(159d) Atom surface fragment contribution method for predicting the toxicity of ionic liquids

Ionic liquids (ILs) have become a paramount research subject in modern chemistry and rapid progress has been achieved in the recent decades [1]. Due to the tunable physicochemical properties, approximately 10¹⁸ ILs with different combinations of anions and cations can be synthesized [2]. Therefore, many linear and nonlinear models for predicting the related properties of ILs were established by various methods, such as group contribution (GC), machine learning methods [3,4]. However, some deficiencies and disadvantages evidently exist in the traditional GC models. The main drawbacks of GC include that only the types and frequencies of the groups are considered and the interactions of groups or neighbor groups are not descripted [5] .

In this study, a novel method—atom surface fragment contribution (ASFC)—was proposed for assessing the properties of compounds. We developed a predictive model using ASFC method based on the sigma surface areas (S_σ-surface) of fragments/groups for estimating the toxicity of ILs. A toxicity dataset of 140 ILs towards leukemia rat cell line (ICP-81) was gathered and employed to train and validate models. The S_σ-surface values of atoms in each group were firstly calculated from the COSMO profiles of cations and anions for ILs. Then the S_σ-surface values of 26 groups were obtained and used as input descriptors for modelling. The R² and MSE of the built ASFC model were 0.924 and 0.071, respectively. Results indicates that the ASFC model developed by the new approach possesses great accuracy and reliability. In total, the ASFC method has extensive potential for the application of estimating diverse properties of ILs and other compounds due to its remarkable advantages.

Keywords: atom surface fragment contribution, sigma surface area, group contribution, ionic liquids, toxicity

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