AIMC Topic: Drug Discovery

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Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data.

Molecules (Basel, Switzerland) Aug 29, 2020
Activity landscape (AL) models are used for visualizing and interpreting structure-activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different t...
Drug Discovery Structure-Activity Relationship Models, Molecular Machine Learning Drug Design
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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Nature communications Aug 21, 2020
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identif...
Drug Delivery Systems Drug Discovery Machine Learning Proteomics Proteome Protein Binding Computational Biology Humans Botrytis HeLa Cells Saccharomyces cerevisiae Phosphotransferases Proteolysis Binding Sites Mass Spectrometry Cell Survival Ligands
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Current methods and challenges for deep learning in drug discovery.

Drug discovery today. Technologies Aug 17, 2020
Driven by rapid advances in computer hardware and publicly available datasets over the past decade, deep learning has achieved tremendous success in the transformation of many computational disciplines. These novel technologies have had considerable ...
Deep Learning Drug Design Humans Drug Discovery
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Challenge-Enabled Machine Learning to Drug-Response Prediction.

The AAPS journal Aug 10, 2020
In recent decades, the advancement of computational algorithms and the availability of big data have enabled artificial intelligence (AI) to dramatically improve predictive performance in nearly all research areas. Specifically, machine learning (ML)...
Machine Learning Drug Discovery
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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction.

Journal of chemical information and modeling Aug 4, 2020
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and r...
Drug Discovery Uncertainty Neural Networks, Computer
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Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a Deep Convolutional Neural Network.

Journal of medicinal chemistry Jul 30, 2020
Rare neglected diseases may be neglected but are hardly rare, affecting hundreds of millions of people around the world. Here, we present a hit identification approach using AtomNet, the world's first deep convolutional neural network for structure-b...
Acetyltransferases Molecular Structure Enzyme Inhibitors Streptomyces Drug Discovery Humans Deep Learning
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The good, the bad, and the ugly in chemical and biological data for machine learning.

Drug discovery today. Technologies Jul 26, 2020
Machine learning and artificial intelligence (ML/AI) have become important research tools in molecular medicine and chemistry. Their rise and recent success in drug discovery promises a rapid progression of development pipelines while reshaping how f...
Machine Learning Algorithms Humans Drug Discovery
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Transfer Learning for Drug Discovery.

Journal of medicinal chemistry Jul 24, 2020
The data sets available to train models for drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence-assisted drug discovery. One solution to this problem is to develop alg...
Drug Discovery Deep Learning Datasets as Topic
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Practical considerations for active machine learning in drug discovery.

Drug discovery today. Technologies Jul 19, 2020
Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discov...
Humans Drug Discovery Supervised Machine Learning
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Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli.

Journal of applied microbiology Jul 16, 2020
AIMS: This article presents models of artificial neural networks (ANN) employed to predict the biological activity of chemical compounds based of their structure. Regression and classification models were designed to determine antimicrobial propertie...
Quantitative Structure-Activity Relationship Microbial Sensitivity Tests Anti-Bacterial Agents Escherichia coli Quaternary Ammonium Compounds Drug Discovery Neural Networks, Computer Imidazoles
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