shehzaidi/pre-training-via-denoising
Official implementation of pre-training via denoising for TorchMD-NET
This project helps chemists and materials scientists predict properties of new molecules by 'pre-training' a model with vast amounts of existing molecular structure data. It takes in 3D molecular structures (like from the PCQM4Mv2 dataset) and outputs a trained model capable of accurately predicting specific molecular properties, such as HOMO/LUMO energy levels. It's designed for researchers who need to develop highly accurate predictive models for drug discovery or materials science applications.
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Use this if you need to predict properties of molecules with high accuracy, especially when you have a large dataset of molecular structures to leverage for initial model training.
Not ideal if you don't have access to computational resources (GPUs) or are not comfortable working with command-line interfaces to run machine learning models.
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Language
Python
License
MIT
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Last pushed
Mar 02, 2023
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