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Quantitative Structure-Activity Relationship

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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...

Classification models of HCV NS3 protease inhibitors based on support vector machine (SVM).

Combinatorial chemistry & high throughput screening
Inhibition of the hepatitis C virus (HCV) non-structural protein 3 (NS3) serine protease by molecule inhibitors is an attractive strategy for the treatment of hepatitis C. We built four classification models based on a dataset of 413 HCV NS3 protease...