Quandary with Quantum Chemistry 🧪
So essentially,
SMI-TED289M knows chemistry
Paper:
A Large Encoder-Decoder Family of Foundation Models For Chemical Language (14 Pages)
Researchers from IBM Research are interested in adapting LLMS for Chemical Language. The research focuses on the development and evaluation of SMI-TED289M, a family of large-scale encoder-decoder chemical foundation models for chemical language processing.
Hmm..What’s the background?
The introduction of large-scale pre-training methodologies for chemical language models has marked a significant advancement in cheminformatics. These models excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on vast, unlabeled datasets. This study emphasizes the importance of data quality for the performance of foundation models, noting that the quality of pre-training data significantly impacts the model's outcomes.
Ok, So what is proposed in the research paper?
This study introduces SMI-TED289M, pre-trained on a curated dataset of 91 million SMILES (Simplified Molecular-Input Line Entry System) samples sourced from PubChem, equivalent to 4 billion molecular tokens. The model comes in two main variants: a base model with 289 million parameters and a Mixture-of-SMI-TED-Experts (SMI-TED8x289M) model with 8 × 289M parameters.
The effectiveness of SMI-TED289M is evaluated on various classification and regression tasks using 11 benchmark datasets from MoleculeNet, encompassing quantum mechanical, physical, biophysical, and physiological property prediction of small molecules
The model's reconstruction capacity is assessed using the MOSES benchmarking dataset
The results demonstrate state-of-the-art performance of SMI-TED289M in molecular properties prediction, molecule reconstruction, and an efficient metric for molecular latent space
Source: Paper
What’s next?
The sources suggest that the research on SMI-TED289M has promising future directions, particularly in expanding the model's capabilities for reasoning applications. The authors highlight the need for further studies that align with methodologies used for compositionally analysis in natural language processing to make more definitive statements about the model's reasoning potential.
So essentially,
SMI-TED289M knows chemistry
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