Review



dfcnn  (AUTODOCK GmbH)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    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/dfcnn/pmc11980327-70-5-16
    Average 90 stars, based on 1 article reviews
    dfcnn - by Bioz Stars, 2026-11
    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

    Related Articles

    Generated:

    Article Title: A beginner’s approach to deep learning applied to VS and MD techniques
    Article Snippet: DeepScreening [ , , , ] , Freely accessible web server capable of training DL models for either classification tasks or regression tasks. For classification tasks, DFCNNs perform binding probability predictions on a provided chemical library. For regression tasks, RNNs generate a de novo compound library and then perform binding probability predictions against a query protein. .. Drug repurposing DFCNN by Zhang et al. [ , ] , DFCNN that uses as input ligand compound vectors generated by Mol2vec and that is capable of predicting protein–ligand binding probabilities. It uses only molecular and chemical information of the compound vectors, not considering spatial information. .. 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.

    Binding Assay:

    Article Title: A beginner’s approach to deep learning applied to VS and MD techniques
    Article Snippet: DeepScreening [ , , , ] , Freely accessible web server capable of training DL models for either classification tasks or regression tasks. For classification tasks, DFCNNs perform binding probability predictions on a provided chemical library. For regression tasks, RNNs generate a de novo compound library and then perform binding probability predictions against a query protein. .. Drug repurposing DFCNN by Zhang et al. [ , ] , DFCNN that uses as input ligand compound vectors generated by Mol2vec and that is capable of predicting protein–ligand binding probabilities. It uses only molecular and chemical information of the compound vectors, not considering spatial information. .. 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.



    Similar Products

    dfcnn  (AUTODOCK GmbH)
    90
    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/dfcnn/pmc11980327-70-5-16
    Average 90 stars, based on 1 article reviews
    dfcnn - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier
    dfcnn  (TargetMol)
    92
    TargetMol 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 TargetMol, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/dfcnn/Lobeline/pmc07738208-2-7-29
    Average 92 stars, based on 1 article reviews
    dfcnn - by Bioz Stars, 2026-11
    92/100 stars
    		  Buy from Supplier

    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