Machine learning and automation for data-driven design of polymer biomaterials and bioformulations
Creators: name: Tamasi, Matthew
Publisher: name: No Publisher Supplied
Year: 2025

description: Synthetic polymers offer exemplary materials for a wide range of applications in drug delivery, tissue engineering, protein stabilization, and mimicry of biologic molecules. This enormous potential of synthetic polymers ultimately stems from polymers capacity for chemical and architectural diversity as they have many tunable parameters including chemical composition, chain length, valency, sequence, tacticity, and architecture. While these represent the properties of a single polymer, additional modulation and diversity is achieved by post polymerization modifications such as functionalization or conjugation. Currently, methods for designing polymer biomaterials involve either a rational design approach based on human expertise and experience, high-throughput (HTP) screening experiments, or a combination of both. Rational design relies on scientists' knowledge and experience to predict optimal chemical properties for a specific goal. However, this approach can be difficult, as it demands extensive knowledge of polymerization chemistry to anticipate complex interactions and synergies that influence the behavior of these complex macromolecules. Further, much of the available chemistries for polymers remain un-explored as the chemical diversity of synthetic polymers is practically infinite. Thus, techniques and approaches that enable HTP and efficient synthesis of polymers, as well as strategies to efficiently explore polymer designs are hypothesized to be critical in identifying synthetic polymers for advanced material applications. In this dissertation, we hypothesize that the pairing of high throughput automated polymer synthesis with machine learning strategies can be utilized to efficiently perform data-driven design of polymer biomaterials. To support this hypothesis, we propose the idea of a Biomaterials Acceleration Platform (BioMAP) in which laboratory robotics and automation is coupled with machine learning (ML) and artificial intelligence (AI) to optimize biomaterials and bioformulations in an efficient, high-throughput, and potentially fully autonomous manner. While a full BioMAP remains unrealized in the field, here we have both developed a platform for autonomous high-throughput polymer synthesis and combined this synthetic capacity with machine learning strategies to optimize polymer designs for enzyme stabilizing polymer-protein hybrids (PPHs) and the design of single chain nanoparticles (SCNPs), two highly complex and diverse biomaterial challenges.Our first chapter involves developing automated tool to develop polymer materials. In biological and medical contexts, polymers have exhibited remarkable utility in drug delivery systems, tissue engineering, and protein stabilization, underscoring the importance of elucidating the intricate relationships between polymer structure, composition, and function. To this end, HTP techniques have emerged as a powerful tool for deconvoluting the complex relationships governing material behavior, enabling the discovery of novel materials and mechanisms. However, high-throughput synthesis of polymers has been historically limited by the oxygen intolerance of most polymerization chemistries. Reactions generally required large sample volumes and inert atmospheres provided by glove boxes, sparging with inert gases such as argon and nitrogen, or freeze-pump-thaw cycling. With the recent advent of oxygen tolerant polymerization techniques that provide chemical mechanisms to sequester and consume oxygen, it has become possible to perform polymer chemistries at small volumes (&lt;300 µL) and in some cases in open-vessels. Leveraging these discoveries here, we: (1) adapted a Hamilton MLSTARlet liquid handling robot to carry out oxygen tolerant photoinduced electron/Energy transfer reversible addition–fragmentation chain transfer (PET-RAFT) and enzyme-assisted reversible addition-fragmentation chain transfer (Enz-RAFT) by custom python scripts, and (2) demonstrate the capacity to perform multi-step synthesis such as block-copolymer synthesis and automated postpolymerization functionalization.The second chapter of this dissertation is to leverage this capacity for HTP automated polymer synthesis to design highly stable polymer-protein hybrids (PPHs). While a myriad of approaches have been used to stabilize proteins; abiotic environments where proteins are exposed to extreme temperatures, organic solvents, or pHs remain largely challenging domains to maintain protein structure and activity within. Polymer-protein hybrids in which a copolymer is tailored to protein surface chemistry, have demonstrated remarkable success in this regard with examples such as retaining hours-long enzyme activity in pure solvents such as toluene. However, identifying these polymers whether by rational design or high-throughput screening is challenging due to the near infinite combinatorial design space. In this chapter we investigated coupling automated high throughput polymer synthesis with active machine learning (ML) to drive the design of enzyme specific-PPHs. Utilizing a “Design-Build-Test-Learn” strategy, we iteratively synthesized polymer candidates, characterized their ability to maintain enzyme activity after thermal stress, and trained machine learning models on the resulting data to drive the design of new candidate polymers. Remarkably, for every enzyme evaluated we: (1) identified high performing PPHs capable preventing significant denaturation above enzyme melting temperatures, (2) identified unique polymer chemistry preferences required for stability in each enzyme case, (3) obtained quantitative structure activity relationships by SHAP, and (4) observed chaperone refolding behavior of key PPHs through biophysical characterization.The third chapter of this dissertation focuses on developing a data-driven approach to improve the efficiency of identifying single chain polymer nanoparticles (SCNPs). Single-chain polymer nanoparticles are synthetic molecules that can self-assemble into complex structures, similar to proteins. SCNPs have gained significant interest in the polymer science community as drug delivery vehicles, diagnostic and biosensing materials, anti-bacterial applications, or as biological mimetics for proteins and enzymes. However, given the complexity of SCNP design in which polymers must undergo hydrophobic collapse and remain stable through charge-charge interactions, hydrogen bonding, steric, and Vander walls forces, rational design is extremely challenging. Therefore, in this chapter, we hypothesized that HTP automated polymerization and machine learning could be utilized to learn SCNP structural design features. Leveraging HTP automated PET-RAFT, we synthesized a set of over 1000 polymers and polymer conjugates, and performed HTP biophysical characterization through dynamic light scattering (DLS) and small angle x-ray scattering (SAXS). Combining this data with polymer chemical featurizations, we then trained evidential neural networks (ENNs), a probabilistic machine learning model to learn key parameters for SCNP structural design, including Porod exponent and Rg. After model training, we attempted to predict synthesizable polymer compositions with significant compactness (Rg/Rh &lt; 0.80) and flexibility (Porod exponent between 3.0-4.0). This approach successfully identified 10 novel SCNPs that exhibited high compactness and flexibility, out of 30 synthesized polymers used to validate the model.In conclusion, this work encompasses initial case studies on how the combination of HTP automated polymer synthesis and machine learning strategies can be used for tailored biomaterial development. Advances here have demonstrated the potential to use these techniques to efficiently discover polymers as effective protein stabilizers, and compact SCNPs. Further, beyond simply identification of materials, these approaches have enabled us to uncover design rules specific to each biomaterials challenge. In the future, these methods could be expanded to accelerate research across a wide berth of biomaterial and bioformulations challenges.