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Datasets and models enclosed within the paper: Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines.
This Figshare repository contains the datasets and models for our paper titled: Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines. It is organized into 2D and 3D, which represent the modeling architectures used in building graph neural networks for molecular descriptors corresponding to acids and amines. Carboxylic acid, primary alkyl amine, and secondary alkyl amine (as well as a combined alkyl amine) libraries are provided in their entirety, including conformer properties. Additional test and external validation statistics for each library are also provided within this repository.
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- Award ID(s):
- 2202693
- PAR ID:
- 10508167
- Publisher / Repository:
- figshare
- Date Published:
- Subject(s) / Keyword(s):
- Computational chemistry Deep learning Neural networks
- Format(s):
- Medium: X Size: 974650231 Bytes
- Size(s):
- 974650231 Bytes
- Location:
- figshare
- Sponsoring Org:
- National Science Foundation
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