Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

Protein-Based Hydrogels and Their Biomedical Applications

Hydrogels made from proteins are attractive materials for diverse medical applications, as they are biocompatible, biodegradable, and amenable to chemical and biological modifications. Recent advances in protein engineering, synthetic biology, and material science have enabled the fine-tuning of protein sequences, hydrogel structures, and hydrogel mechanical properties, allowing for a broad range of biomedical applications using protein hydrogels. This article reviews recent progresses on protein hydrogels with special focus on those made of microbially produced proteins. We discuss different hydrogel formation strategies and their associated hydrogel properties. We also review various biomedical applications, categorized by the origin of protein sequences. Lastly, current challenges and future opportunities in engineering protein-based hydrogels are discussed. We hope this review will inspire new ideas in material innovation, leading to advanced protein hydrogels with desirable properties for a wide range of biomedical applications.

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

LGB-Stack: Stacked Generalization with LightGBM for Highly Accurate Predictions of Polymer Bandgap

Recently, the Ramprasad group reported a quantitative structure-property relationship (QSPR) model for predicting the E _gap values of 4209 polymers, which yielded a test set R ² score of 0.90 and a test set root-mean-square error (RMSE) score of 0.44 at a train/test split ratio of 80/20. In this paper, we present a new QSPR model named LGB-Stack, which performs a two-level stacked generalization using the light gradient boosting machine. At level 1, multiple weak models are trained, and at level 2, they are combined into a strong final model. Four molecular fingerprints were generated from the simplified molecular input line entry system notations of the polymers. They were trimmed using recursive feature elimination and used as the initial input features for training the weak models. The output predictions of the weak models were used as the new input features for training the final model, which completes the LGB-Stack model training process. Our results show that the best test set R ² and the RMSE scores of LGB-Stack at the train/test split ratio of 80/20 were 0.92 and 0.41, respectively. The accuracy scores further improved to 0.94 and 0.34, respectively, when the train/test split ratio of 95/5 was used.