AIMC Journal:
Journal of chemical information and modeling

Showing 441 to 450 of 934 articles

Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Journal of chemical information and modeling Apr 19, 2023
Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein...
Neural Networks, Computer Drug Discovery Proteins Cheminformatics
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Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Journal of chemical information and modeling Apr 14, 2023
Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as...
Deep Learning Proteins Molecular Dynamics Simulation Catalytic Domain Allosteric Site Allosteric Regulation
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Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces.

Journal of chemical information and modeling Apr 12, 2023
This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest ...
Neural Networks, Computer Computer Simulation Learning
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The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking.

Journal of chemical information and modeling Apr 10, 2023
Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to ...
Machine Learning Databases, Factual Molecular Docking Simulation Drug Discovery Supervised Machine Learning Ligands
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Serverless Prediction of Peptide Properties with Recurrent Neural Networks.

Journal of chemical information and modeling Apr 3, 2023
We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solu...
Neural Networks, Computer Machine Learning Reproducibility of Results Peptides Cloud Computing
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HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Mar 29, 2023
Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advanc...
Neural Networks, Computer Software Protein Binding Proteins Ligands
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MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction.

Journal of chemical information and modeling Mar 27, 2023
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from availa...
Artificial Intelligence Machine Learning Software
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AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models.

Journal of chemical information and modeling Mar 23, 2023
We introduce the AiZynthTrain Python package for training synthesis models in a robust, reproducible, and extensible way. It contains two pipelines that create a template-based one-step retrosynthesis model and a RingBreaker model that can be straigh...
Machine Learning Software
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G2GT: Retrosynthesis Prediction with Graph-to-Graph Attention Neural Network and Self-Training.

Journal of chemical information and modeling Mar 22, 2023
Retrosynthesis prediction, the task of identifying reactant molecules that can be used to synthesize product molecules, is a fundamental challenge in organic chemistry and related fields. To address this challenge, we propose a novel graph-to-graph t...
Neural Networks, Computer Algorithms Learning Electric Power Supplies
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Using Machine Learning To Predict Partition Coefficient (Log ) and Distribution Coefficient (Log ) with Molecular Descriptors and Liquid Chromatography Retention Time.

Journal of chemical information and modeling Mar 16, 2023
During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log , and d...
Machine Learning Water Chromatography, Liquid Chromatography, High Pressure Liquid Octanols
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