Molecular Fingerprint-Based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions

Abstract

In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB2 activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds and we have discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Co-Regularised Support Vector Regression

“…All three formats are binary and high-dimensional. An implementation of the proposed methods and baselines, together with the datasets and experiment descriptions are available as open source 7 . We compare the CoSVR variants ε-CoSVR, 2 -CoSVR, and Σ-CoSVR against CoRLSR, as well as SVR with a single-view (SVR([fingerprint name])) in terms of root mean squared error (RMSE) using the linear kernel.…”

“…(In the context of regression, we will use the name ligands for all considered compounds.) Various approaches like neural networks [7] have been applied. However, support vector regression (SVR) is the state-of-the-art method for affinity prediction studies (e.g., [12]).…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…QSAR correlate structure or property descriptors of compounds with chemical or biological activities and increasing number of neural network models are currently published for predicting various physicochemical properties from the molecular structures [70][71][72][73]. All QSAR studies are based on the fundamental concept of interdependence of biological activities on physicochemical parameters.…”

Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

“…In this chapter, we focus on the fingerprint-based ANN-QSAR (FANN-QSAR) research work [17] in predicting biological activities of structurally diverse cannabinoid (CB) ligands using ANN. To the best of our knowledge, there have been no previous studies which have used molecular fingerprints as descriptors to predict biological activities (such as pIC 50 or p K i ), although a few studies have been reported to predict ligand classes [18, 19].…”