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Top1. Introduction
The advancement of nanotechnology in the past decades has led to the extensive use of nanomaterials in the area of environment, energy and biomedicine (L. Hu et al., 2017). Functionalized nanomaterials like semiconductor quantum dots (QDs) are thought to be as new type of ideal fluorescent probes because of their intrinsic optical characteristics like long-term photostability, high photoluminescence quantum yields, as well as size-tunable and narrow-band emissions (Zhao et al., 2019). QDs have attracted substantial attention due to their fantastic applications in biomedical fields like bioimaging, biosensing, cancer diagnostic and drugs (L. Hu et al., 2017; Tang et al., 2008; Xu et al., 2012). Their small size (1 – 10 nm) is suitable for detection and tracking of biological compounds and cell organelles inside the cell as well as in the nucleus (Filali et al., 2020).
Despite wide range of interesting applications, toxicity of QDs is a major bottleneck in their use in biomedical applications. It has been shown that QDs are cytotoxic in nature and cause dysfunction of mitochondria, cell growth inhibition, DNA impairment and apoptosis (Luo et al., 2013; Nagy et al., 2012; Wan et al., 2015). Cadmium containing QDs are reported to be toxic towards renal cells. L-cysteine-capped CdTe-QDs increase cytotoxicity and change the structure and bioactivity of copper–zinc superoxide dismutase in mouse primary nephrocytes (Trabelsi et al., 2013). Soluble CdS and ZnO QDs exhibit cytotoxic effects towards HK-2 human epithelial tubular cells in a dose dependent manner (Pujalté et al., 2015; Zhao et al., 2019). It has been reported that properties like size, surface charge, surface ligand, shell, concentration, assay type etc. influence the cytotoxicity of QDs (X. Hu et al., 2011; L. Hu et al., 2016; Rivera-Gil et al., 2013).
These facts indicate that changes in physicochemical properties of QDs can alter their toxicity (Schwarz-Plaschg et al., 2017) and thus the toxicity can be minimized during very initial phase of QDs synthesis by controlling the physicochemical parameters. Further, assessment of toxicity of QDs is very tedious and expensive task. Thus, there is requirement of applications of in silico alternatives for prediction of toxicity associated with QDs.
Computational techniques like quantitative structure/feature-activity/property/toxicity relationship (QSAR/QSPR/QFTR) can provide solution to the above problems (Davim & Roy, 2015). These are some of the most prominent methods used very often in the design of more effective and safer molecules (Muratov et al., 2020). In QSAR process, the structural information of the compounds is used in prediction of endpoint using some mathematical operations (Cherkasov et al., 2014). The structures of the molecules are quantitated using 1D, 2D and 3D descriptors calculated using specialized tools (Lo et al., 2018). Further, chemical reactivity descriptors such as nucleophilicity index, hardness, polarizability, compressibility, etc. are also used in constructing predictive QSAR models (Sarkar et al., 2012; Tandon et al., 2019, 2019, 2021). These are common methods for traditional substances. In case of nano materials, this classical approach has been applied but that is related with the non-nano components of the nanomaterials (Fourches et al., 2010).