AIMC Topic: Cheminformatics

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AI in drug development: a multidisciplinary perspective.

Molecular diversity Jul 12, 2021
The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this...
Deep Learning Drug Development Structure-Activity Relationship Bayes Theorem Cheminformatics Humans Artificial Intelligence Machine Learning Models, Molecular Algorithms Molecular Structure Drug Design
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In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Molecular diversity Jul 1, 2021
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focu...
Reproducibility of Results Drug Discovery Databases, Chemical ROC Curve Deep Learning Quantitative Structure-Activity Relationship Humans Blood Cells Models, Molecular Cheminformatics Machine Learning Drug-Related Side Effects and Adverse Reactions Algorithms
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Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

Molecular diversity Jun 23, 2021
DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discov...
Databases, Chemical Quantitative Structure-Activity Relationship Diacylglycerol O-Acyltransferase Cheminformatics Humans Reproducibility of Results Molecular Docking Simulation Machine Learning Molecular Structure Drug Discovery Algorithms Molecular Dynamics Simulation ROC Curve Enzyme Inhibitors Models, Molecular
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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity Jun 16, 2021
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...
ROC Curve Biological Products Structure-Activity Relationship Algorithms Molecular Docking Simulation Drug Discovery Molecular Dynamics Simulation Acetylcholinesterase Cholinesterase Inhibitors Cheminformatics Area Under Curve Models, Molecular Workflow Humans Machine Learning Databases, Pharmaceutical
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FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space.

Molecules (Basel, Switzerland) Apr 3, 2021
The question of molecular similarity is core in cheminformatics and is usually assessed via a comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical spa...
Cheminformatics Learning Curve Cluster Analysis Software Clozapine Temperature Floxacillin Deep Learning
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SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning.

Journal of chemical information and modeling Mar 14, 2021
Simplified molecular input line entry system (SMILES)-based deep learning models are slowly emerging as an important research topic in cheminformatics. In this study, we introduce SMILES pair encoding (SPE), a data-driven tokenization algorithm. SPE ...
Humans Quantitative Structure-Activity Relationship Cheminformatics Algorithms Deep Learning
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Discrimination of white wine ageing based on untarget peak picking approach with multi-class target coupled with machine learning algorithms.

Food chemistry Feb 16, 2021
The complexity of the chemical reactions occurring during white wine storage, such as oxidation turns the capacity of prediction and consequently the capacity to avoid it extremely difficult. This study proposes an untarget methodology based on machi...
Time Factors Oxidation-Reduction Wine Machine Learning Esters Cheminformatics
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Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Molecular diversity Feb 3, 2021
Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 re...
Leukotriene Antagonists Receptors, Leukotriene Quantitative Structure-Activity Relationship Binding Sites Cheminformatics Protein Binding Algorithms Models, Molecular Machine Learning Drug Discovery Reproducibility of Results Molecular Structure ROC Curve
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Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks.

Molecules (Basel, Switzerland) Dec 29, 2020
Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is us...
Pharmaceutical Preparations Cheminformatics Databases, Pharmaceutical Neural Networks, Computer Drug Design Deep Learning Drug Discovery Principal Component Analysis Bayes Theorem
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Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge.

Journal of chemical information and modeling Dec 16, 2020
Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be p...
Electrons Cheminformatics Neural Networks, Computer Machine Learning Molecular Dynamics Simulation
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