AIMC Topic: Models, Molecular

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Machine learning-assisted directed protein evolution with combinatorial libraries.

Proceedings of the National Academy of Sciences of the United States of America Apr 12, 2019
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequ...
Models, Molecular Amino Acid Sequence Humans Machine Learning Small Molecule Libraries Combinatorial Chemistry Techniques Oxygenases Protein Conformation Directed Molecular Evolution Rhodothermus
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Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning.

Molecules (Basel, Switzerland) Apr 10, 2019
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability t...
Models, Molecular Nucleic Acid Conformation Machine Learning Neural Networks, Computer DNA
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Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks.

Journal of chemical information and modeling Apr 8, 2019
Several recent reports have shown that long short-term memory generative neural networks (LSTM) of the type used for grammar learning efficiently learn to write Simplified Molecular Input Line Entry System (SMILES) of druglike compounds when trained ...
Models, Molecular Neural Networks, Computer Pharmaceutical Preparations Molecular Conformation Cheminformatics
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Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Journal of chemical information and modeling Apr 2, 2019
Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, ...
Machine Learning Adsorption Hydrogen Silver Models, Molecular Molecular Conformation Alloys Thermodynamics
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DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules.

Journal of chemical information and modeling Mar 27, 2019
Computer simulation studies of multiphase systems rely on the accurate identification of local molecular structures and arrangements in order to extract useful insights. Local order parameters, such as Steinhardt parameters, are widely used for this ...
Deep Learning Molecular Conformation Ice Models, Molecular
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Autonomous Molecular Design: Then and Now.

ACS applied materials & interfaces Mar 25, 2019
The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. I...
Drug Design Models, Molecular Machine Learning Computer Simulation
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GuacaMol: Benchmarking Models for de Novo Molecular Design.

Journal of chemical information and modeling Mar 19, 2019
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural net...
Monte Carlo Method Isomerism Quantitative Structure-Activity Relationship Pharmaceutical Preparations Benchmarking Drug Design Models, Molecular Molecular Structure Deep Learning
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Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling Mar 5, 2019
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...
Drug Design Protein Binding Cell Line Computers, Molecular Models, Molecular Computational Biology Quantitative Structure-Activity Relationship Humans Receptors, FSH Machine Learning Binding Sites Ligands Hydrophobic and Hydrophilic Interactions
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Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling Feb 28, 2019
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...
Pharmaceutical Preparations Hydrophobic and Hydrophilic Interactions Models, Molecular Machine Learning Molecular Structure Drug Design Molecular Conformation Quantitative Structure-Activity Relationship Hydrogen Bonding
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Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Molecules (Basel, Switzerland) Feb 19, 2019
The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, includ...
Spectrum Analysis Molecular Conformation Models, Molecular beta-Lactams Machine Learning Cyclodextrins Structure-Activity Relationship
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