artificial neural network modeling Search Results


neural network based on the hop® eld model  (SATAKE)
90
SATAKE neural network based on the hop® eld model
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artificial neural networks and machine learning  (Tetko Inc)
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brain artificial neural network for chest x-ray reading  (Kakao Corporation)
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Kakao Corporation brain artificial neural network for chest x-ray reading
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artificial neural network  (Informa UK Limited)
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Informa UK Limited artificial neural network
Artificial Neural Network, supplied by Informa UK Limited, 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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recurrent neural network model maria  (Nature Biotechnology)
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Nature Biotechnology recurrent neural network model maria
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artificial neural networks  (Medicago)
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Medicago artificial neural networks
Artificial Neural Networks, supplied by Medicago, 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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recurrent neural network language model  (Deepmind Technologies Ltd)
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Deepmind Technologies Ltd recurrent neural network language model
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rhon algorithm  (Enron Corporation)
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convolutional neural network model  (Accelrys)
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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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artificial neural networks  (Pion Inc)
90
Pion Inc artificial 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.
Artificial Neural Networks, supplied by Pion 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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neuronal models  (Abbott Laboratories)
90
Abbott Laboratories neuronal 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.
Neuronal Models, supplied by Abbott Laboratories, 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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neural network architectures  (Nonlinear Dynamics)
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Nonlinear Dynamics neural network architectures
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.
Neural Network Architectures, supplied by Nonlinear Dynamics, 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