AIMC Topic: Ligands

Clear Filters Showing 501 to 510 of 719 articles

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Journal of chemical information and modeling Mar 18, 2019
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual scr...
Cheminformatics Protein Conformation Molecular Docking Simulation Thermodynamics User-Computer Interface Proteins Drug Evaluation, Preclinical Ligands Machine Learning
View on PubMed DOI

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling Mar 5, 2019
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...
Quantitative Structure-Activity Relationship Receptors, FSH Binding Sites Ligands Hydrophobic and Hydrophilic Interactions Computers, Molecular Drug Design Protein Binding Computational Biology Cell Line Models, Molecular Humans Machine Learning
View on PubMed DOI

In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening.

Journal of chemical information and modeling Mar 5, 2019
Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. ML methods such as convolutional neural networks show promising results and often outperform traditional methods such as e...
Molecular Structure Bias Benchmarking Ligands Retrospective Studies Machine Learning Molecular Docking Simulation Structure-Activity Relationship Databases, Factual
View on PubMed DOI

De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping.

Journal of chemical information and modeling Mar 5, 2019
Here we show that Generative Topographic Mapping (GTM) can be used to explore the latent space of the SMILES-based autoencoders and generate focused molecular libraries of interest. We have built a sequence-to-sequence neural network with Bidirection...
Catalytic Domain Small Molecule Libraries Receptor, Adenosine A2A Deep Learning Drug Evaluation, Preclinical Ligands Molecular Docking Simulation Drug Design
View on PubMed DOI

Predicting protein-ligand binding residues with deep convolutional neural networks.

BMC bioinformatics Feb 26, 2019
BACKGROUND: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categ...
Deep Learning Protein Binding Proteins Databases, Protein Amino Acid Sequence Ligands Neural Networks, Computer Algorithms
View on PubMed DOI

A smartphone-based system for fluorescence polarization assays.

Biosensors & bioelectronics Dec 24, 2018
This paper demonstrates the use of a smartphone-based sensor for fluorescence polarization (FP) analysis of biomolecules. The FP detection can rapidly sense ligand-analyte bindings by measuring molecule mobility, and thus, FP-based assays have been w...
Ligands Lasers Bacterial Toxins Fluorescence Polarization Smartphone Biosensing Techniques Limit of Detection Humans Biomarkers
View on PubMed DOI

Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE.

Journal of chemical theory and computation Dec 24, 2018
In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149...
Thermodynamics Proteins Molecular Dynamics Simulation Ligands Deep Learning Protein Binding
View on PubMed DOI

Converging a Knowledge-Based Scoring Function: DrugScore.

Journal of chemical information and modeling Dec 18, 2018
We present DrugScore, a new version of the knowledge-based scoring function DrugScore, which builds upon the same formalism used to derive DrugScore but exploits a training data set of nearly 40 000 X-ray complex structures, a highly diverse and the,...
Informatics Models, Molecular Knowledge Bases Ligands Drug Design
View on PubMed DOI

More Than Just a Removal Service: Scavenger Receptors in Leukocyte Trafficking.

Frontiers in immunology Dec 12, 2018
Scavenger receptors are a highly diverse superfamily of proteins which are grouped by their inherent ability to bind and internalize a wide array of structurally diverse ligands which can be either endogenous or exogenous in nature. Consequently, sca...
Lipoproteins, LDL Leukocytes Atherosclerosis Cell Movement Apoptosis Receptors, Scavenger Humans Ligands Homeostasis Protein Transport
View on PubMed DOI

An overview of neural networks for drug discovery and the inputs used.

Expert opinion on drug discovery Nov 17, 2018
: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as ...
Humans Neural Networks, Computer Drug Discovery Deep Learning Ligands Artificial Intelligence Drug Design
View on PubMed DOI
Popular Topics
Recent Journals