AIMC Topic: Drug Discovery

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Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Molecular diversity Jun 23, 2021
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to ...
Drug Design Drug Discovery Deep Learning Databases, Factual Reproducibility of Results Big Data Workflow Artificial Intelligence Machine Learning Algorithms
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CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

BMC bioinformatics Jun 22, 2021
BACKGROUND: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined...
Drug Discovery Machine Learning Cluster Analysis Molecular Dynamics Simulation
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QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity Jun 19, 2021
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph...
Neural Networks, Computer Algorithms Quantitative Structure-Activity Relationship Mutagenesis Molecular Structure Bayes Theorem Mutagens Drug Discovery Deep Learning Models, Theoretical
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Drug repurposing: Iron in the fire for older drugs.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie Jun 18, 2021
Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental dr...
Drug Discovery Big Data Drug Repositioning Pharmaceutical Preparations Computer Simulation Drug Delivery Systems Animals Humans Machine Learning
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Predicting Drug-Target Interactions with Deep-Embedding Learning of Graphs and Sequences.

The journal of physical chemistry. A Jun 18, 2021
Computational approaches for predicting drug-target interactions (DTIs) play an important role in drug discovery since conventional screening experiments are time-consuming and expensive. In this study, we proposed end-to-end representation learning ...
Computer Graphics Pharmaceutical Preparations Amino Acid Sequence Drug Discovery Deep Learning Computational Biology Proteins
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Modern computational intelligence based drug repurposing for diabetes epidemic.

Diabetes & metabolic syndrome Jun 18, 2021
BACKGROUND AND AIM: Objectives are to explore recent advances in discovery of new antidiabetic agents using repurposing strategies and to discuss modern technologies used for drug repurposing highlighting diabetic specific web portal.
Drug Repositioning Internet Knowledge Bases Drug Discovery Diabetes Mellitus, Type 2 Humans Hypoglycemic Agents Artificial Intelligence
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Prediction of drug efficacy from transcriptional profiles with deep learning.

Nature biotechnology Jun 17, 2021
Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning-based efficacy predicti...
Transcriptome Deep Learning Mice Proteins Drug Repositioning Animals Drug Discovery
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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity Jun 16, 2021
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...
Molecular Docking Simulation Drug Discovery ROC Curve Machine Learning Algorithms Models, Molecular Workflow Molecular Dynamics Simulation Biological Products Structure-Activity Relationship Area Under Curve Humans Cheminformatics Databases, Pharmaceutical Acetylcholinesterase Cholinesterase Inhibitors
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Combining generative artificial intelligence and on-chip synthesis for de novo drug design.

Science advances Jun 11, 2021
Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were ge...
Drug Design Artificial Intelligence Drug Discovery
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Molecular diversity Jun 10, 2021
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data...
Models, Theoretical Humans Proteins Structure-Activity Relationship Artificial Intelligence Drug Discovery Algorithms Big Data Drug Design Protein Interaction Mapping Protein Binding Protein Folding Databases, Pharmaceutical Models, Molecular Workflow Data Mining
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