A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data

Abstract

In the current study, we propose a new quantitative read-across methodology for predicting the toxicity of newly synthesized NPs based on the similarity with structural analogues.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection

“…The predictive ability of the PLS models developed was further enhanced by “intelligent” consensus modeling. Similarity-based read-across predictions [ 10 , 35 ] superseded both individual PLS models as well as consensus prediction. Furthermore, the SRD analysis gave an idea about the modeling approach’s discriminating ability.…”

“…In the present research, we have applied a machine learning approach for read-across predictions based on similarity measures [ 10 ]. The predictions were made using the tool, Quantitative Read Across v4.0 (available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home ) which uses Euclidean distance, Gaussian kernel function, and Laplacian kernel function-based similarity estimation.…”

“…These techniques are effective and practical approaches in quantifying different biological interactions of ligand-receptor complex [ 8 ]. Computational approaches such as quantitative structure–activity relationship (QSAR) [ 9 ] and read-across [ 10 ] are promising methods where resources are limited and animal experimentation is not feasible. The major advantages of these computational methods are mainly (a) cost effective, (b) reduce animal experimentation, and (c) accelerate the drug development process.…”

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across

“…In recent times, there has been an increase in non-testing methods which comply with the 3Rs (Reduction, Replacement and Refinement in animal experiments) in scientific experimentations [10]. Among various other non-testing methods, Quantitative Structure-Activity Relationship (QSAR) and Chemical Read-Across are two of the most widely used methods for prediction of toxicity associated with chemicals [10][11]. The advantages associated with in silico approaches in general are: a) they reduce experimental time, cost and b) they speed up obtaining the desired results.…”

Applications of chem-bioinformatic, chemometric and machine learning approaches for COVID-19 related research

“…Different chem-bioinformatic approaches including structurebased (homology modeling, molecular docking, molecular dynamics, protein-protein interaction network, etc.) and ligand-based modeling strategies (pharmacophore mapping, quantitative structure-activity relationships or QSARs and chemometric models in addition to similarity-based unsupervised techniques like read-across [3,4]) may be used for this purpose with an objective to prioritize the candidate drugs for further experiments [5,6]. Drug repurposing is an effective and economic approach to find new indications for already known drugs within a short period which can be used to overcome the emergence of resistance to existing antiviral drugs and re-emerging viral infections [7].…”