Summary of The Role Of Model Architecture and Scale in Predicting Molecular Properties: Insights From Fine-tuning Roberta, Bart, and Llama, by Lee Youngmin et al.
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA
by Lee Youngmin, Lang S.I.D. Andrew, Cai Duoduo, Wheat R. Stephen
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)
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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 study introduces a framework to compare Large Language Models (LLMs) for fine-tuning across cheminformatics tasks. Three models – RoBERTa, BART, and LLaMA – are assessed on their ability to predict molecular properties using SMILES as a universal representation format. The comparative analysis involves pre-training 18 configurations with varying parameters and dataset scales, followed by fine-tuning on six DeepChem benchmarking tasks. The study finds that LLaMA-based models generally offer the lowest validation loss, suggesting superior adaptability across tasks and scales. However, absolute validation loss is not a definitive indicator of model performance – contradicting previous research – as model size plays a crucial role. The study provides a robust methodology for selecting the most suitable LLM for specific cheminformatics applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand which Large Language Models (LLMs) are best for predicting molecular properties in chemistry. It compares three models, RoBERTa, BART, and LLaMA, to see how well they can predict things like whether a molecule is good or bad for making medicine. The study uses a special format called SMILES to represent molecules, and it tests the models on six different tasks. The results show that one model, LLaMA, works really well across all the tasks. But the study also finds that how big the model is matters more than just looking at how good or bad it is. This research helps us choose the right model for our needs in fields like drug discovery. |
Keywords
» Artificial intelligence » Fine tuning » Llama
