AIMC Topic: Drug Discovery

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Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Cell chemical biology Dec 21, 2017
Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity d...
Knowledge Bases Pharmaceutical Preparations Drug Interactions Humans Consensus Drug Repositioning Drug Discovery
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Differential Compound Prioritization via Bidirectional Selectivity Push with Power.

Journal of chemical information and modeling Dec 13, 2017
Effective in silico compound prioritization is a critical step to identify promising drug candidates in the early stages of drug discovery. Current computational methods for compound prioritization usually focus on ranking the compounds based on one ...
Humans Machine Learning Drug Discovery Structure-Activity Relationship Models, Biological Computer Simulation Animals
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Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Journal of chemical information and modeling Nov 27, 2017
Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with ma...
Ligands Structure-Activity Relationship Models, Biological Computer Simulation Drug Discovery Humans Machine Learning
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Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms.

Molecular informatics Nov 14, 2017
This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivari...
Antiprotozoal Agents Drug Discovery Quantitative Structure-Activity Relationship Trypanosoma brucei brucei Machine Learning Thiazolidines
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Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Molecular pharmaceutics Nov 13, 2017
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of t...
Drug Discovery Bayes Theorem Datasets as Topic Neural Networks, Computer Machine Learning
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Drug target ontology to classify and integrate drug discovery data.

Journal of biomedical semantics Nov 9, 2017
BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research...
Semantics Biological Ontologies Computational Biology Proteins Humans Drug Delivery Systems Drug Discovery Software
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Generative Recurrent Networks for De Novo Drug Design.

Molecular informatics Nov 2, 2017
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a...
Quantitative Structure-Activity Relationship Drug Discovery Neural Networks, Computer Models, Chemical Drug Design
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ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

Molecular pharmaceutics Oct 27, 2017
Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common...
Quantitative Structure-Activity Relationship Machine Learning Drug Discovery Algorithms Support Vector Machine
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The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury.

Expert review of gastroenterology & hepatology Oct 9, 2017
Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can b...
Humans Risk Assessment Drug Discovery Severity of Illness Index Molecular Structure Databases, Factual Pharmaceutical Preparations Animals Toxicology Chemical and Drug Induced Liver Injury Liver Knowledge Bases Incidence Toxicogenetics Structure-Activity Relationship Hep G2 Cells
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From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

Drug discovery today Sep 4, 2017
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional...
Drug Discovery Machine Learning Humans Quantitative Structure-Activity Relationship Models, Theoretical Artificial Intelligence Drug Design
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