Summary of Enhancing Generative Molecular Design Via Uncertainty-guided Fine-tuning Of Variational Autoencoders, by a N M Nafiz Abeer et al.
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
by A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban, Byung-Jun Yoon
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
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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 A deep generative model has been successfully applied in various molecular design tasks in life and material sciences. However, fine-tuning these models to optimize specific molecular properties is a challenge. Redesigning an effective model from scratch for each new task is impractical due to the black-box nature of property prediction tasks. To address this, we propose a novel approach that uses performance feedback in active learning settings to fine-tune pre-trained variational autoencoder (VAE)-based generative molecular design (GMD) models. Our method quantifies model uncertainty, which expands the space of viable molecules through decoder diversity. We then optimize black-box optimization made tractable by low-dimensionality of the active subspace. Empirical results show that our approach consistently outperforms original pre-trained models across six target molecular properties using multiple VAE-based generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Molecular design tasks are crucial in life and material sciences, and deep generative models have been successful in this area. Fine-tuning these models to optimize specific molecular properties is a challenge because it’s hard to redesign an effective model from scratch for each new task. The problem gets worse when the goal is to predict certain properties of molecules, as this is a black-box process that’s hard to understand. Our solution uses feedback from the performance of the model in an active learning setting to fine-tune pre-trained models. We also use something called “model uncertainty” which helps us find more diverse and better molecules. |
Keywords
» Artificial intelligence » Active learning » Decoder » Fine tuning » Generative model » Optimization » Variational autoencoder
