Summary of Mf-lal: Drug Compound Generation Using Multi-fidelity Latent Space Active Learning, by Peter Eckmann et al.
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
by Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K Gilson, Rose Yu
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed Multi-Fidelity Latent space Active Learning (MF-LAL) framework integrates a set of oracles with varying cost-accuracy tradeoffs to address the challenge of inaccurate activity prediction in generative models for drug discovery. The framework combines generative and multi-fidelity surrogate models, enabling more accurate activity prediction and higher quality samples. A novel active learning algorithm is used to further reduce computational cost. Experimental results on two disease-relevant proteins show that MF-LAL produces compounds with significantly better binding free energy scores than other approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MF-LAL is a new way to find new medicines by combining different methods that help predict how well a medicine works. Usually, these methods are too slow or not accurate enough for use in medicine discovery. The team created a single framework that combines several methods with different levels of accuracy and speed. This helps make more accurate predictions about how well a medicine will work and creates better samples to test. The new approach was tested on two proteins related to diseases, and the results show it can create medicines with much better binding energy scores than other approaches. |
Keywords
» Artificial intelligence » Active learning » Latent space
