article

ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules

Shuhao Zhang, Roman Zubatyuk, Yinuo Yang, Adrian Roitberg, Olexandr Isayev

Journal of Chemical Theory and Computation Vol. 21 (9) pp. 4365–4374 2025 10 citations

Highlight

ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules.

Cite This Paper

@article{Zhang2025,
  author = {Zhang, Shuhao and Zubatyuk, Roman and Yang, Yinuo and Roitberg, Adrian and Isayev, Olexandr},
  title = {ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules},
  year = {2025},
  journal = {Journal of Chemical Theory and Computation},
  volume = {21},
  number = {9},
  pages = {4365--4374},
  doi = {10.1021/acs.jctc.5c00347},
  url = {http://dx.doi.org/10.1021/acs.jctc.5c00347},
  publisher = {American Chemical Society (ACS)},
  highlight = {ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules.},
  citations = {10}
}

Related Publications

2024
cited85

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Zhang S., Makoś M. Z., Jadrich R. B., Kraka E., Barros K., Nebgen B. T., Tretiak S., Isayev O., Lubbers N., Messerly R. A., Smith J. S.

Nature Chemistry, 16, 727–734 (2024)

Ml Potentials
Experiment Automation

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.

DOI
2025
cited47

AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs

Anstine D. M., Zubatyuk R., Isayev O.

Chemical Science, 16, 10228–10244 (2025)

Ml Potentials

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

DOI
2023
cited201

Generative Models as an Emerging Paradigm in the Chemical Sciences

Anstine D. M., Isayev O.

J. Am. Chem. Soc., 145, 8736–8750 (2023)

Generative Models as an Emerging Paradigm in the Chemical Sciences.

DOI
2023
cited143

Machine Learning Interatomic Potentials and Long-Range Physics

Anstine D. M., Isayev O.

J. Phys. Chem. A, 127, 2417–2431 (2023)

Ml Potentials

Machine Learning Interatomic Potentials and Long-Range Physics.

DOI
2022
cited74

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

Korshunova M., Huang N., Capuzzi S., Radchenko D. S., Savych O., Moroz Y. S., Wells C. I., Willson T. M., Tropsha A., Isayev O.

Communications Chemistry, 5 (2022)

Generative Ai
Drug Discovery
Experiment Automation

AbstractDeep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties.

DOI