Evaluation of gene expression and phenotypic profiling data as quantitative descriptors for predicting drug targets and mechanisms of action
Lapins, M.; Spjuth, O.
Show abstract
Profiling drug leads by means of in silico and in vitro assays as well as omics is widely used in drug discovery for safety and efficacy predictions. In this study, we evaluate the performance of machine learning models trained on data from gene expression and phenotypic profiling assays, with models trained on chemical structure descriptors, for prediction of various drug mechanisms of action and target proteins. Models for several hundred mechanisms of actions and targets were trained using data on 1484 compounds characterized in both gene expression using L1000 profiles, and phenotypic profiling with cell painting assay. The results indicate that the accuracy of the three profiling technologies varies for different endpoints, and indicate a clear potential synergistic effect if these methods are combined. We also study the effect of predictive accuracy of data from different cell lines for L1000 profiles, showing that the choice of cell line has a non-negligible effect on the predictive accuracy. The results strengthen the idea of integrated approaches for predicting drug targets and mechanisms of action in preclinical drug discovery.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Distinguishing classes of neuroactive drugs based on computational physicochemical properties and experimental phenotypic profiling in planarians 95%
- Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor 95%
- PharmaNet: Pharmaceutical discovery with deep recurrent neural networks. 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.