AIMC Topic: Cheminformatics

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Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.

International journal of molecular sciences Mar 3, 2022
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent succe...
Drug Design Unsupervised Machine Learning Cheminformatics Humans Neural Networks, Computer Algorithms
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Chemical Reactivity Prediction: Current Methods and Different Application Areas.

Molecular informatics Jan 22, 2022
The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potentia...
Drug Design Drug Discovery Cheminformatics Machine Learning Algorithms
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Unsupervised Representation Learning for Proteochemometric Modeling.

International journal of molecular sciences Nov 28, 2021
In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection a...
Ligands Proteins Quantitative Structure-Activity Relationship Unsupervised Machine Learning Cheminformatics
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Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

Journal of chemical information and modeling Nov 4, 2021
The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural netw...
Neural Networks, Computer Machine Learning 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...
Cheminformatics Deep Learning Drug Development Structure-Activity Relationship Models, Molecular Molecular Structure Bayes Theorem Machine Learning Algorithms Drug Design Humans Artificial Intelligence
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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...
Machine Learning Algorithms Reproducibility of Results Humans Models, Molecular Drug-Related Side Effects and Adverse Reactions Databases, Chemical Drug Discovery Deep Learning ROC Curve Cheminformatics Blood Cells Quantitative Structure-Activity Relationship
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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...
Cheminformatics Humans Databases, Chemical Machine Learning Quantitative Structure-Activity Relationship Algorithms Diacylglycerol O-Acyltransferase ROC Curve Molecular Dynamics Simulation Models, Molecular Enzyme Inhibitors Reproducibility of Results Molecular Structure Molecular Docking Simulation Drug Discovery
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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...
Humans Machine Learning Algorithms Molecular Docking Simulation Drug Discovery Workflow Structure-Activity Relationship Molecular Dynamics Simulation Area Under Curve Acetylcholinesterase Cholinesterase Inhibitors Models, Molecular ROC Curve Cheminformatics Biological Products 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...
Software Cheminformatics Learning Curve Temperature Cluster Analysis Deep Learning Clozapine Floxacillin
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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 ...
Deep Learning Humans Algorithms Quantitative Structure-Activity Relationship Cheminformatics
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