Neural Network Potentials for Low-Energy 3D Structure Generation and Reactivity Prediction

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Molecular Modelling and Drug Discovery

Valence Discovery

60 episodes

1 week ago

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Neural Network Potentials for Low-Energy 3D Structure Generation and Reactivity Prediction | Zhen Liu

Molecular Modelling and Drug Discovery

49 minutes 34 seconds

2 years ago

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.

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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

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