AIMC Topic: Structure-Activity Relationship

Clear Filters Showing 61 to 70 of 240 articles

Accurate positioning of functional residues with robotics-inspired computational protein design.

Proceedings of the National Academy of Sciences of the United States of America Mar 7, 2022
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geom...
Computer-Aided Design Computational Biology Proteins Algorithms Reproducibility of Results Amino Acids Protein Conformation Protein Engineering Isomerases Structure-Activity Relationship Robotics Models, Molecular
View on PubMed DOI

Using Steady-State Kinetics to Quantitate Substrate Selectivity and Specificity: A Case Study with Two Human Transaminases.

Molecules (Basel, Switzerland) Feb 18, 2022
We examined the ability of two human cytosolic transaminases, aspartate aminotransferase (GOT1) and alanine aminotransferase (GPT), to transform their preferred substrates whilst discriminating against similar metabolites. This offers an opportunity ...
Kinetics Transaminases Structure-Activity Relationship Humans Binding Sites Alkyl and Aryl Transferases Protein Conformation Animals Substrate Specificity Enzyme Activation Models, Molecular Cattle Transferases (Other Substituted Phosphate Groups) Amino Acids
View on PubMed DOI

Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland) Feb 14, 2022
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...
Metalloproteins Protein Binding Structure-Activity Relationship Models, Molecular Proteolysis Machine Learning Binding Sites Drug Design Amino Acid Sequence Protein Stability
View on PubMed DOI

Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence.

Journal of chemical information and modeling Jan 26, 2022
In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important ...
Computer Simulation Drug Design Structure-Activity Relationship Neural Networks, Computer Artificial Intelligence
View on PubMed DOI

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Scientific reports Jan 10, 2022
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...
Crystallography, X-Ray Protein Conformation Binding Sites Support Vector Machine Structure-Activity Relationship Ligands Cyclin-Dependent Kinase 2 Machine Learning Molecular Docking Simulation Protein Binding
View on PubMed DOI

Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Scientific reports Jan 7, 2022
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrom...
Computational Biology Protein Processing, Post-Translational Proteins Structure-Activity Relationship Reproducibility of Results Deep Learning Glutamic Acid Models, Molecular Amino Acid Sequence Protein Conformation
View on PubMed DOI

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Bioorganic & medicinal chemistry Nov 27, 2021
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully trans...
Dihydroergotoxine Structure-Activity Relationship Molecular Structure Tumor Suppressor Protein p53 Drug Discovery Antineoplastic Agents Cell Line, Tumor Cell Survival Machine Learning Drug Screening Assays, Antitumor Colorectal Neoplasms Cell Proliferation Molecular Docking Simulation Dose-Response Relationship, Drug Humans
View on PubMed DOI

Application of Deep Neural Network Models in Drug Discovery Programs.

ChemMedChem Oct 18, 2021
In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a ...
Drug Discovery Deep Learning Dose-Response Relationship, Drug Factor Xa Factor Xa Inhibitors Structure-Activity Relationship Molecular Structure Humans Cytochrome P-450 CYP3A Cytochrome P-450 CYP3A Inhibitors
View on PubMed DOI

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils.

Molecules (Basel, Switzerland) Oct 17, 2021
Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, th...
Microsporum Machine Learning Structure-Activity Relationship Arthrodermataceae Anti-Infective Agents Data Collection Complex Mixtures Phylogeny Oils, Volatile
View on PubMed DOI

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

Scientific reports Sep 15, 2021
The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubilit...
Proteins Structure-Activity Relationship Models, Molecular Machine Learning Amino Acids Molecular Conformation Protein Biosynthesis
View on PubMed DOI
Popular Topics
Recent Journals