AIMC Topic: Models, Molecular

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Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

ACS combinatorial science Sep 14, 2016
The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation ve...
Nanostructures Machine Learning Graphite Energy Transfer Models, Molecular Molecular Structure
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ROCS-derived features for virtual screening.

Journal of computer-aided molecular design Sep 8, 2016
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. I...
Molecular Structure Pharmaceutical Preparations Machine Learning Algorithms Computer-Aided Design Drug Design Models, Molecular
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Sorting protein decoys by machine-learning-to-rank.

Scientific reports Aug 17, 2016
Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein str...
Machine Learning Algorithms Proteins Models, Molecular Protein Transport
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Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study.

Journal of molecular graphics & modelling Aug 8, 2016
Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the funda...
Hallucinogens Databases, Chemical Diterpenes, Clerodane Models, Molecular Reproducibility of Results Genomics Knowledge Bases
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Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach.

The Journal of pharmacology and experimental therapeutics Aug 3, 2016
Statistical analysis was performed on physicochemical descriptors of ∼250 drugs known to interact with one or more SLC22 "drug" transporters (i.e., SLC22A6 or OAT1, SLC22A8 or OAT3, SLC22A1 or OCT1, and SLC22A2 or OCT2), followed by application of ma...
Organic Anion Transport Protein 1 Organic Anion Transporters, Sodium-Independent Organic Cation Transporter 1 Protein Conformation Substrate Specificity Protein Binding Computational Biology Models, Molecular Pharmaceutical Preparations Hydrogen-Ion Concentration Membrane Transport Proteins Machine Learning
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Adaptive local learning in sampling based motion planning for protein folding.

BMC systems biology Aug 1, 2016
BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods ...
Computational Biology Proteins Models, Molecular Machine Learning Movement Protein Conformation Protein Folding Thermodynamics
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In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms.

Journal of chemical information and modeling Jul 20, 2016
The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many appli...
Neural Networks, Computer Machine Learning Computer Simulation Linear Models Molecular Conformation Solutions Support Vector Machine Models, Molecular Ionic Liquids
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ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Molecular pharmaceutics Jul 18, 2016
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is qu...
Bayes Theorem Quantitative Structure-Activity Relationship Potassium Channel Blockers Support Vector Machine Cluster Analysis Models, Molecular Machine Learning Drug Discovery
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gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence.

Journal of theoretical biology Jul 1, 2016
DNA-binding proteins are the functional proteins in cells, which play an important role in various essential biological activities. An effective and fast computational method gDNA-Prot is proposed to predict DNA-binding proteins in this paper, which ...
ROC Curve Computational Biology Support Vector Machine DNA-Binding Proteins Amino Acid Sequence Genome DNA Numerical Analysis, Computer-Assisted Models, Molecular Protein Domains Databases, Protein Principal Component Analysis
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Protease Inhibitors in View of Peptide Substrate Databases.

Journal of chemical information and modeling Jun 9, 2016
Protease substrate profiling has nowadays almost become a routine task for experimentalists, and the knowledge on protease peptide substrates is easily accessible via the MEROPS database. We present a shape-based virtual screening workflow using vROC...
Peptide Hydrolases Ligands Protein Conformation Databases, Pharmaceutical Substrate Specificity Protease Inhibitors Small Molecule Libraries User-Computer Interface Machine Learning Models, Molecular Peptides Drug Evaluation, Preclinical
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