FTIR fingerprint — testing a new representation of the binary fingerprint based on FTIR spectra in the prediction of physicochemical properties

Abstract

The paper deals with the development of a new method for the generation of binary fingerprints based on the Savitzky-Golay (SG) algorithm and first-order derivatives of FTIR spectra, which are then used to create prediction models for selected the physicochemical properties of chemical compounds. Models based on the FEDS (Functionally-Enhanced Derivative Spectroscopy) transformation and raw spectra were used as a reference to determine whether the use of the SG filter and first-order derivatives was worth to further develop. The FTIR spectra of 103 compounds with theoretically determined values of logP, logD and logS were studied. The Tanimoto coefficient and correlation coefficient were used to compare the fingerprints obtained, while the root mean square error (RMSE) was used to assess the quality of the prediction models. Based on the results, it was found that the use of the SG filter and derivatives had a positive effect on the quality of the prediction models for logP and logS, and a negative effect on the quality of the models for logD, compared to the models based on original spectra and FEDS transformation.