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.