AIMC Topic: Structure-Activity Relationship

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Rational design of stapled antimicrobial peptides.

Amino acids Feb 12, 2023
The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immu...
Artificial Intelligence Anti-Bacterial Agents Structure-Activity Relationship Antimicrobial Peptides Prospective Studies Microbial Sensitivity Tests
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Research progress on the structure and biological diversities of 2-phenylindole derivatives in recent 20 years.

Bioorganic chemistry Jan 4, 2023
The privileged structure binds to multiple receptors with high affinity, which is helpful to the development of new bioactive compounds. Indole is classified as a privileged structure, which may be one of the most important structural categories in d...
Artificial Intelligence Anti-Bacterial Agents Structure-Activity Relationship Anti-Inflammatory Agents, Non-Steroidal Indoles
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In silico investigation on structure-function relationship of members belonging to the human SLC52 transporter family.

Proteins Dec 23, 2022
Riboflavin is an essential water-soluble vitamin that needs to be provided through the diet because of the conversion into flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN), important cofactors in hundreds of flavoenzymes. The adsorpt...
Structure-Activity Relationship Membrane Transport Proteins Humans Artificial Intelligence Flavin-Adenine Dinucleotide Flavin Mononucleotide Riboflavin Hearing Loss, Sensorineural
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Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Journal of chemical information and modeling Dec 1, 2022
Machine learning has become a crucial tool in drug discovery and chemistry at large, , to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure bu...
Drug Discovery Machine Learning Structure-Activity Relationship Algorithms Models, Molecular
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Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Molecular diversity Jun 23, 2022
Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a trai...
Support Vector Machine Structure-Activity Relationship Molecular Structure Histone Deacetylase 1 Machine Learning Algorithms Humans
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Molecular Similarity Perception Based on Machine-Learning Models.

International journal of molecular sciences May 30, 2022
Molecular similarity is an impressively broad topic with many implications in several areas of chemistry. Its roots lie in the paradigm that 'similar molecules have similar properties'. For this reason, methods for determining molecular similarity fi...
Receptors, Drug Humans Machine Learning Structure-Activity Relationship Perception
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Formula Graph Self-Attention Network for Representation-Domain Independent Materials Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Apr 27, 2022
The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and...
Structure-Activity Relationship Models, Chemical Neural Networks, Computer Machine Learning
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Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.

Journal of computer-aided molecular design Mar 29, 2022
The retrospective evaluation of virtual screening approaches and activity prediction models are important for methodological development. However, for fair comparison, evaluation data sets must be carefully prepared. In this research, we compiled str...
Support Vector Machine Ligands Structure-Activity Relationship Retrospective Studies
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Predicting chemical hazard across taxa through machine learning.

Environment international Mar 17, 2022
We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in ...
Reproducibility of Results Neural Networks, Computer Structure-Activity Relationship Machine Learning Animals Mammals
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Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.

International journal of molecular sciences Mar 17, 2022
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponent...
COVID-19 Drug Treatment Drug Discovery Humans Antiviral Agents Small Molecule Libraries SARS-CoV-2 Artificial Intelligence Ligands Structure-Activity Relationship Drug Design COVID-19 High-Throughput Nucleotide Sequencing Drug Evaluation, Preclinical
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