Towards the development of machine learning models to predict protein-protein interaction modulators

16 May 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Protein-protein interaction (PPI) inhibitors have a continued and increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms are able to classify or identify PPI inhibitors. In this work, we describe the performance of different algorithms broadly used in chemoinformatics to develop a classification model able to identify PPI inhibitors based on structural and physicochemical descriptors. We found that the classification algorithms have different performance according to different features employed in the training process: random forest (RF) models with the extended connectivity fingerprint radius 4 (ECFP4) had the best classification performance as compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for linear regression. ECFP4 also yielded models with high performance metrics, in particular, with support vector machine (SVM). As part of this work, we constructed ensemble models based on the top-performing models. The pipeline code developed in this work and all results are freely available at


computer-aided drug design
drug discovery
machine learning
protein-protein interaction


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.