Structural attributes driving λmax towards NIR region: A QSPR approach

Abstract

Near-infrared materials find extensive applications in bio-sensing, photodynamic treatment, anti-counterfeiting and opto-electronics. Their progress has notably expanded possibilities in optical communication systems, non-invasive imaging and targeted therapy, benefiting fields such as material science, medicine, tele-communication and biology. In light of these advancements, developments of near-infrared region (NIR) based probes are highly desirable. Moreover, the prediction of the optical properties of a compound prior to its synthesis can diminish the need for expensive experimental testing. Considering the importance of prior prediction, we herein present QSPR models for the prediction of absorption maxima using a dataset of 384 compounds. The aim of the present study is to identify molecular features that could shift their ${\lambda }_{\max }$ in the near-infrared region. The Monte Carlo Optimization approach along with the index of ideality of correlation (TF₂) has been utilized using CORAL 2019 software for the development of ten splits. The predictability of the resulting ten models was assessed using various validation metrics. The model derived from the tenth split proved to be efficient, exhibiting $R_{Validation}^2=0.8561$, $IIC=0.7849and{Q}^2=0.8512$. Good and bad fragments were also identified that are responsible for the change in absorption maxima (${\lambda }_{\max }$). Identified fragments were utilized for designing ten new molecules to evaluate their reliability. It was observed that molecules designed using positive attributes shifted the absorption maxima towards the near-infrared region, specifically between 711 and 893 nm. This study opens up new possibilities for the advancement of NIR-based chromophores and will contribute significantly by reducing the overall cost of chromophore development.