Summary of Sddbench: a Benchmark For Synthesizable Drug Design, by Songtao Liu et al.
SDDBench: A Benchmark for Synthesizable Drug Design
by Songtao Liu, Zhengkai Tu, Hanjun Dai, Peng Liu
First submitted to arxiv on: 13 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 A significant challenge in wet lab experiments with current drug design generative models is the trade-off between pharmacological properties and synthesizability. To address this, researchers propose a new metric called the round-trip score to evaluate molecule synthesizability by leveraging retrosynthetic planners and reaction predictors trained on extensive reaction datasets. The proposed method assesses the feasibility of synthetic routes for a given molecule, providing a more accurate evaluation than the commonly used synthetic accessibility (SA) score. The study conducts a comprehensive evaluation of round-trip scores alongside search success rate across various representative molecule generative models, demonstrating the efficacy of this novel metric. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a machine that can create new medicines. But it’s hard to find ways to make these medicines in the lab. This is a big problem for scientists trying to discover new treatments. They need a way to measure how easy or hard it is to make each medicine. That’s where this paper comes in. The researchers created a new method to evaluate how well the machine can make each medicine, by looking at the possible paths that could be taken in the lab to create the medicine. This helps scientists choose the best medicines and makes their job easier. |
