Summary of A Large Encoder-decoder Family Of Foundation Models For Chemical Language, by Eduardo Soares et al.
A Large Encoder-Decoder Family of Foundation Models For Chemical Language
by Eduardo Soares, Victor Shirasuna, Emilio Vital Brazil, Renato Cerqueira, Dmitry Zubarev, Kristin Schmidt
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed chemical foundation model pre-trains on a curated dataset of 91 million SMILES samples, reducing dependence on annotated datasets and broadening chemical language representation understanding. This large-scale pre-training methodology excels in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning. The model supports complex tasks like quantum property prediction and offers flexibility with two main variants (289M and 8x289M). State-of-the-art results are achieved across multiple benchmark datasets, demonstrating the capacity of the proposed model. Additionally, a preliminary assessment of the compositionality of the embedding space is provided as a prerequisite for reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A breakthrough in cheminformatics has been made with large-scale pre-training methodologies for chemical language models. These methods learn contextualized representations of input tokens through self-supervised learning on large unlabeled corpora. This makes it possible to predict properties and generate molecules without needing lots of labeled data. The proposed foundation model is trained on a huge dataset of 91 million SMILES samples, which is like having 4 billion molecular tokens! It can do different tasks, like predicting quantum properties, and has two main versions (289M and 8x289M). The results are the best so far across many benchmark datasets. This is important because it shows how well the model’s embedding space works for reasoning tasks. |
Keywords
» Artificial intelligence » Embedding space » Self supervised
