AIMC Topic: Drug Discovery

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An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions.

Scientific reports Sep 26, 2019
In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a ...
Cell Line Quantum Dots Lysosomes Cell Membrane Toxicity Tests Spectrophotometry Animals Drug Discovery Mice Artificial Intelligence Software
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Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network.

Journal of medicinal chemistry Sep 25, 2019
Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typical...
Organic Chemicals Quantum Theory Ligands Drug Discovery Deep Learning Protein Binding Static Electricity X-Linked Inhibitor of Apoptosis Protein Datasets as Topic Factor Xa Hydrogen Bonding
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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...
Humans Models, Theoretical Drug Development Drug Discovery Data Mining Deep Learning Big Data Artificial Intelligence Animals
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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...
Neural Networks, Computer Drug Design Drug Discovery Humans Molecular Conformation Quantitative Structure-Activity Relationship Proteins Small Molecule Libraries Models, Chemical Ligands
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PTPD: predicting therapeutic peptides by deep learning and word2vec.

BMC bioinformatics Sep 6, 2019
*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational mo...
Peptides Computational Biology Drug Discovery Databases, Nucleic Acid Deep Learning
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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Journal of medicinal chemistry Aug 27, 2019
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
Models, Molecular Solubility Datasets as Topic Deep Learning Databases, Chemical Drug Discovery Organic Chemicals Proof of Concept Study
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Looking beyond the hype: Applied AI and machine learning in translational medicine.

EBioMedicine Aug 26, 2019
Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old t...
Animals Drug Discovery Genomics Translational Research, Biomedical Molecular Imaging Machine Learning Humans Precision Medicine Artificial Intelligence Reproducibility of Results
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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

Journal of chemical information and modeling Aug 22, 2019
The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of comp...
Thermodynamics Machine Learning Models, Molecular Small Molecule Libraries Humans Drug Design Protein Kinase Inhibitors Cyclin-Dependent Kinase 2 Drug Discovery
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Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Journal of chemical information and modeling Aug 19, 2019
Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learnin...
Humans Machine Learning Molecular Docking Simulation Drug Design Drug Discovery HIV Infections HIV Protease Protein Binding HIV Protease Inhibitors HIV-1 Ligands
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Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.

Journal of medicinal chemistry Aug 15, 2019
The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity dat...
Deep Learning Drug Discovery Protein Kinases Datasets as Topic Databases, Chemical Protein Kinase Inhibitors Polypharmacology
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