AIMC Topic: Quantitative Structure-Activity Relationship

Clear Filters Showing 491 to 500 of 551 articles

Comparison of predictions of developmental toxicity for compounds of solvent data set.

SAR and QSAR in environmental research
We have considered a series of 235 compounds technically classified as solvents. Chemically, they belong to different classes. Their potential developmental toxicity was evaluated using two models available on platform VEGA HUB; model CAESAR and the ...

Machine Learning for In Silico ADMET Prediction.

Methods in molecular biology (Clifton, N.J.)
ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safet...

Deep Neural Networks for QSAR.

Methods in molecular biology (Clifton, N.J.)
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the f...

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints.

Methods in molecular biology (Clifton, N.J.)
The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug d...

Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.

Briefings in bioinformatics
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure-activity relationship (QSAR) methods...

Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Briefings in bioinformatics
Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the...

A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors.

SAR and QSAR in environmental research
The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning ...

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.

Briefings in bioinformatics
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 ...