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AAAI 2022

HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract)

Short Paper AAAI Student Abstract and Poster Program Artificial Intelligence

Abstract

Large-scale transformer-based methods are gaining popularity as a tool for predicting the properties of chemical compounds, which is of central importance to the drug discovery process. To accelerate their development and dissemination among the community, we are releasing HuggingMolecules – an open-source library, with a simple and unified API, that provides the implementation of several state-of-the-art transformers for molecular property prediction. In addition, we add a comparison of these methods on several regression and classification datasets. HuggingMolecules package is available at: github. com/gmum/huggingmolecules.

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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
1143074090861092092