Frank-LIU-520/DeepMoleNet
Deep learning for molecules quantum chemistry properties prediction
This tool helps computational chemists and materials scientists accurately predict key quantum chemistry properties of molecules, such as dipole moment, HOMO, and Gibbs free energy. You input molecular structure data, typically in SDF files, and it outputs precise predictions for these properties. It's designed for researchers needing reliable molecular property estimations without extensive manual quantum chemistry calculations.
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Use this if you need to quickly and accurately predict quantum chemistry properties for a set of molecules, especially for drug discovery, material design, or chemical research.
Not ideal if you primarily need to perform traditional quantum mechanics simulations or require detailed atomic-level interaction visualizations beyond property prediction.
Stars
40
Forks
7
Language
Python
License
MPL-2.0
Category
Last pushed
Apr 14, 2021
Commits (30d)
0
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