Sign up to save your podcasts
Or
Share Neural Network Potentials for Low-Energy 3D Structure Generation and Reactivity Prediction | Zhen Liu
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Neural Network Potentials for Low-Energy 3D Structure Generation and Reactivity Prediction | Zhen Liu
[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.
Also, consider joining the M2D2 Slack.
Abstract: Accurate 3D molecular information is a keystone for many computational programs but accessing reliable 3D conformers is still challenging. It requires enumerating and optimizing a huge isomer and conformer space, which would overwhelm any traditional computational methods. In light of this, we proposed the Auto3D package for generating low-energy 3D conformers using fast and reliable neural network potentials (NNPs). Given a SMILES, Auto3D returns the low-energy 3D conformers by automatizing the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization process, and ranking process. In conjunction with Auto3D, we developed ANI-2xt NNP, which was trained especially for tautomer-related tasks. These NNPs were used to generate 3D structures and compute molecular properties. In a tautomeric reaction energy calculation task, the ANI-2xt NNP achieved similar accuracy but was several orders of magnitude faster than the reference DFT method.
Speaker: Zhen Liu
Twitter - Prudencio
Twitter - Therence
Twitter - Jonny
Twitter - Valence Discovery
View all episodes
By Valence Discovery[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.
Also, consider joining the M2D2 Slack.
Abstract: Accurate 3D molecular information is a keystone for many computational programs but accessing reliable 3D conformers is still challenging. It requires enumerating and optimizing a huge isomer and conformer space, which would overwhelm any traditional computational methods. In light of this, we proposed the Auto3D package for generating low-energy 3D conformers using fast and reliable neural network potentials (NNPs). Given a SMILES, Auto3D returns the low-energy 3D conformers by automatizing the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization process, and ranking process. In conjunction with Auto3D, we developed ANI-2xt NNP, which was trained especially for tautomer-related tasks. These NNPs were used to generate 3D structures and compute molecular properties. In a tautomeric reaction energy calculation task, the ANI-2xt NNP achieved similar accuracy but was several orders of magnitude faster than the reference DFT method.
Speaker: Zhen Liu
Twitter - Prudencio
Twitter - Therence
Twitter - Jonny
Twitter - Valence Discovery