AIMC Journal:
Journal of chemical information and modeling

Showing 321 to 330 of 934 articles

Combining IC or Values from Different Sources Is a Source of Significant Noise.

Journal of chemical information and modeling Feb 23, 2024
As part of the ongoing quest to find or construct large data sets for use in validating new machine learning (ML) approaches for bioactivity prediction, it has become distressingly common for researchers to combine literature IC data generated using ...
Machine Learning Software Biological Assay
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Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Journal of chemical information and modeling Feb 22, 2024
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated si...
Machine Learning Proteins Ligands
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MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models.

Journal of chemical information and modeling Feb 22, 2024
Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. ...
Machine Learning Algorithms
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Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units.

Journal of chemical information and modeling Feb 21, 2024
Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the deve...
Neural Networks, Computer Algorithms Communication Intelligence Acceleration
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graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Journal of chemical information and modeling Feb 17, 2024
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of bin...
Neural Networks, Computer Drug Discovery Protein Binding Proteins Ligands
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Image2InChI: Automated Molecular Optical Image Recognition.

Journal of chemical information and modeling Feb 15, 2024
The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable repre...
Drug Discovery Deep Learning Image Processing, Computer-Assisted Optical Imaging
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The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling.

Journal of chemical information and modeling Feb 12, 2024
There has been a growing recognition of the need for diversity and inclusion in scientific fields. This trend is reflected in the Journal of Chemical Information and Modeling (JCIM), where there has been a gradual increase in the number of papers tha...
Humans Female Natural Language Processing Models, Chemical
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Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow.

Journal of chemical information and modeling Feb 6, 2024
Machine learning (ML) methods can train a model to predict material properties by exploiting patterns in materials databases that arise from structure-property relationships. However, the importance of ML-based feature analysis and selection is often...
Machine Learning Algorithms Databases, Factual Bayes Theorem Workflow
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From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.

Journal of chemical information and modeling Feb 6, 2024
Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for s...
Neural Networks, Computer Databases, Factual Peptides Organic Chemicals Entropy
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Pretraining Strategies for Structure Agnostic Material Property Prediction.

Journal of chemical information and modeling Feb 1, 2024
In recent years, machine learning (ML), especially graph neural network (GNN) models, has been successfully used for fast and accurate prediction of material properties. However, most ML models rely on relaxed crystal structures to develop descriptor...
Humans Neural Networks, Computer Machine Learning
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