Density, viscosity, surface tension, excess properties and molecular interaction of diethylene glycol (1) + 1,2-propanediamine (2) at atmospheric pressure and T = 293.15 K ∼ 318.15 K

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.

Radial distribution and hydrogen bonded network graphs of alcohol-aniline binary mixture

CONTEXT

Hydrogen bonds play a vital role in the stability and functioning of biomolecules. Suitable binary liquids are often used as prototypes for the study of biologically significant hydrogen bond studies and their intricate networks. Often, such systems show deviations in their physico-chemical properties from ideal conditions. As a continuation of our research interest in biologically important hydrogen-bonded systems, this paper reports the classical molecular dynamic studies on mixtures of aniline with 8 primary alcohols (C_RH_2R+1-OH, R = 1 to 8) for the complete concentration range. The energetics results indicate the predominance of OH--O interactions over other hydrogen bonds. Structures in the network are analyzed using radial distribution function (RDF), hydrogen bond statistics, and graph theoretical analysis (GTA). Coordination numbers, hydrogen bond statistics, and GTA show a bunching of alcohol-alcohol hydrogen bonds for lower aniline concentrations, while the aniline-aniline interactions are not affected by changes in the concentration.

METHODS

Interaction energies are calculated using B3LYP/6-311G++(d, p) density functional theory using Gaussian-09. The molecular dynamics simulations are carried out using GROMACS (V 2020.6) with the OPLS/AA force field and the simulation box is visualized using VMD. The NetworkX Python package is used for GTA calculation.