AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 81 to 90 of 934 articles

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches.

Journal of chemical information and modeling Feb 27, 2025
Nitroaromatic compounds (NAs) are widely used in industrial applications but pose significant genotoxic risks, necessitating accurate mutagenicity prediction for chemical safety assessments. This study integrates conceptual density functional theory ...
Quantitative Structure-Activity Relationship Machine Learning Density Functional Theory Nitro Compounds Mutagenicity Tests Mutagens
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GLMCyp: A Deep Learning-Based Method for CYP450-Mediated Reaction Site Prediction.

Journal of chemical information and modeling Feb 27, 2025
Cytochrome P450 enzymes (CYP450s) play crucial roles in metabolizing many drugs, and thus, local chemical structure can profoundly influence drug efficacy and toxicity. Therefore, the accurate prediction of CYP450-mediated reaction sites can increase...
Drug Discovery Cytochrome P-450 Enzyme System Deep Learning Humans
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Large Model Era: Deep Learning in Osteoporosis Drug Discovery.

Journal of chemical information and modeling Feb 26, 2025
Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in a decline in patients' life quality. In response to the increased incidence of osteoporosis, related dr...
Deep Learning Osteoporosis Drug Discovery Humans
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SynthMol: A Drug Safety Prediction Framework Integrating Graph Attention and Molecular Descriptors into Pre-Trained Geometric Models.

Journal of chemical information and modeling Feb 25, 2025
Drug safety is affected by multiple molecular properties and safety assessment is critical for clinical application. Evaluating a drug candidate's therapeutic potential is facilitated by machine learning models trained on extensive compound bioactivi...
Deep Learning Drug Development
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Natural Language Processing Methods for the Study of Protein-Ligand Interactions.

Journal of chemical information and modeling Feb 24, 2025
Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and developmen...
Humans Drug Discovery Machine Learning Proteins Ligands Protein Binding Natural Language Processing
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Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.

Journal of chemical information and modeling Feb 24, 2025
Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for ...
Reward Deep Learning Artificial Intelligence Drug Discovery Machine Learning
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Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

Journal of chemical information and modeling Feb 23, 2025
While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine le...
Bacteria Small Molecule Libraries Microbial Sensitivity Tests Anti-Bacterial Agents Drug Discovery Anti-Infective Agents Machine Learning
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Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Journal of chemical information and modeling Feb 21, 2025
Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsical...
Peptide Fragments Molecular Dynamics Simulation Amyloid beta-Peptides Deep Learning Protein Conformation Intrinsically Disordered Proteins
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Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.

Journal of chemical information and modeling Feb 21, 2025
Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregat...
Hydrophobic and Hydrophilic Interactions Molecular Docking Simulation Surface Properties Neural Networks, Computer Protein Binding Binding Sites Proteins ROC Curve
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EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations.

Journal of chemical information and modeling Feb 21, 2025
Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine lea...
Computational Biology Enzymes Machine Learning
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