AIMC Topic: Models, Molecular

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TopScore: Using Deep Neural Networks and Large Diverse Data Sets for Accurate Protein Model Quality Assessment.

Journal of chemical theory and computation Oct 9, 2018
The value of protein models obtained with automated protein structure prediction depends primarily on their accuracy. Protein model quality assessment is thus critical to select the model that can best answer biologically relevant questions from an e...
Databases, Protein Models, Molecular Protein Conformation Neural Networks, Computer Proteins
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Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication.

Journal of chemical information and modeling Sep 7, 2018
Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory...
Surface Properties Zeolites Monte Carlo Method Models, Molecular Machine Learning Silicon Computer Simulation
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Extraction, identification and structure-activity relationship of antioxidant peptides from sesame (Sesamum indicum L.) protein hydrolysate.

Food research international (Ottawa, Ont.) Sep 4, 2018
To elucidate the sequence, origin and structure-activity relationship of antioxidant peptides from sesame protein, sesame protein was hydrolysed by a dual-enzyme system comprised alcalase and trypsin, then this hydrolysate was fractionated by ultrafi...
Subtilisins Sulfonic Acids Protein Hydrolysates Sesamum Biphenyl Compounds Picrates Models, Molecular Antioxidants Trypsin Benzothiazoles Oligopeptides Amino Acid Sequence Seeds Quantitative Structure-Activity Relationship
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Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery.

Journal of chemical information and modeling Aug 28, 2018
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, method comparison is difficult because of various flaws of the currently employed evaluation metrics. We...
Software Databases, Factual Computer Simulation Models, Molecular Drug Discovery Deep Learning
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Algorithmic Analysis of Cahn-Ingold-Prelog Rules of Stereochemistry: Proposals for Revised Rules and a Guide for Machine Implementation.

Journal of chemical information and modeling Aug 17, 2018
The most recent version of the Cahn-Ingold-Prelog rules for the determination of stereodescriptors as described in Nomenclature of Organic Chemistry: IUPAC Recommendations and Preferred Names 2013 (the "Blue Book"; Favre and Powell. Royal Society of ...
Machine Learning Software Organic Chemicals Models, Molecular Structure-Activity Relationship Molecular Structure Stereoisomerism Algorithms Computer Simulation
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Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme.

Chemical research in toxicology Jul 30, 2018
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacop...
Protein Conformation Estrogen Receptor alpha Support Vector Machine Models, Molecular Quantitative Structure-Activity Relationship Computer Simulation Crystallography, X-Ray Humans Ligands Reproducibility of Results Machine Learning Protein Binding
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Conditional Molecular Design with Deep Generative Models.

Journal of chemical information and modeling Jul 27, 2018
Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method th...
Drug Design Models, Molecular Machine Learning Computer Simulation
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Deep reinforcement learning for de novo drug design.

Science advances Jul 25, 2018
We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ...
Small Molecule Libraries Janus Kinase 2 Quantitative Structure-Activity Relationship Drug Design Neural Networks, Computer Deep Learning Models, Molecular
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Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme.

Molecules (Basel, Switzerland) Jul 22, 2018
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood⁻brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorp...
Computer Simulation Support Vector Machine ATP Binding Cassette Transporter, Subfamily B, Member 1 Quantitative Structure-Activity Relationship Models, Molecular Humans
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3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction.

Journal of physics. Condensed matter : an Institute of Physics journal Jul 2, 2018
In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These s...
Molecular Conformation Thermodynamics Models, Molecular Linear Models Neural Networks, Computer
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