InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem

InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem

Abstract The development of robust artificial intelligence (AI)-driven predictive models relies on high-quality, diverse chemical datasets. However, the scarcity of negative data and a publication bias toward positive results often hinder accurate biological activity prediction. To address this chal...

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Bibliographic Details
Main Authors: Seungchan An, Yeonjin Lee, Junpyo Gong, Seokyoung Hwang, In Guk Park, Jayhyun Cho, Min Ju Lee, Minkyu Kim, Yun Pyo Kang, Minsoo Noh
Format: Article
Language:English
Published: BMC 2025-04-01
Series:Journal of Cheminformatics
Subjects:
Inactive compounds
Virtual screening
Synthetic negative data
Large-scale bioassay
Generative model
Predictive pharmacology
Online Access:https://doi.org/10.1186/s13321-025-00999-1
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https://doi.org/10.1186/s13321-025-00999-1

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