AIMC Topic: Models, Molecular

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Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation.

Journal of chemical information and modeling Apr 9, 2020
drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired set of biological properties. Fragment-based design tools have been successfully applied in the disco...
Humans Machine Learning Models, Molecular Enzyme Inhibitors Computer Simulation Molecular Conformation Drug Design
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Deep Generative Models for 3D Linker Design.

Journal of chemical information and modeling Apr 2, 2020
Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of t...
Models, Molecular Algorithms Proteins Machine Learning
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Deep-learning- and pharmacophore-based prediction of RAGE inhibitors.

Physical biology Mar 16, 2020
The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, ...
Deep Learning Receptor for Advanced Glycation End Products Humans Models, Molecular Small Molecule Libraries Software
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Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.

Molecules (Basel, Switzerland) Mar 13, 2020
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals c...
Models, Molecular Quantitative Structure-Activity Relationship Animals Rats Deep Learning Cell Line, Tumor Receptors, Aryl Hydrocarbon ROC Curve Principal Component Analysis
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Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps.

Scientific reports Mar 9, 2020
Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on a...
Amino Acid Sequence Protein Conformation Sequence Homology Proteins Cryoelectron Microscopy Algorithms Models, Molecular Deep Learning Neural Networks, Computer Humans Software
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Sequence-Based Prediction of Fuzzy Protein Interactions.

Journal of molecular biology Feb 27, 2020
It is becoming increasingly recognised that disordered proteins may be fuzzy, in that they can exhibit a wide variety of binding modes. In addition to the well-known process of folding upon binding (disorder-to-order transition), many examples are em...
Fuzzy Logic Databases, Protein Models, Molecular Algorithms Protein Binding Intrinsically Disordered Proteins Protein Conformation Protein Folding Protein Domains Amino Acid Sequence Humans Sequence Homology Sequence Analysis, Protein Protein Interaction Domains and Motifs
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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...
Protein Conformation Protein Folding Caspases Software Datasets as Topic Deep Learning Models, Molecular Amino Acid Sequence Proteins
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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 Lipids Algorithms Models, Molecular Drug Discovery Quantitative Structure-Activity Relationship
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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 ...
Density Functional Theory Models, Molecular Crystallization Algorithms Structure-Activity Relationship Neural Networks, Computer Machine Learning
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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...
Models, Molecular Machine Learning Protein Folding Proteins
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