AIMC Journal:
Journal of chemical information and modeling

Showing 551 to 560 of 958 articles

MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Journal of chemical information and modeling Dec 23, 2021
Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (...
Neural Networks, Computer Machine Learning Algorithms Molecular Dynamics Simulation Entropy
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Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction.

Journal of chemical information and modeling Dec 10, 2021
We present results on the extent to which physics-based simulation (exemplified by FEP) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibit...
Machine Learning Computer Simulation Molecular Docking Simulation Ligands Physics
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Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention.

Journal of chemical information and modeling Dec 2, 2021
Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding o...
Humans Neural Networks, Computer Machine Learning Language Molecular Structure Semantics
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Structure-Based Drug Design Using Deep Learning.

Journal of chemical information and modeling Nov 18, 2021
In recent years, deep learning-based methods have emerged as promising tools for drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties....
Drug Design Deep Learning Proteins Models, Molecular Ligands
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Call for a Public Open Database of All Chemical Reactions.

Journal of chemical information and modeling Nov 10, 2021
Today there exists no public, freely downloadable, comprehensive database of all known chemical reactions and associated information. Such a database not only would serve chemical sciences and technologies around the world but also would enable the p...
Machine Learning Databases, Factual
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Machine-Guided Polymer Knowledge Extraction Using Natural Language Processing: The Example of Named Entity Normalization.

Journal of chemical information and modeling Nov 9, 2021
A rich body of literature has emerged in recent years that discusses the extraction of structured information from materials science text through named entity recognition models. Relatively little work has been done to address the "normalization" of ...
Natural Language Processing Polymers
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Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method.

Journal of chemical information and modeling Nov 9, 2021
In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical ...
Humans Neural Networks, Computer Molecular Dynamics Simulation Physical Phenomena
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Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

Journal of chemical information and modeling Nov 4, 2021
The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural netw...
Neural Networks, Computer Machine Learning Cheminformatics
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A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network.

Journal of chemical information and modeling Nov 1, 2021
This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combined with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the directed message-passin...
Neural Networks, Computer Bayes Theorem
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pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors.

Journal of chemical information and modeling Nov 1, 2021
Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other dr...
Machine Learning Software Protein Interaction Mapping
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