AIMC Topic: Drug Design

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Deep Learning in the Study of Protein-Related Interactions.

Protein and peptide letters Jan 1, 2020
Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In re...
Drug Design Deep Learning Protein Binding Humans Pharmaceutical Preparations RNA Proteins Databases, Protein DNA
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A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques.

Current computer-aided drug design Jan 1, 2020
BACKGROUND: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to t...
Neural Networks, Computer Machine Learning Drug Design Drug Discovery Data Mining Humans
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Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity.

Current drug discovery technologies Jan 1, 2020
BACKGROUND: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs.
Microbial Sensitivity Tests Models, Chemical Antitubercular Agents Machine Learning Animals Drug Design Daphnia Toxicity Tests, Acute Isoniazid Rifampin Tuberculosis, Multidrug-Resistant Mycobacterium tuberculosis Datasets as Topic Humans
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Hunting for New Drugs with AI.

Nature Dec 1, 2019
United States Food and Drug Administration Drug Industry Cytochrome P-450 Enzyme System Parkinson Disease Prostatic Neoplasms Drug-Related Side Effects and Adverse Reactions Drug Approval Cytochrome P-450 Enzyme Inhibitors Artificial Intelligence Machine Learning Male Drug Design Drug Discovery United States Humans
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Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning.

Journal of computer-aided molecular design Nov 1, 2019
Small Molecule Libraries Computational Chemistry Artificial Intelligence Machine Learning Drug Design Humans
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Microsoft makes splash in AI-enabled lab solutions.

Nature biotechnology Aug 1, 2019
Algorithms Drug Design Mammals Biomedical Research Machine Learning Cell Line Synthetic Biology Animals Molecular Dynamics Simulation
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Applications of machine learning in drug discovery and development.

Nature reviews. Drug discovery Jun 1, 2019
Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality...
Humans Neural Networks, Computer Machine Learning Animals Drug Design Drug Discovery
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Opportunities and challenges using artificial intelligence in ADME/Tox.

Nature materials May 1, 2019
A recent conference organized a panel of scientists representing small and big pharma companies, who work at the interface of machine learning (ML) and absorption, distribution, metabolism, excretion, and toxicology (ADME/Tox). With the recent rebirt...
Algorithms Computer Simulation Drug Design Pharmacokinetics Humans Congresses as Topic Artificial Intelligence Decision Making Machine Learning Drug Discovery Mitochondria Deep Learning Technology, Pharmaceutical Computational Biology
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Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review.

Current computer-aided drug design Jan 1, 2019
Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on th...
Antitubercular Agents Drug Design Mycobacterium tuberculosis Tuberculosis Humans Drug Discovery Ligands Molecular Targeted Therapy Structure-Activity Relationship Machine Learning Animals
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Machine Learning in Quantitative Protein-peptide Affinity Prediction: Implications for Therapeutic Peptide Design.

Current drug metabolism Jan 1, 2019
BACKGROUND: Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recen...
Protein Binding Proteins Models, Molecular Machine Learning Humans Peptides Drug Design
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