AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 41 to 50 of 934 articles

Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation.

Journal of chemical information and modeling Apr 14, 2025
The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a signifi...
Models, Molecular Semantics Deep Learning Neural Networks, Computer Crystallography, X-Ray
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Heat Capacity of Ionic Liquids: Toward Interpretable Chemical Structure-Based Machine Learning Approaches.

Journal of chemical information and modeling Apr 10, 2025
This study focuses on predicting the heat capacity of pure liquid-phase ionic liquids (ILs) using machine learning models from various categories, including support vector machines, instance-based learning, ensemble learning, and neural networks, wit...
Hot Temperature Machine Learning Ionic Liquids Neural Networks, Computer
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PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery.

Journal of chemical information and modeling Apr 9, 2025
The advent of powerful machine learning algorithms as well as the availability of high volume of pharmacological data has given new fuel to QSAR, opening new unprecedented options for deriving highly predictive models for assisting the rationale desi...
Drug Discovery Quantitative Structure-Activity Relationship Machine Learning Software
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Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.

Journal of chemical information and modeling Apr 8, 2025
Deep learning has revolutionized difficult tasks in chemistry and biology, yet existing language models often treat these domains separately, relying on concatenated architectures and independently pretrained weights. These approaches fail to fully e...
Drug Discovery Models, Molecular Computational Biology Deep Learning Proteins
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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.

Journal of chemical information and modeling Apr 8, 2025
Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we ...
Thermodynamics Molecular Dynamics Simulation Neural Networks, Computer Protein Binding Proteins Ligands Quantum Theory
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HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.

Journal of chemical information and modeling Apr 4, 2025
Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately predicting...
Thermodynamics Graph Neural Networks Hydrolysis Hydrogen-Ion Concentration Neural Networks, Computer
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Application of Deep Learning to Predict the Persistence, Bioaccumulation, and Toxicity of Pharmaceuticals.

Journal of chemical information and modeling Apr 3, 2025
This study investigates the application of a deep learning (DL) model, specifically a message-passing neural network (MPNN) implemented through Chemprop, to predict the persistence, bioaccumulation, and toxicity (PBT) characteristics of compounds, wi...
Drug Discovery Deep Learning Neural Networks, Computer Humans Pharmaceutical Preparations
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A Specialized and Enhanced Deep Generation Model for Active Molecular Design Targeting Kinases Guided by Affinity Prediction Models and Reinforcement Learning.

Journal of chemical information and modeling Apr 2, 2025
Kinases are critical regulators in numerous cellular processes, and their dysregulation is linked to various diseases, including cancer. Thus, protein kinases have emerged as major drug targets at present, with approximately a quarter to a third of g...
Protein Kinases Models, Molecular Protein Kinase Inhibitors Drug Design Deep Learning
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Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Journal of chemical information and modeling Apr 1, 2025
Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthost...
Deep Learning Ligands Drug Design Molecular Docking Simulation Protein Kinase Inhibitors Allosteric Regulation Protein Conformation Cyclin-Dependent Kinase 2
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Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks.

Journal of chemical information and modeling Mar 31, 2025
Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture ric...
Pharmaceutical Preparations Graph Neural Networks Drug Discovery Neural Networks, Computer Proteins
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