Summary of Pathway-guided Optimization Of Deep Generative Molecular Design Models For Cancer Therapy, by Alif Bin Abdul Qayyum et al.
Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
by Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban, Byung-Jun Yoon
First submitted to arxiv on: 5 Nov 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 This paper proposes an optimization technique to improve the efficiency of generative molecular design (GMD) in suggesting novel drug-like molecules with enhanced properties. The authors use junction tree variational autoencoders (JTVAEs) and mechanistic models, such as pathway models described by differential equations, to optimize the latent space of GMD models. This approach is demonstrated using a pharmacodynamic model that predicts how a drug-like small molecule modulates a cancer pathway. By incorporating this model into the optimization process, the authors show improved performance in suggesting better molecules with enhanced properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us design new medicines by using computers to create new tiny molecules that might work better than existing ones. The scientists use special computer models called junction tree variational autoencoders (JTVAEs) and math equations to try out different combinations of atoms until they find something good. They’re trying to make this process faster and more effective, so we can discover new medicines faster. |
Keywords
» Artificial intelligence » Latent space » Optimization
