ArticleBioinformatics (Oxford, England)2024
Afpdb: an efficient structure manipulation package for AI protein design.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- CD98hc-targeted antibody shuttles for central nervous system delivery with broad cross-species reactivity.Nature biomedical engineering · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe advent of AlphaFold and other protein Artificial Intelligence (AI) models has transformed protein design, necessitating efficient handling of large-scale data and complex workflows. Using existing programming packages that predate recent AI advancements often leads to inefficiencies in human coding and slow code execution. To address this gap, we developed the Afpdb package.
resultsAfpdb, built on AlphaFold's NumPy architecture, offers a high-performance core. It uses RFDiffusion's contig syntax to streamline residue and atom selection, making coding simpler and more readable. Integrating PyMOL's visualization capabilities, Afpdb allows automatic visual quality control. With over 180 methods commonly used in protein AI design, which are otherwise hard to find, Afpdb enhances productivity in structural biology by supporting the development of concise, high-performance code. AVAILABILITY AND IMPLEMENTATION: Code and documentation are available on GitHub (https://github.com/data2code/afpdb) and PyPI (https://pypi.org/project/afpdb). An interactive tutorial is accessible through Google Colab.
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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.