AIMC Journal:
Journal of chemical information and modeling

Showing 221 to 230 of 934 articles

Spatially Resolved Uncertainties for Machine Learning Potentials.

Journal of chemical information and modeling Aug 7, 2024
Machine learning potentials have become an essential tool for atomistic simulations, yielding results close to ab initio simulations at a fraction of computational cost. With recent improvements on the achievable accuracies, the focus has now shifted...
Molecular Dynamics Simulation Uncertainty Water Machine Learning
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Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.

Journal of chemical information and modeling Aug 7, 2024
Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders...
Temperature Bayes Theorem Algorithms Machine Learning Magnets
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Identification of Active Molecules against Thrombocytopenia through Machine Learning.

Journal of chemical information and modeling Aug 7, 2024
Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening complications. Discovering new therapeutic agents against thrombocytopenia has proven to be a challen...
Megakaryocytes Thrombocytopenia Humans Machine Learning Computer Simulation Algorithms Drug Discovery Blood Platelets
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Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Journal of chemical information and modeling Aug 1, 2024
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited scr...
Machine Learning Probability Protein Engineering Models, Molecular Proteins
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Artificial Intelligence Must Be Made More Scientific.

Journal of chemical information and modeling Jul 27, 2024
The role of AI within science is growing. Here we assess its impact on research and argue that AI often lacks reproducibility, transparency, objectivity, and mechanistic understanding. To ensure AI benefits research, we need to develop forms of AI th...
Artificial Intelligence Humans Reproducibility of Results
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A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties.

Journal of chemical information and modeling Jul 25, 2024
Machine learning plays a role in accelerating drug discovery, and the design of effective machine learning models is crucial for accurately predicting molecular properties. Characterizing molecules typically involves the use of molecular fingerprints...
Drug Discovery Cell Line, Tumor Algorithms Humans Machine Learning Neural Networks, Computer
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Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design.

Journal of chemical information and modeling Jul 24, 2024
Computational molecular generation methods that generate chemical structures from gene expression profiles have been actively developed for de novo drug design. However, most omics-based methods involve complex models consisting of multiple neural ne...
Transcription, Genetic Drug Design Gene Expression Profiling Ligands Neural Networks, Computer Humans
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EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.

Journal of chemical information and modeling Jul 22, 2024
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enha...
Models, Molecular Protein Engineering Mutation Enzymes Deep Learning
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HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery.

Journal of chemical information and modeling Jul 22, 2024
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactio...
Deep Learning Ligands Drug Discovery Proteins Protein Binding Neural Networks, Computer Drug Design
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Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks.

Journal of chemical information and modeling Jul 20, 2024
Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost th...
Fungicides, Industrial Drug Synergism Neural Networks, Computer Deep Learning
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