Generative AI for computational chemistry: A roadmap to predicting emergent phenomena

• 成果类型: Article
• 署名作者: Tiwary, Pratyush; Herron, Lukas; John, Richard; Lee, Suemin; Sanwal, Disha; Wang, Ruiyu
• 署名单位: University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2415655121
• 发表日期: 2025-10-14
• 页码: e2415655121
• 关键词: generative AI; computational chemistry; molecular modeling; rna; DYNAMICS

摘要

The recent surge in generative AI has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both generative AI and computational chemistry. It then covers widely used generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models, and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before and that generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics.