Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning.
Mahendra Awale1, Jean-Louis Reymond1
1Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure , University of Berne , Freiestrasse 3 , 3012 Berne , Switzerland.
Journal of Chemical Information and Modeling
|December 19, 2018
Summary
PPB2 is a new tool that predicts drug targets using molecular fingerprints and machine learning. Combining nearest neighbor searches with Naive Bayes machine learning offers superior precision for predicting potential off-targets.
Area of Science:
- Computational chemistry
- Cheminformatics
- Drug discovery
Background:
- Accurate prediction of drug targets is crucial for efficient drug discovery.
- Existing methods may lack precision in identifying potential off-targets.
Purpose of the Study:
- To introduce PPB2, a novel target prediction tool.
- To evaluate the performance of different computational approaches for target prediction.
Main Methods:
- Utilized ChEMBL database for target assignment.
- Employed molecular fingerprints (MQN, Xfp, ECfp4) for ligand similarity.
- Implemented nearest neighbor (NN) searches, Naive Bayes (NB), and Deep Neural Network (DNN) models.
Main Results:
- Nearest Neighbor with ECfp4 (NN(ECfp4)) showed the best recall in cross-validation.
- A combination of NN searches and NB machine learning demonstrated superior precision.
- The combined approach excelled in predicting off-targets for a TRPV6 calcium channel inhibitor.
Conclusions:
- PPB2 offers a valuable approach for predicting drug targets and off-targets.
- The combined NN and NB method enhances prediction precision.
- PPB2 is publicly accessible for assessing small molecule drug-like compounds.
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