AIMC Journal:
Journal of chemical information and modeling

Showing 411 to 420 of 934 articles

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning.

Journal of chemical information and modeling Jul 24, 2023
Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric facto...
Neural Networks, Computer Machine Learning Temperature Transition Temperature
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PREFER: A New Predictive Modeling Framework for Molecular Discovery.

Journal of chemical information and modeling Jul 24, 2023
Machine-learning and deep-learning models have been extensively used in cheminformatics to predict molecular properties, to reduce the need for direct measurements, and to accelerate compound prioritization. However, different setups and frameworks a...
Machine Learning Cheminformatics
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Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review.

Journal of chemical information and modeling Jul 17, 2023
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisi...
Humans Artificial Intelligence Machine Learning Databases, Factual Drug Interactions Drug-Related Side Effects and Adverse Reactions
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Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Journal of chemical information and modeling Jul 17, 2023
Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI d...
Machine Learning Drug Discovery Proteins Databases, Protein
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A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Journal of chemical information and modeling Jul 3, 2023
Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique l...
Neural Networks, Computer Proteins Amino Acids Amino Acid Sequence Catalytic Domain
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Augmenting Polymer Datasets by Iterative Rearrangement.

Journal of chemical information and modeling Jun 30, 2023
One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the success of data augmentation in computer vision and natural lan...
Machine Learning Natural Language Processing Polymers
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RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing.

Journal of chemical information and modeling Jun 27, 2023
Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex; thus, robustly parsing them into structured data is an open challenge. In this paper, we...
Machine Learning
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Discovery of a Novel DCAF1 Ligand Using a Drug-Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets.

Journal of chemical information and modeling Jun 23, 2023
DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4 and the HECT family EDVP E3 ubiquitin ligases. The WDR domain of DCAF1 serves as a binding platform for substrate proteins and is also targeted by HIV and SIV lentiviral adapto...
Artificial Intelligence Machine Learning Carrier Proteins Ligands Ubiquitin-Protein Ligases
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Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges.

Journal of chemical information and modeling Jun 20, 2023
The Quantum Theory of Atoms in Molecules (QTAIM) provides an intuitive, yet physically sound, strategy to determine the partial charges of any chemical system relying on the topology induced by the electron density ρ() . In a previous work [ , , 0141...
Machine Learning Software Quantum Theory
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Characterizing Uncertainty in Machine Learning for Chemistry.

Journal of chemical information and modeling Jun 20, 2023
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the d...
Machine Learning Reproducibility of Results Uncertainty
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