Multiplet-based fingerprint mapping has been used to analyse the relationship between the structural features of potential drug candidates and the enzyme LRRK2 inhibition expressed as the inhibition constant (pKi). For 198 structurally diverse compounds 4195 dimensional fingerprints were generated and mathematically manipulated using partial least squares (PLS) regression. A variation of PLS-BETA technique was developed for the reduction of noise by eliminating excess variables that resulted in a 636 dimensional fingerprint related to pKi. The QSAR model for the training set of 170 compounds (R2=0.87, Q2=0.77 and SDEC=0.42) had four latent variables (PLS components) and it was validated by the external test set of 28 compounds (Qext2=0.63). The proposed model of LRRK2 inhibitory activity can be helpful in designing focused libraries enriched in LRRK2 inhibitors and identifying new active chemotypes in compound databases.