  Deoxyribonucleic acid (DNA) has shown great promise in enabling computational
applications, most notably in the fields of DNA data storage and DNA computing.
The former exploits the natural properties of DNA, such as high storage density
and longevity, for the archival of digital information, while the latter aims
to use the interactivity of DNA to encode computations. Recently, the two
paradigms were jointly used to formulate the near-data processing concept for
DNA databases, where the computations are performed directly on the stored
data. The fundamental, low-level operation that DNA naturally possesses is that
of hybridisation, also called annealing, of complementary sequences.
Information is encoded as DNA strands, which will naturally bind in solution,
thus enabling search and pattern-matching capabilities. Being able to control
and predict the process of hybridisation is crucial for the ambitious future of
the so-called Hybrid Molecular-Electronic Computing. Current tools are,
however, limited in terms of throughput and applicability to large-scale
problems.
  In this work, we present the first comprehensive study of machine learning
methods applied to the task of predicting DNA hybridisation. For this purpose,
we introduce a synthetic hybridisation dataset of over 2.5 million data points,
enabling the use of a wide range of machine learning algorithms, including the
latest in deep learning. Depending on the hardware, the proposed models provide
a reduction in inference time ranging from one to over two orders of magnitude
compared to the state-of-the-art, while retaining high fidelity. We then
discuss the integration of our methods in modern, scalable workflows. The
implementation is available at:
https://github.com/davidbuterez/dna-hyb-deep-learning
