biostudies-literatureDiaz-Eufracio BIAustrian Science Fund FWF7986https://www.ebi.ac.uk/biostudies/studies/S-EPMC9694076biostudies-literatureUnknown27(22)Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCSMolecules (Basel, Switzerland)Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.PMC9694076P 29275Diaz-Eufracio BIMedina-Franco JLfalseMachine Learning Models to Predict Protein-Protein Interaction Inhibitors.Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS2022-01-01T00:00:00Z2022 Nov2025-04-19T00:35:17.274Z2025-04-07T11:39:15.024ZS-EPMC96940763643208610.3390/molecules27227986