AIMC Topic: Drug Discovery

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Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Molecules (Basel, Switzerland) Jul 26, 2019
Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are...
Models, Theoretical Machine Learning Decision Support Techniques Decision Trees Reproducibility of Results Algorithms Databases, Chemical Drug Discovery Quantitative Structure-Activity Relationship Computational Biology Ligands Chemical Phenomena
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Learning-to-rank technique based on ignoring meaningless ranking orders between compounds.

Journal of molecular graphics & modelling Jul 24, 2019
Learning-to-rank, which is a machine-learning technique for information retrieval, was recently introduced to ligand-based virtual screening to reduce the costs of developing a new drug. The quality of a rank prediction model depends on experimental ...
Models, Theoretical Algorithms Drug Discovery Humans Machine Learning
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Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.

International journal of molecular sciences Jul 22, 2019
Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain popul...
Anti-HIV Agents Antiviral Agents Humans Models, Molecular Molecular Structure Quantitative Structure-Activity Relationship Protein Binding Databases, Pharmaceutical Drug Discovery HIV Infections Polypharmacology Machine Learning Hepatitis C Algorithms HIV-1 Bayes Theorem Molecular Conformation Hepacivirus
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The Art of Virtualizing Pharma R&D.

Drug discovery today Jul 16, 2019
Drug Discovery Drug Industry Artificial Intelligence Software
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Advancing Drug Discovery via Artificial Intelligence.

Trends in pharmacological sciences Jul 15, 2019
Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process which typically costs 2....
Quantitative Structure-Activity Relationship Animals Drug Discovery Translational Research, Biomedical Pharmacology Pharmaceutical Preparations Humans Artificial Intelligence
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.

Chemical reviews Jul 11, 2019
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be ...
Artificial Intelligence Machine Learning Algorithms Neural Networks, Computer Drug Design Bayes Theorem Drug Discovery Small Molecule Libraries
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Improved Prediction of Drug-Target Interactions Using Self-Paced Learning with Collaborative Matrix Factorization.

Journal of chemical information and modeling Jul 11, 2019
Identifying drug-target interactions (DTIs) plays an important role in the field of drug discovery, drug side-effects, and drug repositioning. However, in vivo or biochemical experimental methods for identifying new DTIs are extremely expensive and t...
Computational Biology Drug Repositioning Drug-Related Side Effects and Adverse Reactions Machine Learning Drug Discovery
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Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

International journal of molecular sciences Jul 10, 2019
Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Followin...
Software Reproducibility of Results Drug Discovery Neural Networks, Computer Deep Learning
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Neural networks in drug discovery: current insights from medicinal chemists.

Future medicinal chemistry Jul 9, 2019
Humans Neural Networks, Computer Drug Discovery
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Deep Reinforcement Learning for Multiparameter Optimization in Drug Design.

Journal of chemical information and modeling Jul 5, 2019
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods f...
Neural Networks, Computer Models, Theoretical Machine Learning Models, Molecular Drug Design Molecular Structure Drug Discovery
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