AIMC Topic: Molecular Structure

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Quantitative structure retention relationship (QSRR) modelling for Analytes' retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Jan 19, 2022
In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived re...
Algorithms Bayes Theorem Organic Chemicals Linear Models Mass Spectrometry Databases, Chemical Metabolomics Machine Learning Chromatography, Liquid Molecular Structure
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Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1.

Molecules (Basel, Switzerland) Dec 10, 2021
A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular finger...
Machine Learning Databases, Factual Molecular Structure Humans Histone Demethylases Drug Evaluation, Preclinical Enzyme Inhibitors Lysine
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Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets.

Molecules (Basel, Switzerland) Dec 7, 2021
The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form ...
Humans Neural Networks, Computer Amines Blood-Brain Barrier Molecular Structure Biological Transport Machine Learning Databases, Factual
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Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention.

Journal of chemical information and modeling Dec 2, 2021
Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding o...
Semantics Humans Neural Networks, Computer Machine Learning Language Molecular Structure
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A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Bioorganic & medicinal chemistry Nov 27, 2021
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully trans...
Molecular Structure Tumor Suppressor Protein p53 Dihydroergotoxine Drug Screening Assays, Antitumor Structure-Activity Relationship Antineoplastic Agents Colorectal Neoplasms Cell Line, Tumor Molecular Docking Simulation Cell Proliferation Humans Drug Discovery Dose-Response Relationship, Drug Machine Learning Cell Survival
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Charge-Transfer Landscape Manifesting the Structure-Rate Relationship in the Condensed Phase Machine Learning.

The journal of physical chemistry. B Nov 26, 2021
In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and re...
Solvents Entropy Molecular Structure Molecular Dynamics Simulation Machine Learning
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Discovery of Pyrazolo[3,4-]pyridazinone Derivatives as Selective DDR1 Inhibitors via Deep Learning Based Design, Synthesis, and Biological Evaluation.

Journal of medicinal chemistry Nov 25, 2021
Alterations of discoidin domain receptor1 (DDR1) may lead to increased production of inflammatory cytokines, making DDR1 an attractive target for inflammatory bowel disease (IBD) therapy. A scaffold-based molecular design workflow was established and...
Animals Drug Design Drug Discovery Deep Learning Mice Mice, Inbred C57BL Humans Pyridazines Mice, Inbred ICR Dextran Sulfate Anti-Inflammatory Agents Discoidin Domain Receptor 1 Colitis Protein Kinase Inhibitors Pyrazolones Drug Screening Assays, Antitumor Molecular Structure
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Machine learning and deep learning enabled fuel sooting tendency prediction from molecular structure.

Journal of molecular graphics & modelling Nov 22, 2021
Soot formation models become increasingly important in advanced renewable fuels formulation for soot reduction benefit. This work evaluates performance of machine learning (ML) and deep learning (DL) to predict yield sooting index (YSI) from chemical...
Molecular Structure Soot Neural Networks, Computer Machine Learning Deep Learning
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Predicting biochemical and physiological effects of natural products from molecular structures using machine learning.

Natural product reports Nov 17, 2021
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which...
Drug Interactions Machine Learning Biological Products Molecular Structure
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Improvement of the Force Field for -d-Glucose with Machine Learning.

Molecules (Basel, Switzerland) Nov 5, 2021
While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse co...
Molecular Conformation Static Electricity Machine Learning Algorithms Models, Theoretical Molecular Structure Glucose Molecular Dynamics Simulation
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