Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities. The generated 3D QSAR model revealed the main structural features important in the potency of RON inhibitors. The results obtained from the presented study can be used in lead optimization projects for designing of novel compounds where inhibition of RON is needed.
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http://dx.doi.org/10.1142/S0219720022500159 | DOI Listing |
J Chem Inf Model
September 2023
Molecular Discovery, Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom.
Deep Learning approaches are able to automatically extract relevant features from the input data and capture nonlinear relationships between the input and output. In this work, we present the GRID-derived AI (GrAId) descriptors, a simple modification to GRID MIFs that facilitate their use in combination with Convolutional Neural Networks (CNNs) to build Deep Learning models in a rotationally, conformationally, and alignment-independent approach we are calling DeepGRID. To our knowledge, this is the first time that GRID MIFs have been combined with CNNs in a Deep Learning approach.
View Article and Find Full Text PDFJ Bioinform Comput Biol
June 2022
Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities.
View Article and Find Full Text PDFArch Toxicol
July 2018
Federal Food Safety and Veterinary Office FSVO/BLV, Risk Assessment Division, Schwarzenburgstrasse 155, Berne, 3003, Switzerland.
A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated C and N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set.
View Article and Find Full Text PDFEcotoxicol Environ Saf
August 2018
College of Sciences, Faculty of Chemistry, University of Tehran, Tehran, Iran. Electronic address:
In the present study, a very thorough and in-depth three-dimensional quantitative structure-toxicity relationship (3D-QSTR) analysis has been implemented to make a correlation between the structural information of the ionic liquids (ILs) and their cytotoxicity towards Leukemia rat cell line IPC-81, as one of the ILs' toxicological consequences. To do this, alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs), were correlated to the cytotoxicity values by partial least squares (PLS) and support vector regression (SVR). Genetic algorithm (GA), as a powerful linear tool, was used to select the best and interpretative subset of variables for the predictive model building.
View Article and Find Full Text PDFAdv Pharm Bull
September 2017
Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Receptor tyrosine kinase (RTK) inhibitors are widely used pharmaceuticals in cancer therapy. Fibroblast growth factor receptors (FGFRs) are members of RTK superfamily which are highly expressed on the surface of carcinoma associate fibroblasts (CAFs). The involvement of FGFRs in different types of cancer makes them promising target in cancer therapy and hence, the identification of novel FGFR inhibitors is of great interest.
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