AIMC Topic: Models, Molecular

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Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

Scientific reports May 25, 2017
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property play...
Machine Learning Software Algorithms Humans Chemical Phenomena Quantitative Structure-Activity Relationship Intestinal Absorption Drug Discovery Models, Molecular Blood-Brain Barrier
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Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.

PloS one May 24, 2017
De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proxi...
Amino Acid Sequence Algorithms Protein Structure, Secondary Membrane Proteins Protein Folding Models, Molecular Humans Machine Learning
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Protein-Ligand Scoring with Convolutional Neural Networks.

Journal of chemical information and modeling Apr 11, 2017
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affini...
Protein Conformation Computational Biology Proteins Drug Evaluation, Preclinical Neural Networks, Computer Models, Molecular Ligands User-Computer Interface
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Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

BioMed research international Apr 9, 2017
The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redunda...
Ion Channels Conotoxins Support Vector Machine Models, Molecular
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Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

BMC bioinformatics Mar 23, 2017
BACKGROUND: One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex i...
Data Accuracy Protein Conformation Models, Statistical Proteins Humans Models, Molecular Machine Learning Animals Protein Binding
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FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.

Journal of computational chemistry Mar 10, 2017
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a krigi...
Amino Acids Protein Conformation Static Electricity Proteins Models, Molecular Physical Phenomena Thermodynamics Peptides Models, Theoretical Machine Learning
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Crystal Structure Representation for Neural Networks using Topological Approach.

Molecular informatics Mar 7, 2017
In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotie...
Neural Networks, Computer Software Algorithms Humans Entropy Models, Molecular Molecular Structure
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Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.

Molecular informatics Feb 28, 2017
HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replicati...
HIV Integrase Inhibitors Humans Molecular Conformation HIV Integrase Machine Learning Molecular Structure Computer Simulation Intercellular Signaling Peptides and Proteins Protein Binding Structure-Activity Relationship Models, Molecular Reproducibility of Results ROC Curve
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A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016.

Journal of chemical information and modeling Feb 27, 2017
We present the third generation of our scoring function for the prediction of protein-ligand binding free energy. This function is now a hybrid between a knowledge-based potential and an empirical function. We constructed a diversified set of ∼1000 c...
Protein Binding Proteins Models, Molecular Knowledge Bases Protein Conformation Databases, Protein Entropy Ligands Probability
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Classifying kinase conformations using a machine learning approach.

BMC bioinformatics Feb 2, 2017
BACKGROUND: Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and ...
Protein Kinases Catalytic Domain Models, Molecular Protein Conformation Machine Learning
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