Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

Abstract

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Lead Discovery, Chemistry Optimization, and Biological Evaluation Studies of Novel Biamide Derivatives as CB2 Receptor Inverse Agonists and Osteoclast Inhibitors

“…38,39 Given its high CB 2 affinity, selectivity, and strongest inhibition of osteoclastogenesis, compound 26 was selected as a template compound in our CoMFA studies. To search for preferred conformations of compound 26 , molecular dynamic simulations and molecular mechanics (MD/MM) were carried out based on our established computational protocol.…”

Latest advances in molecular topology applications for drug discovery

“…Afterward, other Comparative Molecular Field Analysislike approaches such as Comparative Molecular Similarity Indices Analysis [3], Self-Organizing Molecular Field Analysis [3] and GRID/GOLPE [4] were also introduced.…”

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

“…Quantitative structure-activity relationship (QSAR) studies play essential roles in pharmaceutical research to identify and generate high-quality leads in the early stages of drug discovery [1–3]. QSAR studies help reduce the costly failures of drug candidates by identifying promising lead compounds and reducing the number of costly experiments.…”

“…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]. Three types of molecular fingerprints were used as network inputs to train ANN-QSAR models, and the results were compared to well-known 2D and 3D QSAR methods [1] using five data sets. As a case study, we used the FANN-QSAR method to predict binding affinities of cannabinoid ligands using a large and structurally diverse CB ligand data set [20].…”