  In the present paper we evaluated efficiency of the recent Transformer-CNN
models to predict target properties based on the augmented stereochemical
SMILES. We selected a well-known Cliff activity dataset as well as a Dipole
moment dataset and compared the effect of three representations for R/S
stereochemistry in SMILES. The considered representations were SMILES without
stereochemistry (noChiSMI), classical relative stereochemistry encoding
(RelChiSMI) and an alternative version with absolute stereochemistry encoding
(AbsChiSMI). The inclusion of R/S in SMILES representation allowed simplify the
assignment of the respective information based on SMILES representation, but
did not always show advantages on regression or classification tasks.
Interestingly, we did not see degradation of the performance of Transformer-CNN
models when the stereochemical information was not present in SMILES. Moreover,
these models showed higher or similar performance compared to descriptor-based
models based on 3D structures. These observations are an important step in NLP
modeling of 3D chemical tasks. An open challenge remains whether
Transformer-CNN can efficiently embed 3D knowledge from SMILES input and
whether a better representation could further increase the accuracy of this
approach.
