Abstract
A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.
Highlights
Precise manipulation and control of the structure of matter on the nanoscale brings an opportunity to design nanomaterials (ENMs) that are safe for humans and the environment in addition to obtaining their maximal efficiency in the context of the required application
We propose to replace the traditional Nano-QSAR modeling practice with the use of Structure–Activity Prediction Networks (SAPNets) – an approach that effectively links the description of ENMs’ structure with their toxicity through a series of layers built from nodes that correspond to predictive “meta-models” developed with machine learning techniques as well as Artificial Intelligence (AI) (Fig. 1)
The three presented case studies illustrate the high potential of SAPNets to serve as a valuable tool for developing new nanoparticles in line with the idea of safe-by-design
Summary
Precise manipulation and control of the structure of matter on the nanoscale brings an opportunity to design nanomaterials (ENMs) that are safe for humans and the environment in addition to obtaining their maximal efficiency in the context of the required application. The other variables (electrochemical equivalent, 2nd ionization potential, covalent radius, and thermal conductivity) can be considered as properties that are consequences of the structure Another example is the model for the prediction of zeta potential based on grouping of the NMs according to their nearest neighbors developed by Varsou et al.[5] The model utilizes three variables: the type of the core (metal oxide or pure metal), the main elongation expressing the lengthening of the particle, and the pH where the zeta potential was measured.[5] There are models based on quasi-SMILES, which are character-based representations derived from traditional SMILES.[6] They can encode the structural features, the physicochemical properties, and the exposure conditions such as cell lines.[7,8,9,10,11] Another way to take into account factors such as changes in chemical compositions, assay organisms, or exposure time is to apply the QSAR-perturbation approach.[12,13] All of the mentioned models are very useful tools.
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