AIMC Topic: Models, Molecular

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Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology.

Talanta Dec 23, 2019
In the last years, regulatory agencies in biopharmaceutical industry have promoted the design and implementation of Process Analytical Technology (PAT), which aims to develop rapid and high-throughput strategies for real-time monitoring of bioprocess...
Neural Networks, Computer Cells, Cultured Animals Models, Molecular Surface Properties Calibration Fluorescence CHO Cells Cricetulus Etanercept
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Computational prediction of cytochrome P450 inhibition and induction.

Drug metabolism and pharmacokinetics Dec 20, 2019
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is high...
Cytochrome P-450 Enzyme System Cytochrome P-450 Enzyme Inhibitors Machine Learning Models, Molecular Drug Interactions Humans
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iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.

International journal of molecular sciences Dec 20, 2019
Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the p...
Models, Molecular Amino Acid Sequence Bacterial Physiological Phenomena Bacteria Machine Learning Support Vector Machine Quorum Sensing Peptides Drug Discovery
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Validation Study of QSAR/DNN Models Using the Competition Datasets.

Molecular informatics Dec 18, 2019
Since the QSAR/DNN model showed predominant predictive performance over other conventional methods in the Kaggle QSAR competition, many artificial neural network (ANN) methods have been applied to drug and material discovery. Appearance of artificial...
Databases, Factual Models, Molecular Quantitative Structure-Activity Relationship Neural Networks, Computer Artificial Intelligence
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A genetic programming-based approach to identify potential inhibitors of serine protease of .

Future medicinal chemistry Nov 15, 2019
We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of () and the discovery of new inhibitors as drug candidates. The experimental dataset of serine protease inhibit...
Mycobacterium tuberculosis Machine Learning Models, Molecular Serine Proteases Serine Proteinase Inhibitors
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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling Nov 5, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...
Gold Models, Chemical Titanium Computer Simulation Surface Properties Alloys Neural Networks, Computer Density Functional Theory Thermodynamics Crystallization Models, Molecular
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Improving neural protein-protein interaction extraction with knowledge selection.

Computational biology and chemistry Oct 23, 2019
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and thei...
Protein Interaction Mapping Models, Molecular Proteins Knowledge Bases Datasets as Topic
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Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Journal of computer-aided molecular design Oct 18, 2019
In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibito...
Drug Design Protein Binding Computational Biology Proteins Models, Molecular Artificial Intelligence Machine Learning Software Humans Protein Conformation Amino Acid Sequence Small Molecule Libraries Sequence Homology Ligands
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Recent developments in deep learning applied to protein structure prediction.

Proteins Oct 14, 2019
Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent...
Structural Homology, Protein Neural Networks, Computer Deep Learning Proteins Computational Biology Models, Molecular Protein Conformation
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Analysis of distance-based protein structure prediction by deep learning in CASP13.

Proteins Sep 13, 2019
This paper reports the CASP13 results of distance-based contact prediction, threading, and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by...
Crystallography, X-Ray Protein Conformation Machine Learning Protein Folding Software Algorithms Proteins Sequence Alignment Deep Learning Models, Molecular Sequence Analysis, Protein Computational Biology
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