AIMC Topic: Drug Design

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Deep inverse reinforcement learning for structural evolution of small molecules.

Briefings in bioinformatics Jul 20, 2021
The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the p...
Deep Learning High-Throughput Screening Assays Small Molecule Libraries Drug Design Drug Discovery
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Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.

Nucleic acids research Jul 2, 2021
Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-bas...
Humans Machine Learning Software Antigens, Viral Vaccinology Workflow Spike Glycoprotein, Coronavirus Drug Design Epitopes Vaccines Internet COVID-19 SARS-CoV-2 COVID-19 Vaccines Proteome
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DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges.

Briefings in bioinformatics May 20, 2021
Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction ...
Drug Design Molecular Structure Quantum Theory Neural Networks, Computer Machine Learning
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MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.

Briefings in bioinformatics May 20, 2021
Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and ...
Drug Design Proteins Software Algorithms Databases, Protein Quantitative Structure-Activity Relationship Artificial Intelligence
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Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.

Physical chemistry chemical physics : PCCP Jan 21, 2021
Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the ...
Small Molecule Libraries Xenopus laevis Drug Design Receptors, N-Methyl-D-Aspartate Deep Learning Binding Sites Animals Mice Molecular Structure Ligands
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Trends in Deep Learning for Property-driven Drug Design.

Current medicinal chemistry Jan 1, 2021
It is more pressing than ever to reduce the time and costs for the development of lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the developmen...
Deep Learning Models, Molecular Drug Design Humans Drug Discovery Machine Learning
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Artificial Intelligence and Cheminformatics-Guided Modern Privileged Scaffold Research.

Current topics in medicinal chemistry Jan 1, 2021
With the rapid development of computer science in scopes of theory, software, and hardware, artificial intelligence (mainly in form of machine learning and more complex deep learning) combined with advanced cheminformatics is playing an increasingly ...
Drug Design Drug Discovery Machine Learning Cheminformatics Humans Software Artificial Intelligence
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Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD.

Current drug discovery technologies Jan 1, 2021
BACKGROUND: Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applicatio...
Humans Drug Design Machine Learning Drug Discovery
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Convolutional Neural Network-based Virtual Screening.

Current medicinal chemistry Jan 1, 2021
Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different sco...
Drug Design Ligands Humans Neural Networks, Computer
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An Analysis of QSAR Research Based on Machine Learning Concepts.

Current drug discovery technologies Jan 1, 2021
Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions...
Drug Discovery Quantitative Structure-Activity Relationship Machine Learning Drug Design Humans
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