Transfer Learning for Drug Discovery

The data sets available to train
models for in silico drug discovery efforts are often
small. Indeed, the sparse availability
of labeled data is a major barrier to artificial-intelligence-assisted
drug discovery. One solution to this problem is to develop algorithms
that can cope with relatively heterogeneous and scarce data. Transfer
learning is a type of machine learning that can leverage existing,
generalizable knowledge from other related tasks to enable learning
of a separate task with a small set of data. Deep transfer learning
is the most commonly used type of transfer learning in the field of
drug discovery. This Perspective provides an overview of transfer
learning and related applications to drug discovery to date. Furthermore,
it provides outlooks on the future development of transfer learning
for drug discovery.