ArticleProceedings of the National Academy of Sciences of the United States of America2025
Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Predicting phase transitions across temperature, pressure, and chemical potential using exponentially tilted thermodynamic maps.Nature communications · 2026Article
- Unlocking the Chemical Space for Rechargeable Batteries with a Generative Solvent Design System.ACS nano · 2026Article
- Article
- Free-Energy Landscapes of HBV Hexamer Closure Reveal Key Structural Features of the Transition.Journal of chemical theory and computation · 2026Article
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Integrating generative AI and physical tests to strengthen claims about emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- af2rave: protein ensemble generation with physics-based sampling.Digital discovery · 2025Article
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.