deep learning convolutional neural network models Search Results


imagenet classification with deep convolutional neural networks  (Curran Associates Inc)
90
Curran Associates Inc imagenet classification with deep convolutional neural networks
Imagenet Classification With Deep Convolutional Neural Networks, supplied by Curran Associates Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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3tp u-net deep convolutional neural network  (Marrone Bio Innovations)
90
Marrone Bio Innovations 3tp u-net deep convolutional neural network
3tp U Net Deep Convolutional Neural Network, supplied by Marrone Bio Innovations, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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convolutional neural network model  (Accelrys)
90
Accelrys convolutional neural network model
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Convolutional Neural Network Model, supplied by Accelrys, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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convolutional neural network model - by Bioz Stars, 2026-03
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convolutional neural network models  (Cleerly Inc)
90
Cleerly Inc convolutional neural network models
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Convolutional Neural Network Models, supplied by Cleerly Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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convolutional neural network models - by Bioz Stars, 2026-03
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mobile convolution neural network  (Multimed Inc)
90
Multimed Inc mobile convolution neural network
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Mobile Convolution Neural Network, supplied by Multimed Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural networks  (TenCent Inc)
90
TenCent Inc deep convolutional neural networks
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Deep Convolutional Neural Networks, supplied by TenCent Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural networks - by Bioz Stars, 2026-03
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very deep convolutional networks for large-scale image recognition  (Curran Associates Inc)
90
Curran Associates Inc very deep convolutional networks for large-scale image recognition
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Very Deep Convolutional Networks For Large Scale Image Recognition, supplied by Curran Associates Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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very deep convolutional networks for large-scale image recognition - by Bioz Stars, 2026-03
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deep convolutional neural networks  (Optik GmbH)
90
Optik GmbH deep convolutional neural networks
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Deep Convolutional Neural Networks, supplied by Optik GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cascaded convolutional neural network  (IEEE Access)
90
IEEE Access cascaded convolutional neural network
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Cascaded Convolutional Neural Network, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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transferred learning-based convolutional neural network  (Imoto Machinery Co)
90
Imoto Machinery Co transferred learning-based convolutional neural network
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Transferred Learning Based Convolutional Neural Network, supplied by Imoto Machinery Co, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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transferred learning-based convolutional neural network - by Bioz Stars, 2026-03
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custom convolutional neural network models  (Hamad Medical Corporation)
90
Hamad Medical Corporation custom convolutional neural network models
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Custom Convolutional Neural Network Models, supplied by Hamad Medical Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural network applications  (Lohmeyer GmbH)
90
Lohmeyer GmbH deep convolutional neural network applications
Schematic of deep <t>convolutional</t> neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.
Deep Convolutional Neural Network Applications, supplied by Lohmeyer GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Schematic of deep convolutional neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.

Journal: Journal of chemical information and modeling

Article Title: Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

doi: 10.1021/acs.jcim.8b00104

Figure Lengend Snippet: Schematic of deep convolutional neural network models for predicting small-molecule reactivity and bioassay promiscuity. (A) Atoms in a test compound are represented as rows of numerical descriptors in a data matrix. These data are input to a neural network with one hidden layer of ten units. This neural network calculates four atom reactivity scores, each score predicts nucleophilic attack at that atom by GSH, cyanide, DNA, or protein. The top five atom reactivity scores in each category are then combined with molecule descriptors and are then used to calculate four molecule reactivity scores. Each molecule level reactivity score is then trained to predict conjugation of the input molecule to either GSH, cyanide, DNA, or protein.34,35 (B) Molecule-level reactivity scores are further combined with another neural network to produce a single integrated reactive promiscuity score. This network can then be trained to predict promiscuous bioactivity in HTS data sets. (C) A hybrid model combines molecule-level reactivity scores with binary indicators for PAINS substructure filter matches. A single hidden layer neural network is then trained to predict promiscuous behavior in HTS data sets.

Article Snippet: 34 , 35 Briefly, a convolutional neural network model was trained using literature-derived data extracted from the Accelrys Metabolite Database and other sources.

Techniques: Bioassay, Conjugation Assay