Summary of Conditional Synthesis Of 3d Molecules with Time Correction Sampler, by Hojung Jung et al.
Conditional Synthesis of 3D Molecules with Time Correction Sampler
by Hojung Jung, Youngrok Park, Laura Schmid, Jaehyeong Jo, Dongkyu Lee, Bongsang Kim, Se-Young Yun, Jinwoo Shin
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Time-Aware Conditional Synthesis (TACS), a novel approach to conditional molecular generation using diffusion models. The authors tackle the trade-off between targeting specific chemical properties and generating meaningful samples by integrating adaptively controlled plug-and-play “online” guidance into the model. The new Time Correction Sampler (TCS) is used to control guidance and ensure generated molecules remain on the correct manifold at each reverse step of the diffusion process. TACS demonstrates significant performance in conditional 3D molecular generation, potentially facilitating advancements in drug discovery, materials science, and related fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating new molecules using a special type of computer program called a “diffusion model”. The challenge is to make sure the generated molecules have certain properties or characteristics. The authors developed a new way to do this, called Time-Aware Conditional Synthesis (TACS). It uses a special tool called the Time Correction Sampler (TCS) to control how the molecule is created. This approach shows promise for creating new molecules that are useful in fields like drug discovery and materials science. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
