Review



dfcnn  (AUTODOCK GmbH)

 
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    Structured Review

    AUTODOCK GmbH dfcnn
    Summary of DL models mentioned throughout the “ <xref ref-type= Deep learning and virtual screening ” section of this review used to aid in performing VS workflows" width="250" height="auto" />
    Dfcnn, supplied by AUTODOCK GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/dfcnn/pmc11980327-70-5-16?v=AUTODOCK+GmbH
    Average 90 stars, based on 1 article reviews
    dfcnn - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "A beginner’s approach to deep learning applied to VS and MD techniques"

    Article Title: A beginner’s approach to deep learning applied to VS and MD techniques

    Journal: Journal of Cheminformatics

    doi: 10.1186/s13321-025-00985-7

    Summary of DL models mentioned throughout the “ <xref ref-type= Deep learning and virtual screening ” section of this review used to aid in performing VS workflows" title="Summary of DL models mentioned throughout the “Deep learning and virtual screening” section ... " property="contentUrl" width="100%" height="100%"/>
    Figure Legend Snippet: Summary of DL models mentioned throughout the “ Deep learning and virtual screening ” section of this review used to aid in performing VS workflows

    Techniques Used: Generated, Binding Assay, Drug discovery, In Silico, In Vitro, In Vivo, Plasmid Preparation, Diffusion-based Assay, Sequencing, Modification, Protein Binding, Sampling, Molecular Weight



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    Summary of DL models mentioned throughout the “ <xref ref-type= Deep learning and virtual screening ” section of this review used to aid in performing VS workflows" width="250" height="auto" />
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    Summary of DL models mentioned throughout the “ <xref ref-type= Deep learning and virtual screening ” section of this review used to aid in performing VS workflows" width="250" height="auto" />
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    Image Search Results


    Summary of DL models mentioned throughout the “ <xref ref-type= Deep learning and virtual screening ” section of this review used to aid in performing VS workflows" width="100%" height="100%">

    Journal: Journal of Cheminformatics

    Article Title: A beginner’s approach to deep learning applied to VS and MD techniques

    doi: 10.1186/s13321-025-00985-7

    Figure Lengend Snippet: Summary of DL models mentioned throughout the “ Deep learning and virtual screening ” section of this review used to aid in performing VS workflows

    Article Snippet: DeepBindBC [ , ] , DFCNN that uses as input protein–ligand complex structures as generated by AutoDock Vina (thus considering spatial information) and that can estimate protein–ligand binding probabilities.

    Techniques: Generated, Binding Assay, Drug discovery, In Silico, In Vitro, In Vivo, Plasmid Preparation, Diffusion-based Assay, Sequencing, Modification, Protein Binding, Sampling, Molecular Weight