Summary of Accelerating the Inference Of String Generation-based Chemical Reaction Models For Industrial Applications, by Mikhail Andronov et al.
Accelerating the inference of string generation-based chemical reaction models for industrial applications
by Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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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 This research proposes a method to accelerate inference in autoregressive SMILES generators, specifically targeting template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis. The authors introduce speculative decoding, which involves copying query string subsequences into target strings, achieving over 3X faster inference speed with no loss in accuracy using the molecular transformer implemented in Pytorch Lightning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a way to make predictions about chemical reactions happen faster without losing quality. It’s like having a super-smart assistant that can predict what will happen when you mix certain chemicals together, really quickly and accurately! |
Keywords
» Artificial intelligence » Autoregressive » Inference » Transformer » Translation
