AIMC Topic: Molecular Structure

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Coverage bias in small molecule machine learning.

Nature communications Jan 9, 2025
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end mo...
Molecular Structure Ligands Machine Learning Small Molecule Libraries Algorithms
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Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.

European journal of medicinal chemistry Dec 10, 2024
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...
Molecular Structure Drug Evaluation, Preclinical Ligands Machine Learning Drug Design Small Molecule Libraries Humans Artificial Intelligence
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MPCD: A Multitask Graph Transformer for Molecular Property Prediction by Integrating Common and Domain Knowledge.

Journal of medicinal chemistry Dec 2, 2024
Molecular property prediction with deep learning often employs self-supervised learning techniques to learn common knowledge through masked atom prediction. However, the common knowledge gained by masked atom prediction dramatically differs from the ...
Molecular Structure Chemical Phenomena Deep Learning
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Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.

Bioorganic chemistry Oct 28, 2024
The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literatu...
Molecular Structure Ligands Cheminformatics Machine Learning Drug Discovery Proteins Humans Artificial Intelligence
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Drugs from drugs: New chemical insights into a mature concept.

Drug discovery today Oct 22, 2024
Developing new drugs from marketed ones is a well-established and successful approach in drug discovery. We offer a unified view of this field, focusing on the new chemical aspects of the involved approaches: (a) chemical transformation of the origin...
Humans Prodrugs Drug Repositioning Molecular Structure Pharmaceutical Preparations Artificial Intelligence Drug Discovery
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Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors.

Computational biology and chemistry Oct 10, 2024
Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selectiv...
Sirtuin 2 Quantitative Structure-Activity Relationship Niacinamide Molecular Structure Enzyme Inhibitors Humans Molecular Docking Simulation Drug Design Machine Learning
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Wee1 inhibitor optimization through deep-learning-driven decision making.

European journal of medicinal chemistry Sep 29, 2024
Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, w...
Cell Line, Tumor Protein Kinase Inhibitors Cell Cycle Proteins Cell Proliferation Dose-Response Relationship, Drug Antineoplastic Agents Deep Learning Drug Screening Assays, Antitumor Humans Structure-Activity Relationship Protein-Tyrosine Kinases Molecular Structure Decision Making
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Recursive dynamics of GspE through machine learning enabled identification of inhibitors.

Computational biology and chemistry Sep 28, 2024
Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including Achromobacter xylosoxidans. ATPase GspE is the powerhouse of the T2SS. It powers the entire secretion process by binding with...
Molecular Structure Molecular Docking Simulation Molecular Dynamics Simulation Machine Learning Microbial Sensitivity Tests Enzyme Inhibitors Adenosine Triphosphatases Anti-Bacterial Agents
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Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Journal of computational chemistry Sep 26, 2024
Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking...
Protein Binding Molecular Structure Ligands Enzyme Inhibitors Molecular Dynamics Simulation Oxidoreductases Acting on CH-CH Group Donors Humans Binding Sites Machine Learning Dihydroorotate Dehydrogenase Molecular Docking Simulation
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Environmentally Sustainable Detection of Arsenic using Convolutional Neural Networks and Imidazole-Based Organic Probes: Application in Food Samples and Arsenic Album.

Chemical research in toxicology Sep 12, 2024
Arsenic contamination poses a significant health risk, particularly when it infiltrates water supplies. While current detection methods offer precise analysis, they often involve complex instrumentation not suitable for field use. This study presents...
Food Contamination Food Analysis Molecular Structure Imidazoles Arsenic Neural Networks, Computer Colorimetry
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