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Models, Molecular

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A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

Molecular bioSystems Dec 1, 2015
Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize ...
Gene Expression Regulation, Neoplastic Antineoplastic Agents, Phytogenic Receptors, CXCR4 Gene Regulatory Networks Protein Binding Computational Biology Molecular Conformation Binding Sites Protein Interaction Mapping Models, Molecular Mouth Neoplasms High-Throughput Screening Assays Humans Machine Learning Drug Discovery Cell Line, Tumor ROC Curve
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Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2015
Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide b...
Computational Biology Proline Machine Learning Models, Molecular Algorithms Isomerism
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Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning.

Journal of molecular recognition : JMR Jan 1, 2015
The identification of protein-protein interactions is vital for understanding protein function, elucidating interaction mechanisms, and for practical applications in drug discovery. With the exponentially growing protein sequence data, fully automate...
Proteins Algorithms Models, Molecular Protein Binding Hydrogen Bonding Binding Sites Evolution, Molecular Machine Learning Software
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Artificial neural networks for dihedral angles prediction in enzyme loops: a novel approach.

International journal of bioinformatics research and applications Jan 1, 2015
Structure prediction of proteins is considered a limiting step and determining factor in drug development and in the introduction of new therapies. Since the 3D structures of proteins determine their functionalities, prediction of dihedral angles rem...
Pattern Recognition, Automated Protein Conformation Molecular Sequence Data Models, Molecular Models, Chemical Neural Networks, Computer Protein Structure, Tertiary Amino Acid Sequence Computer Simulation Enzymes Sequence Analysis, Protein
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In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

Mini reviews in medicinal chemistry Jan 1, 2015
The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of to...
Thiamine Antiprotozoal Agents Tinidazole Quantitative Structure-Activity Relationship Benzylamines Heterocyclic Compounds Humans Models, Molecular Pharmaceutical Preparations Neural Networks, Computer Models, Biological Computer Simulation Animals Fullerenes Vitamin B Complex Drug Discovery Toxicological Phenomena Databases, Pharmaceutical Anti-HIV Agents Cyclams
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GENN: a GEneral Neural Network for learning tabulated data with examples from protein structure prediction.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its...
Software Computational Biology Proteins Models, Molecular Neural Networks, Computer Protein Conformation
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Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...
Quantitative Structure-Activity Relationship Cannabinoids Receptors, Cannabinoid Models, Molecular Ligands Databases, Chemical Algorithms Protein Binding Computational Biology Neural Networks, Computer
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AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
UNLABELLED: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predict...
Data Mining Quantitative Structure-Activity Relationship Drug Discovery Models, Molecular Pharmaceutical Preparations Humans Neural Networks, Computer Software
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Protein structural information derived from NMR chemical shift with the neural network program TALOS-N.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
Chemical shifts are obtained at the first stage of any protein structural study by NMR spectroscopy. Chemical shifts are known to be impacted by a wide range of structural factors, and the artificial neural network based TALOS-N program has been trai...
Neural Networks, Computer Models, Molecular Protein Conformation Nuclear Magnetic Resonance, Biomolecular Software Proteomics Proteins Carbon Isotopes
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