Small Molecule Libraries

In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning.

Genes Nov 7, 2019

In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the...

Humans Protein Binding Databases, Chemical Small Molecule Libraries Molecular Docking Simulation Drug Discovery Deep Learning

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Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Journal of chemical information and modeling Oct 31, 2019

Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the tra...

Humans Machine Learning Molecular Docking Simulation Proteins Databases, Protein Ligands Drug Design Water Protein Binding Molecular Conformation Small Molecule Libraries

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Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Journal of computer-aided molecular design Oct 18, 2019

In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibito...

Small Molecule Libraries Software Sequence Homology Drug Design Humans Amino Acid Sequence Protein Binding Computational Biology Artificial Intelligence Proteins Ligands Machine Learning Models, Molecular Protein Conformation

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Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

PloS one Oct 7, 2019

The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-act...

Cell Line Software Small Molecule Libraries Humans Animals Quantitative Structure-Activity Relationship Machine Learning Drug Discovery Drug Resistance

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Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

International journal of molecular sciences Sep 30, 2019

The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for...

Small Molecule Libraries Constitutive Androstane Receptor Quantitative Structure-Activity Relationship Receptors, Cytoplasmic and Nuclear Drug Discovery Deep Learning Algorithms Ligands Reproducibility of Results

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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019

Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...

Neural Networks, Computer Drug Design Drug Discovery Proteins Models, Chemical Ligands Humans Molecular Conformation Quantitative Structure-Activity Relationship Small Molecule Libraries

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Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation.

Journal of biomolecular structure & dynamics Sep 9, 2019

Histone Deacetylases (HDACs) play a significant role in the regulation of gene expression by modifying histones and non-histone substrates. Since they are key regulators in the reversible epigenetic mechanism, they are considered as promising drug ta...

Histone Deacetylase Inhibitors Small Molecule Libraries Histone Deacetylases Molecular Docking Simulation Support Vector Machine Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Histones

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Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists.

Journal of chemical information and modeling Sep 4, 2019

Small molecules targeting peripheral CB1 receptors have therapeutic potential in a variety of disorders including obesity-related, hormonal, and metabolic abnormalities, while avoiding the psychoactive effects in the central nervous system. We applie...

Ligands Machine Learning Small Molecule Libraries Computer Simulation Receptor, Cannabinoid, CB1 Computational Biology

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EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge.

Journal of chemical information and modeling Sep 3, 2019

Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and pharmaceutical companies because it reduces the cost and duration of the drug development pipeline an...

Databases, Pharmaceutical Knowledge Bases Data Mining Small Molecule Libraries Drug Design Drug Repositioning Humans Software Animals

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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

Journal of chemical information and modeling Aug 22, 2019

The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of comp...

Protein Kinase Inhibitors Humans Cyclin-Dependent Kinase 2 Drug Design Thermodynamics Drug Discovery Machine Learning Small Molecule Libraries Models, Molecular

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Explore the latest research on artificial intelligence and machine learning in medicine.

In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning.

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation.

Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists.

EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge.

Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

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