Converting nanotoxicity data to information using artificial intelligence and simulation
Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …
However, data is not equal to information. The question is how to extract critical information …
[HTML][HTML] NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting
unique physicochemical (PChem) properties compared to their bulk analogues. These …
unique physicochemical (PChem) properties compared to their bulk analogues. These …
Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules …
Abstract World Health Organization characterized novel coronavirus disease (COVID-19),
caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as …
caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as …
Zeta potential for metal oxide nanoparticles: a predictive model developed by a nano-quantitative structure–property relationship approach
Physico–chemical characterization of nanoparticles in the context of their transport and fate
in the environment is an important challenge for risk assessment of nanomaterials. One of …
in the environment is an important challenge for risk assessment of nanomaterials. One of …
Practices and trends of machine learning application in nanotoxicology
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with
very encouraging results. Adverse effects of nanoforms are affected by multiple features …
very encouraging results. Adverse effects of nanoforms are affected by multiple features …
How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?
One of the important applications of quantitative structure-activity relationship (QSAR)
models is to fill data gaps by predicting a given response property or activity from known …
models is to fill data gaps by predicting a given response property or activity from known …
Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as …
As the world struggles against current global pandemic of novel coronavirus disease
(COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum …
(COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum …
Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties
Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins
adsorbed onto the NP surface, forming a “protein corona”, thereby impacting cellular …
adsorbed onto the NP surface, forming a “protein corona”, thereby impacting cellular …
QSPR/QSAR: State-of-art, weirdness, the future
AA Toropov, AP Toropova - Molecules, 2020 - mdpi.com
Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for
epistemological processes in natural sciences is discussed. Some weirdness of …
epistemological processes in natural sciences is discussed. Some weirdness of …
Nano-QSAR modeling for predicting biological activity of diverse nanomaterials
KP Singh, S Gupta - RSC Advances, 2014 - pubs.rsc.org
This study reports robust reliable ensemble learning (EL) approach based nano-QSAR
models for predicting the biological effects of diverse nanomaterials (NMs) using simple …
models for predicting the biological effects of diverse nanomaterials (NMs) using simple …