AIMC Topic: Drug Development

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Application of Machine Learning in Drug Development and Regulation: Current Status and Future Potential.

Clinical pharmacology and therapeutics Feb 8, 2020
Forecasting Drug Development Humans Drug and Narcotic Control Machine Learning
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Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.

BMC bioinformatics Feb 7, 2020
BACKGROUND: Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, lev...
Cluster Analysis Proteins Drug Discovery Drug Development Machine Learning Computer Simulation
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Scientific and Regulatory Considerations for an Ontogeny Knowledge Base for Pediatric Clinical Pharmacology.

Clinical pharmacology and therapeutics Jan 26, 2020
Understanding all aspects of developmental biology, or pediatric ontogeny, that affect drug therapy from the fetus to the adolescent child is the holy grail of pediatric scientists and clinical pharmacologists. The scientific community is now close t...
Drug Development Knowledge Bases Humans Child Child Development Pharmacology, Clinical
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Key indicators of phase transition for clinical trials through machine learning.

Drug discovery today Jan 8, 2020
A significant number of drugs fail during the clinical testing stage. To understand the attrition of drugs through the regulatory process, here we review and advance machine-learning (ML) and natural language-processing algorithms to investigate the ...
Drug Development Clinical Trials as Topic Humans Machine Learning
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Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration.

Small (Weinheim an der Bergstrasse, Germany) Nov 13, 2019
Cancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the complexity of the local milieu...
Models, Biological Microfluidics Neoplasms Drug Development Humans Artificial Intelligence Tumor Microenvironment
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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Journal of chemical information and modeling Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
Drug Discovery Protein Binding Drug Development Neural Networks, Computer Protein Conformation Ligands Proteins Binding Sites Databases, Chemical
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Does artificial intelligence have the potential to transform drug formulation development?

Journal of controlled release : official journal of the Controlled Release Society Oct 14, 2019
Artificial Intelligence Drug Industry Drug Development Drug Compounding
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LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets.

Journal of chemical information and modeling Oct 9, 2019
Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster speed and lower cost compared to experimental bioassays. Gradient boosting is an effect...
Mutagens Drug-Related Side Effects and Adverse Reactions Datasets as Topic Drug Development Molecular Structure Machine Learning Software Algorithms Reproducibility of Results
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Big Data and Artificial Intelligence Modeling for Drug Discovery.

Annual review of pharmacology and toxicology Sep 13, 2019
Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynami...
Deep Learning Big Data Animals Humans Data Mining Models, Theoretical Drug Discovery Artificial Intelligence Drug Development
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Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier.

Artificial intelligence in medicine Jul 19, 2019
Discovering and accurately locating drug targets is of great significance for the research and development of new drugs. As a different approach to traditional drug development, the machine learning algorithm is used to predict the drug target by min...
Proteins Molecular Targeted Therapy Drug Development Humans Support Vector Machine Algorithms
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