AIMC Topic: Databases, Chemical

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A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.

Molecular informatics Feb 11, 2020
Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identify new therapeutic profiles for various diseases. However, the exponential growth in the genomic and...
Drug Development Databases, Protein Receptors, G-Protein-Coupled Machine Learning Computer Simulation Algorithms Drug Discovery Receptors, Cytoplasmic and Nuclear Enzymes Ion Channels Pharmaceutical Preparations Amino Acid Sequence Databases, Chemical
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DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening.

Genomics, proteomics & bioinformatics Feb 6, 2020
Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-base...
Pharmaceutical Preparations Databases, Chemical ROC Curve Deep Learning Proteins User-Computer Interface Area Under Curve
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Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem.

Molecules (Basel, Switzerland) Jan 23, 2020
Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming and studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are approaches, which present a cost-efficient...
Databases, Chemical Quantitative Structure-Activity Relationship Chemical and Drug Induced Liver Injury Databases, Pharmaceutical Computer Simulation Neural Networks, Computer Liver Algorithms Drug-Related Side Effects and Adverse Reactions
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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...
Drug Discovery Protein Binding Deep Learning Humans Databases, Chemical Molecular Docking Simulation Small Molecule Libraries
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Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

ACS chemical neuroscience Nov 4, 2019
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like ...
Big Data Protein Binding Signal Transduction Male Middle Aged Guanosine 5'-O-(3-Thiotriphosphate) Serotonin Agents Sulfur Radioisotopes Receptors, G-Protein-Coupled Databases, Chemical Cerebral Cortex Humans Machine Learning Female Aged Adult
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CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association Oct 25, 2019
Determining chemical carcinogenicity in the early stages of drug discovery is fundamentally important to prevent the adverse effect of carcinogens on human health. There has been a recent surge of interest in developing computational approaches to pr...
Animals Deep Learning Rats Databases, Chemical Carcinogens
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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Journal of chemical information and modeling Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
Ligands Databases, Chemical Protein Conformation Protein Binding Binding Sites Drug Development Neural Networks, Computer Proteins Drug Discovery
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Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature.

Journal of chemical information and modeling Sep 26, 2019
Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables d...
Chemistry, Pharmaceutical Molecular Structure Models, Chemical Databases, Chemical Machine Learning
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Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes.

Journal of medicinal chemistry Aug 30, 2019
Noncovalent inhibitors of protein kinases have different modes of action. They bind to the active or inactive form of kinases, compete with ATP, stabilize inactive kinase conformations, or act through allosteric sites. Accordingly, kinase inhibitors ...
Crystallography, X-Ray Protein Kinase Inhibitors Machine Learning Datasets as Topic Protein Binding Databases, Chemical Protein Kinases
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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Journal of medicinal chemistry Aug 27, 2019
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
Proof of Concept Study Drug Discovery Deep Learning Solubility Organic Chemicals Datasets as Topic Models, Molecular Databases, Chemical
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