ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design

Clicks: 4
ID: 324272
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #232 of 829 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: De novo drug design requires not only promoting desired target activity, but also avoiding undesired target interactions that compromise selectivity and safety. However, existing generative models largely focus on optimizing positive activity, while overlooking negative activity information that could help suppress off-target effects during molecular design. RESULTS: We present ActivityDiff, a classifier-guided diffusion framework for activity-controlled molecular generation. Unlike conventional approaches that rely primarily on positive activity optimization, ActivityDiff explicitly incorporates both positive and negative guidance through separately trained drug-target classifiers. This design enables the model not only to promote desired target activities, but also to suppress harmful off-target interactions during generation. Experiments show that ActivityDiff effectively supports various drug design tasks, including single- and dual-target generation, fragment-constrained dual-target design, selective generation for improved target specificity, and reduction of off-target effects. These results demonstrate that classifier-guided diffusion with explicit negative guidance provides an effective strategy for jointly optimizing efficacy and safety in molecular design. AVAILABILITY: The source code can be obtained from https://github.com/e-yi/ActivityDiff. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7196994330 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Huimin Zhu, Renyi Zhou, Jing Tang, Min Li
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag564
URL
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag564/70507023/btag564.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.