AIMC Topic: Drug Discovery

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Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter.

Journal of medicinal chemistry Nov 13, 2018
Assay interference compounds give rise to false-positives and cause substantial problems in medicinal chemistry. Nearly 500 compound classes have been designated as pan-assay interference compounds (PAINS), which typically occur as substructures in o...
ROC Curve Computational Biology Models, Statistical Machine Learning Drug Discovery
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Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks.

Journal of chemical information and modeling Oct 30, 2018
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modeling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability, and ...
Drug Discovery Deep Learning Research Design
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How far have decision tree models come for data mining in drug discovery?

Expert opinion on drug discovery Oct 23, 2018
Data Mining Drug Discovery Decision Trees Machine Learning Molecular Docking Simulation Humans
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QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.

PloS one Oct 17, 2018
BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cyb...
Programming Languages Computer Simulation Algorithms Computational Biology Computer Security Databases, Factual Automation Semiconductors Reproducibility of Results Quantum Dots Neural Networks, Computer Drug Discovery Data Mining Machine Learning
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Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data.

Journal of chemical information and modeling Oct 16, 2018
Machine learning has shown enormous potential for computer-aided drug discovery. Here we show how modern convolutional neural networks (CNNs) can be applied to structure-based virtual screening. We have coupled our densely connected CNN (DenseNet) wi...
Databases, Protein Ligands Databases, Pharmaceutical Drug Discovery Proteins Humans Machine Learning Molecular Docking Simulation Computer-Aided Design Neural Networks, Computer
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Deep learning and virtual drug screening.

Future medicinal chemistry Oct 5, 2018
Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads...
Humans Deep Learning Ligands Neural Networks, Computer Drug Discovery
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Artificial Intelligence in Drug Design.

Molecules (Basel, Switzerland) Oct 2, 2018
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicoch...
Humans Drug Discovery Neural Networks, Computer Quantitative Structure-Activity Relationship Artificial Intelligence
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Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance.

Molecular informatics Sep 24, 2018
A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public da...
Humans Metabolic Clearance Rate Hepatobiliary Elimination Microsomes, Liver Machine Learning Databases, Chemical Drug Discovery
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Deep learning-based transcriptome data classification for drug-target interaction prediction.

BMC genomics Sep 24, 2018
BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded...
Models, Theoretical Proteins Molecular Targeted Therapy Drug Interactions Gene Expression Profiling Transcriptome Computer Simulation Drug Discovery Machine Learning Algorithms Databases, Factual Humans
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Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery.

Molecular pharmaceutics Sep 19, 2018
Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this...
Janus Kinase 3 Animals Drug Discovery Neural Networks, Computer Humans Machine Learning
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