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Improved protein structure prediction using potentials from deep learning.

Nature Jan 15, 2020
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function; however, protein str...
Deep Learning Proteins Models, Molecular Software Protein Folding Datasets as Topic Amino Acid Sequence Protein Conformation Caspases
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Systematic Modeling of log  Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.

Journal of chemical information and modeling Jan 10, 2020
Lipophilicity, as evaluated by the -octanol/buffer solution distribution coefficient at pH = 7.4 (log ), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In thi...
Machine Learning Models, Molecular Quantitative Structure-Activity Relationship Drug Discovery Lipids Algorithms
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Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability.

Journal of chemical information and modeling Jan 3, 2020
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. The classical approach ...
Neural Networks, Computer Machine Learning Density Functional Theory Models, Molecular Crystallization Algorithms Structure-Activity Relationship
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Machine learning for protein folding and dynamics.

Current opinion in structural biology Dec 24, 2019
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way si...
Machine Learning Proteins Models, Molecular Protein Folding
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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...
Models, Molecular Neural Networks, Computer Surface Properties Cells, Cultured Etanercept Calibration CHO Cells Animals Cricetulus Fluorescence
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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...
Humans Cytochrome P-450 Enzyme Inhibitors Drug Interactions Cytochrome P-450 Enzyme System Machine Learning Models, Molecular
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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...
Support Vector Machine Models, Molecular Bacterial Physiological Phenomena Quorum Sensing Amino Acid Sequence Drug Discovery Bacteria Machine Learning Peptides
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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 Neural Networks, Computer Artificial Intelligence Quantitative Structure-Activity Relationship
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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...
Serine Proteinase Inhibitors Mycobacterium tuberculosis Machine Learning Serine Proteases Models, Molecular
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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...
Titanium Neural Networks, Computer Surface Properties Gold Density Functional Theory Crystallization Models, Chemical Thermodynamics Computer Simulation Models, Molecular Alloys
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