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Models, Molecular

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Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins Aug 22, 2019
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...
Algorithms Reproducibility of Results Animals Humans Machine Learning Databases, Protein Phosphorylation Models, Molecular Protein Conformation Computational Biology Protein Processing, Post-Translational Proteins Serine Sequence Analysis, Protein Threonine Species Specificity Tyrosine Binding Sites
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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

Journal of chemical information and modeling Aug 22, 2019
The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of comp...
Small Molecule Libraries Cyclin-Dependent Kinase 2 Humans Machine Learning Drug Design Protein Kinase Inhibitors Thermodynamics Drug Discovery Models, Molecular
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Distance-based protein folding powered by deep learning.

Proceedings of the National Academy of Sciences of the United States of America Aug 9, 2019
Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs, even coupled with time-consuming conformation sampling with fragments. We show that we can accurate...
Models, Molecular Deep Learning Algorithms Protein Folding Time Factors Sequence Alignment Membrane Proteins
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A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Journal of chemical information and modeling Aug 8, 2019
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize ...
Neural Networks, Computer Density Functional Theory Models, Molecular Molecular Conformation
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Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Nature methods Jul 29, 2019
Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a...
Cryoelectron Microscopy Humans Deep Learning Protein Structure, Secondary Neural Networks, Computer Proteins Software Models, Molecular
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Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters Jul 22, 2019
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...
Matrix Metalloproteinase 13 Machine Learning Ligands Databases, Protein Proto-Oncogene Proteins c-bcl-2 Drug Design Models, Molecular Tumor Suppressor Protein p53 Antineoplastic Agents Mutation Cyclin-Dependent Kinase 6 Algorithms Deep Learning Databases, Pharmaceutical Binding Sites Peptides Cyclin-Dependent Kinase Inhibitor p16 Proto-Oncogene Proteins c-mdm2 Proteins
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Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.

International journal of molecular sciences Jul 22, 2019
Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain popul...
Polypharmacology Protein Binding Models, Molecular Molecular Structure HIV-1 Humans HIV Infections Bayes Theorem Quantitative Structure-Activity Relationship Molecular Conformation Machine Learning Hepacivirus Anti-HIV Agents Databases, Pharmaceutical Algorithms Antiviral Agents Hepatitis C Drug Discovery
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Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning.

Molecular informatics Jul 19, 2019
In this paper we used two sets of calculated molecular descriptors to predict blood-brain barrier (BBB) entry of a collection of 415 chemicals. The set of 579 descriptors were calculated by Schrodinger and TopoCluj software. Polly and Triplet softwar...
Models, Molecular Quantitative Structure-Activity Relationship Algorithms Blood-Brain Barrier Organic Chemicals Machine Learning Software
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Deep Reinforcement Learning for Multiparameter Optimization in Drug Design.

Journal of chemical information and modeling Jul 5, 2019
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods f...
Models, Molecular Molecular Structure Drug Design Neural Networks, Computer Drug Discovery Machine Learning Models, Theoretical
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Evaluating Polymer Representations via Quantifying Structure-Property Relationships.

Journal of chemical information and modeling Jul 3, 2019
Machine learning techniques are being applied in quantifying structure-property relationships for a wide variety of materials, where the properly represented materials play key roles. Although algorithms for representation learning are extensively st...
Polymers Machine Learning Structure-Activity Relationship Models, Molecular Molecular Structure
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