Message-passing neural network based multi-task deep-learning framework for COSMO-SAC based σ-profile and VCOSMO prediction

Abstract

• A message-passing neural network based feedforward neural network (MPNN-FNN) is developed for σ-profile and V COSMO prediction. • Hybrid molecular representations are utilized to extract the molecular features. • The proposed model can well discriminate the cis/trans and structural isomers. • The IDAC calculated by predicted σ -profile and V COSMO is very close to the QM derived ones. The correct implementation of the quantum mechanics (QM) calculation for COSMO-SAC based surface charge density profiles ( σ -profile) and cavity volumes ( V COSMO ) is tricky and time-consuming. As a shortcut, the GC approaches (GC-COSMO) were proposed to predict the required σ -profile and V COSMO for COSMO based methods. However, the GC-COSMO model cannot discriminate the isomers and cannot predict the complex molecules due to a lack of diverse functional groups. In this context, a message-passing neural network based feedforward neural network (MPNN-FNN) is proposed for rapid and accurate predicting COSMO-SAC based σ -profile and V COSMO . The hybrid molecular representation integrated the message-passing neural network (MPNN) learned representations and 200 additional molecule-level RDKit features, which can capture both local and global features of the overall molecule, is utilized to develop a better molecular feature extraction model. Based on the hybrid molecular representations, a feedforward neural network (FNN) model for the σ -profile and V COSMO prediction is trained and tested on the VT-2005 database. The developed MPNN-FNN model demonstrates significantly better performance than the GC-COSMO method and can well discriminate the cis/trans and structural isomers. In addition, the calculation speed based on the proposed model for COSMO-SAC based infinite dilution activity coefficient (IDAC) reaches about 100 compounds per second, which are the prerequisite to performing a high throughput green solvent screening.