Background
RET (Rearranged during Transfection) is a proto-oncogene that codes for a receptor tyrosine kinase. This means it produces a protein that plays a role in signaling pathways within cells, particularly related to cell growth and differentiation. When activated, RET helps regulate cell survival, proliferation, and differentiation. Mutations or alterations in the RET gene can lead to uncontrolled cell growth and potentially the development of cancer.
Benchmarking
- RET wild type: In some cases, targeting both mutant and wild-type RET together can be more effective than targeting only one form as Combination Therapies. In certain cancer types, such as some subtypes of non-small cell lung cancer (NSCLC), the RET signaling pathway can interact with other oncogenic pathways, such as the EGFR (epidermal growth factor receptor) pathway. Targeting both pathways simultaneously might offer a synergistic effect and improve treatment outcomes.
The goal of this benchmark is to perform a single task, which is to the best predictive model for
- Optimization of the bioactivity % inhibition for RET wile type.
- Discovery of potential hits in new chemical space.
Description of readout
- Readouts:
RET
- Bioassay readout: percentage of inhnibition.
- Optimization objective: Higher value
- Number of data points: train: 534 test: 106
Data resource:
Train/test split
Given the benchmarking goal, a scaffold-based splitting approach was applied to ensure training and test sets contain distinct chemical structures while maintaining the diversity of scaffolds.
Distribution of the train/test in the chemical space
Related links
The full curation and creation process is documented -> notebook
Related benchmarks
- polaris/drewry_ret_wildtype_singletask_clf_v1
- polaris/drewry_ret_wt_v804l_y791f_multitask_reg_v1
- polaris/drewry_ret_wt_v804l_y791f_multitask_clf_v1
Note: It's recommanded to evaluate your methods agaisnt all the benchmarks related to this dataset.
