AIMC Topic: Drug Discovery

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Is Multitask Deep Learning Practical for Pharma?

Journal of chemical information and modeling Aug 1, 2017
Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack ...
Machine Learning Serine Proteinase Inhibitors Software Protein Kinase Inhibitors Drug Discovery Absorption, Radiation Ultraviolet Rays Inhibitory Concentration 50
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Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

Journal of chemical information and modeling Jul 10, 2017
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with differ...
Humans Transcription Factors Protein Conformation Cell Line Machine Learning Cell Cycle Proteins Drug Evaluation, Preclinical Drug Discovery Structure-Activity Relationship Molecular Docking Simulation Nuclear Proteins Molecular Targeted Therapy
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Predicting anatomic therapeutic chemical classification codes using tiered learning.

BMC bioinformatics Jun 7, 2017
BACKGROUND: The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed...
Machine Learning Algorithms Computational Biology Drug Evaluation, Preclinical Reproducibility of Results Drug Discovery
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Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

Scientific reports May 25, 2017
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property play...
Quantitative Structure-Activity Relationship Intestinal Absorption Algorithms Blood-Brain Barrier Models, Molecular Chemical Phenomena Drug Discovery Machine Learning Software Humans
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Chemical Space Mimicry for Drug Discovery.

Journal of chemical information and modeling Apr 3, 2017
We describe a new library generation method, Machine-based Identification of Molecules Inside Characterized Space (MIMICS), that generates sets of molecules inspired by a text-based input. MIMICS-generated libraries were found to preserve distributio...
Vascular Endothelial Growth Factor Receptor-2 Neural Networks, Computer Drug Discovery
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DrugClust: A machine learning approach for drugs side effects prediction.

Computational biology and chemistry Mar 30, 2017
BACKGROUND: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predi...
Drug-Related Side Effects and Adverse Reactions Machine Learning Drug Discovery
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Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome.

Artificial intelligence in medicine Mar 22, 2017
Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks r...
Drug Discovery Liver Neoplasms Databases, Factual Humans Drug Repositioning Carcinoma, Hepatocellular Breast Neoplasms Neural Networks, Computer Molecular Targeted Therapy
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Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

Journal of chemical information and modeling Mar 17, 2017
The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries...
Temperature Drug Discovery Models, Theoretical Bayes Theorem ROC Curve Machine Learning Quantitative Structure-Activity Relationship Malaria Antimalarials
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CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.

CPT: pharmacometrics & systems pharmacology Mar 13, 2017
Despite the existence of various databases cataloging cancer drugs, there is an emerging need to support the development and application of personalized therapies, where an integrated understanding of the clinical factors and drug mechanism of action...
Humans Clinical Trials as Topic Antineoplastic Agents Databases, Factual Biomedical Research Knowledge Bases Precision Medicine Neoplasms Drug Discovery
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Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework.

Journal of chemical information and modeling Mar 9, 2017
Effective prioritization of chemical compounds that show promising bioactivities from compound screenings represents a first critical step toward identifying successful drug candidates. Current development on computational approaches for compound pri...
Drug Discovery Time Factors Biological Assay Machine Learning Computer Simulation
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