ArticlePhysical chemistry chemical physics : PCCP2024
An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.
Article in Physical chemistry chemical physics : PCCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Simulating Biomolecules with the Variable Protonation State: A Tutorial for Constant-pH Molecular Dynamics in NAMD.The journal of physical chemistry. B · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents is of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.