AIMC Topic: Drug Discovery

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Is one-shot learning a viable option in drug discovery?

Expert opinion on drug discovery Mar 18, 2019
Humans Neural Networks, Computer Drug Discovery Drug Development Machine Learning
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A combined drug discovery strategy based on machine learning and molecular docking.

Chemical biology & drug design Mar 7, 2019
Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests,...
Molecular Docking Simulation Crystallography, X-Ray Drug Discovery Binding Sites Area Under Curve Principal Component Analysis Machine Learning Protein Structure, Tertiary Inhibitory Concentration 50 ROC Curve Databases, Factual
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Molecular Structure Extraction from Documents Using Deep Learning.

Journal of chemical information and modeling Feb 27, 2019
Chemical structure extraction from documents remains a hard problem because of both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines t...
Documentation Drug Discovery Data Mining Deep Learning
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Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

Molecular pharmaceutics Feb 26, 2019
The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcr...
Reverse Transcriptase Inhibitors HIV Reverse Transcriptase Decision Trees Anti-HIV Agents Humans Support Vector Machine Databases, Factual Bayes Theorem Neural Networks, Computer Drug Discovery HIV HIV Infections Machine Learning
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Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning.

Journal of chemical information and modeling Feb 22, 2019
Binding prediction between targets and drug-like compounds through deep neural networks has generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classif...
Deep Learning Quantitative Structure-Activity Relationship Informatics Reproducibility of Results Drug Discovery
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Imputation of Assay Bioactivity Data Using Deep Learning.

Journal of chemical information and modeling Feb 21, 2019
We describe a novel deep learning neural network method and its application to impute assay pIC values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and c...
Databases, Pharmaceutical Drug Discovery Biological Assay Molecular Structure Quantitative Structure-Activity Relationship Pharmaceutical Preparations Deep Learning
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Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.

PLoS computational biology Feb 20, 2019
Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequenc...
Sarcoma Kaplan-Meier Estimate Computational Biology Databases, Genetic Male Prognosis Female Middle Aged Humans Drug Discovery Biomarkers, Tumor Machine Learning Gene Expression Profiling Transcriptome
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Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints.

Journal of applied toxicology : JAT Feb 14, 2019
The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several...
Machine Learning Algorithms Humans Carcinogens Drug Discovery Quantitative Structure-Activity Relationship
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Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

Journal of chemical information and modeling Feb 11, 2019
The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes n...
Drug Discovery Protein Binding Receptors, Androgen Software Molecular Docking Simulation Humans Machine Learning Endocrine Disruptors Androgens Androgen Receptor Antagonists
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Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification.

Journal of chemical information and modeling Feb 1, 2019
Chemical synthesis planning is a key aspect in many fields of chemistry, especially drug discovery. Recent implementations of machine learning and artificial intelligence techniques for retrosynthetic analysis have shown great potential to improve co...
Drug Discovery Deep Learning Cheminformatics Databases, Pharmaceutical Patents as Topic United States Chemistry Techniques, Synthetic
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