Polymers, with the capacity to tunably alter properties and response based on manipulation
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …

Molecular representations have essential roles in bio-cheminformatics as they facilitate the
growth of machine learning applications in numerous sub-domains of biology and chemistry …

The complexity and diversity of polymer topologies, or chain architectures, present
substantial challenges in predicting and engineering polymer properties. Although machine …

The prediction of a variety of polymer properties from their monomer composition has been a
challenge for material informatics, and their development can lead to a more effective …

Establishing the structure–property relationship by machine learning (ML) models is
extremely valuable for accelerating the molecular design of polymers. However, existing ML …

The BigSMILES notation, a concise tool for polymer ensemble representation, is augmented
here by introducing an enhanced version called generative BigSMILES. G-BigSMILES is …

Molecular generative models based on deep learning have increasingly gained attention for
their ability in de novo polymer design. However, there remains a knowledge gap in the …

Defining the similarity between chemical entities is an essential task in polymer informatics,
enabling ranking, clustering, and classification. Despite its importance, the pairwise …

One of the biggest obstacles to successful polymer property prediction is an effective
representation that accurately captures the sequence of repeat units in a polymer. Motivated …

The development of new polymer materials is an emerging field for more than 100 years.
However, it is currently facing major challenges and the application of digital methods can …