AIMC Topic: Drug Discovery

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MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development.

PloS one Apr 30, 2015
Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine lea...
Drug Discovery Cluster Analysis Machine Learning Algorithms
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Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

BMC bioinformatics Apr 17, 2015
BACKGROUND: Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when boun...
Ligands Binding Sites Artificial Intelligence Software Drug Design Drug Discovery Protein Binding Proteins Humans
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In silico prediction of anti-malarial hit molecules based on machine learning methods.

International journal of computational biology and drug design Apr 13, 2015
Machine learning techniques have been widely used in drug discovery and development in the areas of cheminformatics. Aspartyl aminopeptidase (M18AAP) of Plasmodium falciparum is crucial for survival of malaria parasite. We have created predictive mod...
Artificial Intelligence Computer Simulation Drug Discovery Models, Statistical Models, Molecular Antimalarials
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Machine learning assisted design of highly active peptides for drug discovery.

PLoS computational biology Apr 7, 2015
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...
Machine Learning Pattern Recognition, Automated Bacterial Physiological Phenomena Protein Interaction Mapping Antimicrobial Cationic Peptides Molecular Sequence Data Peptides Structure-Activity Relationship Sequence Analysis, Protein Drug Discovery Amino Acid Sequence
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Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

Computational biology and chemistry Mar 23, 2015
Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (I...
Protein Interaction Maps Computational Biology Support Vector Machine Humans Drug Discovery Gene Ontology Protein Interaction Mapping Proteins Databases, Protein Molecular Targeted Therapy
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Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature.

BMC bioinformatics Mar 18, 2015
BACKGROUND: Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discovery and drug repositioning. However, a comprehensive drug-SE association knowledge base does not exi...
Knowledge Bases Data Mining Biological Ontologies MEDLINE Search Engine Databases, Pharmaceutical Pharmaceutical Preparations Periodicals as Topic Drug-Related Side Effects and Adverse Reactions Drug Discovery Drug Repositioning Humans Artificial Intelligence Phenotype
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Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis.

PloS one Mar 5, 2015
Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and ...
Artifacts Support Vector Machine Humans Structure-Activity Relationship Drug Discovery Drug Evaluation, Preclinical
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A new modeling approach for quantifying expert opinion in the drug discovery process.

Statistics in medicine Feb 23, 2015
Expert opinion plays an important role when choosing clusters of chemical compounds for further investigation. Often, the process by which the clusters are assigned to the experts for evaluation, the so-called selection process, and the qualitative r...
Humans Likelihood Functions Drug Discovery Computer Simulation Expert Systems Cluster Analysis Drug Industry Models, Statistical
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Deep neural nets as a method for quantitative structure-activity relationships.

Journal of chemical information and modeling Feb 17, 2015
Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more r...
Algorithms Neural Networks, Computer Prospective Studies Machine Learning Drug Discovery Support Vector Machine Workflow Quantitative Structure-Activity Relationship
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Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity.

Bioorganic & medicinal chemistry Dec 20, 2014
A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this r...
Plasmodium falciparum Bayes Theorem Models, Biological Machine Learning Drug Discovery Small Molecule Libraries Databases, Pharmaceutical Hemeproteins Antimalarials Malaria, Falciparum Parasitic Sensitivity Tests Humans
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