data regimes within chemistry.
To investigate the integration of chemical knowledge into deep learning architectures.
To enhance the data efficiency and predictive power of ML models for chemical reactivity.

Main Methods:

Augmenting deep learning models with descriptors based on computed quantum-chemical properties.

Keywords:

Digital chemistry Machine learning Reactivity

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Utilizing differentiable programming to merge neural networks with physical and chemical mathematical models.

Developing chemistry-informed machine learning (CIML) approaches.

Main Results:

CIML methods demonstrate improved performance, especially in low-data scenarios.
Integrating chemical knowledge enhances model data efficiency.
Models trained with CIML show better generalization to unseen molecules.

Conclusions:

Chemistry-informed machine learning offers a promising avenue for accelerating chemical research.
These methods are crucial for applications like drug design, materials discovery, and catalysis.
The integration of domain knowledge into ML is key for tackling data scarcity in chemistry.