Gianni De Fabritiis
@gdefabritiis.bsky.social
Computer Simulations, Computational Intelligence, AI.
Research professor and interim CEO acellera.com.
Lab: www.compscience.org
Scholar: https://scholar.google.com/citations?hl=en&user=-_kX4kMAAAAJ&view_op=list_works&sortby=pubdate
Research professor and interim CEO acellera.com.
Lab: www.compscience.org
Scholar: https://scholar.google.com/citations?hl=en&user=-_kX4kMAAAAJ&view_op=list_works&sortby=pubdate
How did you make that? Manually?
November 24, 2024 at 11:01 AM
How did you make that? Manually?
After Martini, Espresso and Grappa methods/models in Italian drink names. Amaro was long overdue.
November 20, 2024 at 7:07 AM
After Martini, Espresso and Grappa methods/models in Italian drink names. Amaro was long overdue.
In Spring I come to ACS, you can get me a beer and we are even.
November 19, 2024 at 5:59 PM
In Spring I come to ACS, you can get me a beer and we are even.
You are welcome!
November 19, 2024 at 5:54 PM
You are welcome!
The next one will be called Greg.
November 19, 2024 at 4:37 PM
The next one will be called Greg.
AMARO’s transferability was tested on unseen, fast-folding proteins like Trp-Cage and α3D. It effectively recovered free energy landscapes for most
This is version 1. Model fully available.
github.com/compsciencel...
This is version 1. Model fully available.
github.com/compsciencel...
GitHub - compsciencelab/amaro
Contribute to compsciencelab/amaro development by creating an account on GitHub.
github.com
November 19, 2024 at 3:16 PM
AMARO’s transferability was tested on unseen, fast-folding proteins like Trp-Cage and α3D. It effectively recovered free energy landscapes for most
This is version 1. Model fully available.
github.com/compsciencel...
This is version 1. Model fully available.
github.com/compsciencel...
AMARO leverages TensorNet, an O(3)-equivariant neural network, and omits hydrogen atoms to reduce complexity.
Trained on the mdCATH dataset, AMARO excels in scalability and generalization. It predicts forces with minimal error even for larger domains (>150 residues).
Trained on the mdCATH dataset, AMARO excels in scalability and generalization. It predicts forces with minimal error even for larger domains (>150 residues).
November 19, 2024 at 3:16 PM
AMARO leverages TensorNet, an O(3)-equivariant neural network, and omits hydrogen atoms to reduce complexity.
Trained on the mdCATH dataset, AMARO excels in scalability and generalization. It predicts forces with minimal error even for larger domains (>150 residues).
Trained on the mdCATH dataset, AMARO excels in scalability and generalization. It predicts forces with minimal error even for larger domains (>150 residues).