Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

Abstract

Modern nanotechnology research has generated numerous experimental data for various nanomaterials. However, the few nanomaterial databases available are not suitable for modeling studies due to the way they are curated. Here, we report the construction of a large nanomaterial database containing annotated nanostructures suited for modeling research. The database, which is publicly available through http://www.pubvinas.com/, contains 705 unique nanomaterials covering 11 material types. Each nanomaterial has up to six physicochemical properties and/or bioactivities, resulting in more than ten endpoints in the database. All the nanostructures are annotated and transformed into protein data bank files, which are downloadable by researchers worldwide. Furthermore, the nanostructure annotation procedure generates 2142 nanodescriptors for all nanomaterials for machine learning purposes, which are also available through the portal. This database provides a public resource for data-driven nanoinformatics modeling research aimed at rational nanomaterial design and other areas of modern computational nanotechnology.

Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

“…In vitro NP-cell interactions [22] Mixed naive Bayes, sequential minimal optimization (SMO), J48, bagging, locally weighted learning (LWL), decision Q-dots and FeOx NPs Cellular uptake of cross-linked iron oxide NPs [25] Target specificity of NPs Nanoinformatics prediction [18,26] Logistic linear regression with an expectation minimization algorithm NPs in the printing industry Pulmonary toxicity [27] Apriori algorithm Nanoparticulate aerosol Systems toxicology meta-analysis [28] Conductive metal NPs SEM analytic tool [29] Decision tree Poly amido amine dendrimers Cytotoxicity, prediction as cell viability considered as a binary variable, toxic/nontoxic NPs in human colorectal cancer cells [23] 21 different NPs Classify nanomaterials [30] Engineered nanomaterials (ENMs) Ecotoxicity of ENMs [31] k-nearest neighbors Q-dots and FeOx NPs Cellular uptake of cross-linked iron oxide NPs [25,32] 3. Theoretical and Computational Ab Initio Tools to Address Safer Bio-Nano Materials…”

Engineering the pH-Sensitivity of the Graphene and Carbon Nanotube Based Nanomedicines in Smart Cancer Therapy by Grafting Trimetyl Chitosan

“…It is well established that simulations can be highly useful in developing novel and smart nanomedicine systems. In this regard, many efforts have been devoted to exploiting Molecular Dynamics (MD) (31)(32)(33)(34)(35), Density Functional Theory(DFT) (36), and Machine Learning (37)(38)(39) in discovering emerging and smart drug nanocarriers. Shariatinia and Mazloom-Jalali (40) perused the graphene containing chitosan as a carrier of anticancer ifosfamide drug through MD, in which, the N-doped graphene/chitosan was suggested as an effective drug carrier.…”

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data

“…This means several assumptions on metadata similarity or expansion of the dataset with relevant metadata is needed to identify statistical similarity or significant difference [ 124 , 125 , 126 , 127 , 128 , 129 , 130 ]. As a result, the currently existing databases do not meet the needs of the modelling community, which has led to the emergence of computational databases, allowing capture of computational descriptors and direct application of modelling tools [ 157 ]. For example, the NanoSolveIT project is developing a modelling-dedicated database through the Enalos cloud platform.…”