AIMC Topic: Drug Design

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Trends in application of advancing computational approaches in GPCR ligand discovery.

Experimental biology and medicine (Maywood, N.J.) Feb 27, 2021
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefo...
Humans Machine Learning Drug Design Drug Discovery Receptors, G-Protein-Coupled Ligands
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The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design.

Life sciences Feb 22, 2021
Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery s...
Nanostructures Biomimetics Drug Delivery Systems Drug Design Electronics Humans Artificial Intelligence
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Prioritizing Pain-Associated Targets with Machine Learning.

Biochemistry Feb 19, 2021
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model ...
Drug Design Drug Discovery Drug Delivery Systems Models, Biological Pain Analgesics Humans Machine Learning
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Graph neural networks for automated de novo drug design.

Drug discovery today Feb 17, 2021
The goal of de novo drug design is to create novel chemical entities with desired biological activities and pharmacokinetics (PK) properties. Over recent years, with the development of artificial intelligence (AI) technologies, data-driven methods ha...
Neural Networks, Computer Artificial Intelligence Humans Drug Discovery Technology Drug Design
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In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Molecular diversity Feb 7, 2021
Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico...
Proteins Molecular Dynamics Simulation RNA Drug Design Aptamers, Nucleotide CD13 Antigens Ligands Molecular Conformation Machine Learning Biomarkers Molecular Docking Simulation Algorithms
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Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

International journal of molecular sciences Feb 7, 2021
. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properti...
Pharmaceutical Preparations Machine Learning Animals Drug Design Humans Neural Networks, Computer
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An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

Scientific reports Feb 5, 2021
The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical the...
Humans COVID-19 Allergens Solubility Immunogenicity, Vaccine RNA, Viral COVID-19 Vaccines Molecular Docking Simulation Molecular Dynamics Simulation Spike Glycoprotein, Coronavirus Drug Design Vaccines, Subunit Deep Learning Protein Conformation Computational Biology T-Lymphocytes, Cytotoxic Models, Molecular Epitopes, B-Lymphocyte T-Lymphocytes, Helper-Inducer SARS-CoV-2 Epitopes, T-Lymphocyte Codon Usage Mutation
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Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling Jan 25, 2021
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug desi...
Catalytic Domain Deep Learning Humans Proteins Drug Design Ligands
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A comprehensive comparison of molecular feature representations for use in predictive modeling.

Computers in biology and medicine Jan 9, 2021
Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples r...
Neural Networks, Computer Machine Learning Drug Design
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OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design.

Journal of chemical information and modeling Jan 4, 2021
Deep learning models have demonstrated outstanding results in many data-rich areas of research, such as computer vision and natural language processing. Currently, there is a rise of deep learning in computational chemistry and materials informatics,...
Support Vector Machine Computational Chemistry Drug Design Deep Learning Machine Learning
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