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Updated: Jan 15, 2026
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Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
Published on: April 13, 2022
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Generative AI for computational chemistry: A roadmap to predicting emergent phenomena
Pratyush Tiwary1,2, Lukas Herron2,3, Richard John4
1Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742.
Summary
Generative artificial intelligence (AI) offers new computational chemistry tools for molecular sampling and simulations. Future AI models must integrate chemical principles for accurate prediction of novel phenomena.
Area of Science:
- Computational Chemistry
- Artificial Intelligence
Background:
- Generative AI is rapidly advancing, offering potential breakthroughs in computational chemistry.
- Current AI methods show promise in molecular structure sampling, force field development, and simulation acceleration.
Purpose of the Study:
- To provide a structured overview of generative AI methods in computational chemistry.
- To highlight applications and challenges of AI in predicting chemical phenomena.
Main Methods:
- Review of fundamental concepts in generative AI and computational chemistry.
- Discussion of widely used AI methods: autoencoders, generative adversarial networks, reinforcement learning, flow models, and language models.
Main Results:
- Generative AI has demonstrated progress in molecular sampling, force field development, and speeding up simulations.
- Selected applications include force field development and protein/RNA structure prediction.
Conclusions:
- Generative AI faces challenges in becoming truly predictive, especially for emergent chemical phenomena.
- Future AI models require integration of core chemical principles, particularly statistical mechanics, to meet predictive standards.
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