  Machine learning has been widely verified and applied in chemoinformatics,
and have achieved outstanding results in the prediction, modification, and
optimization of luminescence, magnetism, and electrode materials. Here, we
propose a deepth first search traversal (DFST) approach combined with lightGBM
machine learning model to search the classic Organic field-effect transistor
(OFET) functional molecules chemical space, which is simple but effective.
Totally 2820588 molecules of different structure within two certain types of
skeletons are generated successfully, which shows the searching efficiency of
the DFST strategy. With the simplified molecular-input line-entry system
(SMILES) utilized, the generation of alphanumeric strings that describe
molecules directly tackle the inverse design problem, for the generation set
has 100% chemical validity. Light Gradient Boosting Machine (LightGBM) model's
intrinsic Distributed and efficient features enables much faster training
process and higher training efficiency, which means better model performance
with less amount of data. 184 out of 2.8 million molecules are finally screened
out with density functional theory (DFT) calculation carried out to verify the
accuracy of the prediction.
