AIMC Topic: Drug Discovery

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How can artificial intelligence be used for peptidomics?

Expert review of proteomics Aug 23, 2021
INTRODUCTION: Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful ...
Machine Learning Peptides Drug Discovery Humans Artificial Intelligence
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Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening.

Journal of chemical information and modeling Aug 17, 2021
Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever-increasing sizes of libraries of drug-like compounds and separating true positives from false positives. Here,...
Molecular Docking Simulation Drug Discovery Drug Evaluation, Preclinical Neural Networks, Computer Machine Learning
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A Convolutional Neural Network System to Discriminate Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics Aug 6, 2021
Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or f...
Computational Biology Drug Development Proteins Algorithms Drug Discovery Neural Networks, Computer Deep Learning Pharmaceutical Preparations
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MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Biomolecules Jul 29, 2021
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. Howeve...
Protein Conformation, alpha-Helical Proteins Protein Conformation, beta-Strand Kinetics Drug Discovery Datasets as Topic High-Throughput Screening Assays Drugs, Investigational Deep Learning Binding Sites Humans Protein Binding Protein Interaction Domains and Motifs Molecular Docking Simulation
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Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.

Science China. Life sciences Jul 26, 2021
Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has ...
Algorithms Artificial Intelligence Drug Discovery ERG1 Potassium Channel Datasets as Topic
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Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses.

Progress in neuro-psychopharmacology & biological psychiatry Jul 25, 2021
Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neur...
Neural Networks, Computer Artificial Intelligence Algorithms Animals Drug Discovery Drug Development Locomotion Psychotropic Drugs Disease Models, Animal Zebrafish Datasets as Topic
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Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.

Molecular diversity Jul 23, 2021
Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the labori...
Models, Theoretical Drug Development Models, Molecular Ligands Binding Sites Drug Discovery Deep Learning Protein Binding Algorithms Databases, Factual Biomarkers Humans Neural Networks, Computer
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The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Genes Jul 20, 2021
Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has re...
Humans Machine Learning High-Throughput Screening Assays Algorithms Single-Cell Analysis Precision Medicine Computational Biology Animals Systems Biology Drug Discovery
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Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.

Molecular diversity Jul 19, 2021
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The ava...
Databases, Factual Drug Design Drug Discovery Artificial Intelligence Machine Learning Algorithms Drug Repositioning Models, Molecular Neurodegenerative Diseases Deep Learning Big Data Drug Development Humans Ligands Quantitative Structure-Activity Relationship
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Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.

Journal of computer-aided molecular design Jul 17, 2021
Accurate prediction of lipophilicity-logP-based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps...
Models, Chemical Quantum Theory Solubility Solvents Thermodynamics Machine Learning Drug Discovery Models, Statistical Neural Networks, Computer
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