AIMC Topic: Drug Discovery

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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...
Computer Simulation Drug Discovery Structure-Activity Relationship Humans Machine Learning Models, Biological Ligands
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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 Quantitative Structure-Activity Relationship Trypanosoma brucei brucei Drug Discovery Thiazolidines Machine Learning
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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...
Drug Discovery Computational Biology Humans Semantics Biological Ontologies Proteins Software Drug Delivery Systems
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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...
Drug Discovery Neural Networks, Computer Drug Design Models, Chemical Quantitative Structure-Activity Relationship
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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...
Machine Learning Algorithms Quantitative Structure-Activity Relationship Drug Discovery 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...
Knowledge Bases Toxicology Toxicogenetics Chemical and Drug Induced Liver Injury Structure-Activity Relationship Molecular Structure Pharmaceutical Preparations Hep G2 Cells Liver Animals Risk Assessment Severity of Illness Index Incidence Humans Drug Discovery Databases, Factual
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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 Models, Theoretical Artificial Intelligence Quantitative Structure-Activity Relationship Humans Machine Learning Drug Design
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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 Absorption, Radiation Software Inhibitory Concentration 50 Serine Proteinase Inhibitors Drug Discovery Protein Kinase Inhibitors Ultraviolet Rays
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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...
Cell Cycle Proteins Molecular Docking Simulation Nuclear Proteins Structure-Activity Relationship Transcription Factors Cell Line Drug Discovery Molecular Targeted Therapy Humans Drug Evaluation, Preclinical Protein Conformation Machine Learning
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