Details

Autor(en) / Beteiligte

• Luukkonen, Sohvi
• Meijer, Erik
• Tricarico, Giovanni A.
• Hofmans, Johan
• Stouten, Pieter F. W.
• van Westen, Gerard J. P.
• Lenselink, Eelke B.

Titel

Large-Scale Modeling of Sparse Protein Kinase Activity Data

Ist Teil von

• Journal of chemical information and modeling, 2023-06, Vol.63 (12), p.3688-3696

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• Protein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP binding sites, which when targeted can lead to similar activities of inhibitors against different kinases. This can be exploited to create multitarget drugs. On the other hand, selectivity (lack of similar activities) is desirable in order to avoid toxicity issues. There is a vast amount of protein kinase activity data in the public domain, which can be used in many different ways. Multitask machine learning models are expected to excel for these kinds of data sets because they can learn from implicit correlations between tasks (in this case activities against a variety of kinases). However, multitask modeling of sparse data poses two major challenges: (i) creating a balanced train–test split without data leakage and (ii) handling missing data. In this work, we construct a protein kinase benchmark set composed of two balanced splits without data leakage, using random and dissimilarity-driven cluster-based mechanisms, respectively. This data set can be used for benchmarking and developing protein kinase activity prediction models. Overall, the performance on the dissimilarity-driven cluster-based split is lower than on random split-based sets for all models, indicating poor generalizability of models. Nevertheless, we show that multitask deep learning models, on this very sparse data set, outperform single-task deep learning and tree-based models. Finally, we demonstrate that data imputation does not improve the performance of (multitask) models on this benchmark set.