Decoding Interaction Patterns from the Chemical Sequence of Polymers Using Neural Networks

The relation between
chemical sequences and the properties of polymers
is considered using artificial neural networks with a low-dimensional
bottleneck layer of neurons. These encoder–decoder architectures
may compress the input information into a meaningful set of physical
variables that describe the correlation between distinct types of
data. In this work, neural networks were trained to translate a sequence
of hydrophilic and hydrophobic segments into the effective free energy
landscape of a copolymer interacting with a lipid membrane. The training
data were obtained by the sampling of coarse-grained polymer conformations
in a given membrane density field. Neural networks that were split
into separate channels have learned to decompose the free energy into
independent components that are explainable by known concepts from
polymer physics. The semantic information in the hidden layers was
employed to predict polymer translocation events through a membrane
for a more detailed dynamic model via a transfer learning procedure.
The search for minimal translocation times in the compressed chemical
space underlined that nontrivial sequence motifs may lead to optimal
properties.