Monte Carlo optimization based QSAR modeling of the cytotoxicity of acrylic acid-based dental monomers

Abstract

Acrylic acid derivatives are extensively utilized as initial monomers in dental materials. Nevertheless, these substances exhibit cytotoxicity towards different cell types, a phenomenon that must be reduced in future materials. The primary objective of this research is to establish a QSAR model for the prediction of cytotoxic effects and to identify molecular fragments and descriptors with mechanistic interpretations that play a role in cytotoxic effects. The Monte Carlo optimization technique was employed QSAR models that are not reliant on conformation. These models utilized both molecular graph-based and SMILES-based descriptors. By employing a variety of statistical methodologies, an assessment of the predictive capabilities and resilience of the established QSAR models was achieved. The demonstrated numerical values used for their validation underscore the strong suitability of these QSAR models. The Monte Carlo optimization technique effectively identified molecular fragments represented in QSAR modeling through the use of SMILES notation, elucidating their impact on cytotoxicity, both positively and negatively. Given that the majority of molecular databases adhere to this molecular structure conformation, the featured QSAR models can serve as a rapid and precise screening tool for novel dental monomers.