AIMC Topic: Quantitative Structure-Activity Relationship

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Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

Combinatorial chemistry & high throughput screening
The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties o...

QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence.

Combinatorial chemistry & high throughput screening
Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called com...

A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.

SAR and QSAR in environmental research
Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two qu...

The application of machine learning to the modelling of percutaneous absorption: an overview and guide.

SAR and QSAR in environmental research
Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specific...

Role of moving average analysis for development of multi-target (Q)SAR models.

Mini reviews in medicinal chemistry
In modern drug discovery era, multi target- quantitative structure activity relationship [mt- (Q)SAR] approaches have emerged as novel and powerful alternatives in the field of in-silico drug design so as to facilitate the discovery of new chemical e...

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.)
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...

AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

Methods in molecular biology (Clifton, N.J.)
UNLABELLED: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predict...

Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation.

Methods in molecular biology (Clifton, N.J.)
With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silic...

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents.

Methods in molecular biology (Clifton, N.J.)
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibi...