DeepCancer: A Versatile Deep Learning Platform for Target- and Cell-Based Anticancer Drug Discovery

Wu, Jingxing; Xiao, Yi; Cai, Hanxuan; Zhao, Duancheng; Li, Yirui; Luo, Hailin; Tang, Chuanqi

doi:10.2139/ssrn.4314696

Cited by 1 publication

(2 citation statements)

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“…It is an unwanted property for antibiotic discovery, and it has become a key factor leading to the failed clinical trials of antibiotics 38 . Considering that some toxicity-related benchmarking sets are not only obsolescent for practical use but also has limited correlation to pharmacological toxicology 39 , we chose to retrieve training data from a recently published platform named DeepCancerMap 40 . It contains a subset comprised of 10,861 binary inhibitory labels on 32 normal cell lines from 9,668 compounds.…”

Section: Resultsmentioning

confidence: 99%

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AutoMolDesigner for Antibiotic Discovery: An AI-based Open-source Software for Automated Design of Small-molecule Antibiotics

Shen,

Guo,

Han

et al. 2023

Preprint

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Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in the holistic and efficient tool specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative deep learning-enabled molecular generation and automated machine learning based-antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole cell-based antibiotic screening and design. It is open-source thus allows for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/8366085).

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Section: Resultsmentioning

confidence: 99%

“…The final MUBD dataset was comprised of 501 unbiased ligands and 19,539 unbiased decoys. The dataset used for cytotoxicity prediction was retrieved from a subset in DeepCancerMap 9 . This server contains 32 subsets comprised of compounds with annotated binary toxicity labels against normal cell lines.…”

Section: Methodsmentioning

confidence: 99%