Evolving Data‐Driven Strategies for the Characterization of Supramolecular Polymers and Systems

Inspired by the dynamic assembly of fibrillar proteins in biology, research on supramolecular polymers has progressed rapidly toward the development of synthetic multicomponent systems. In this review, we highlight recent advances in the study of supramolecular polymers in solution, with an emphasis on how combined computational and experimental approaches deepen our understanding of these systems. In particular, these studies have elucidated the mechanisms of protein aggregation and provided insights into the characteristics of synthetic systems. We discuss the classification of these polymers and systems, highlighting how their different interaction modes and microstructures give rise to diverse structural and functional properties. In addition, we outline the emerging role of machine learning as a powerful tool to navigate the inherent complexity of these systems, thereby enhancing strategies for rational design and characterization. This review highlights how computational approaches, from traditional modeling to emerging machine learning techniques, enable the experimental characterization and understanding of supramolecular polymer chemistry.

Recent years have witnessed an increasing interest in hybrid molecular systems in which the programmability of DNA hybridization is used to introduce enhanced molecular control in synthetic systems. The first examples of DNA-functionalized supramolecular polymers have been reported only recently, but have already revealed structural and functional properties that are not easily obtained in either synthetic supramolecular polymers or DNA-only based systems. In this Topical Review, we provide an overview of the various forms of additional control offered by DNA hybridization for different types of supramolecular polymers and discuss how orthogonal supramolecular interactions in these hybrid systems can give rise to emergent structural and functional properties.

Owing to the mastery exhibited by Nature in integrating both covalent and noncovalent interactions in a highly efficient manner, the quest to construct polymeric systems that rival not only the precision and fidelity but also the structure of natural systems has remained a daunting challenge. Supramolecular chemists have long endeavored to control the interplay between covalent and noncovalent bond formation, so as to examine and fully comprehend how function is predicated on self-assembly. The ability to reliably control polymer self-assembly is essential to generate "smart" materials and has the potential to tailor polymer properties (i.e., viscosity, electronic properties) through fine-tuning the noncovalent interactions that comprise the polymer architecture. In this context, supramolecular polymers have a distinct advantage over fully covalent systems in that they are dynamically modular, since noncovalent recognition motifs can be engineered to either impart a desired functionality within the overall architecture or provide a designed bias for the self-assembly process. In this Account, we describe engineering principles being developed and pursued by our group that exploit the orthogonal nature of noncovalent interactions, such as hydrogen bonding, metal coordination, and Coulombic interactions, to direct the self-assembly of functionalized macromolecules, resulting in the formation of supramolecular polymers. To begin, we describe our efforts to fabricate a modular poly(norbornene)-based scaffold via ring-opening metathesis polymerization (ROMP), wherein pendant molecular recognition elements based upon nucleobase-mimicking elements (e.g., thymine, diaminotriazine) or SCS-Pd(II) pincer were integrated within covalent monofunctional or symmetrically functionalized polymers. The simple polymer backbones exhibited reliable self-assembly with complementary polymers or small molecules. Within these systems, we applied successful protecting group strategies and template polymerizations to enhance the control afforded by ROMP. Main-chain-functionalized alternating block polymers based upon SCS-Pd(II) pincer-pyridine motifs were achieved through the combined exploitation of bimetallic initiators and supramolecularly functionalized terminators. Our initial design principles led to the successful fabrication of both main-chain- and side-chain-functionalized poly(norbornenes) via ROMP. Utilizing all of these techniques in concert led to engineering orthogonality while achieving complexity through the installation of multiple supramolecular motifs within the side chain, main chain, or both in our polymer systems. The exploitation and modification of design principles based upon functional ROMP initiators and terminators has resulted in the first synthesis of main-chain heterotelechelic polymers that self-assemble into A/B/C supramolecular triblock polymers composed of orthogonal cyanuric acid-Hamilton wedge and SCS-Pd(II) pincer-pyridine motifs. Furthermore, supramolecular A/B/A triblock copolymers were realized through the amalgamation of functionalized monomers, ROMP initiators, and terminators. To date, this ROMP-fabricated system represents the only known method to afford polymer main chains and side chains studded with orthogonal motifs. We end by discussing the impetus to attain functional materials via orthogonal self-assembly. Collectively, our studies suggest that combining covalent and noncovalent bonds in a well-defined and precise manner is an essential design element to achieve complex architectures. The results discussed in this Account illustrate the finesse associated with engineering orthogonal interactions within supramolecular systems and are considered essential steps toward developing complex biomimetic materials with high precision and fidelity.

Exciton-polarons in self-assembling helical aggregates: relating optical properties to supramolecular structure

ConspectusSince its inception in the early 1990s, the field of supramolecular polymers (SPs) has grown into an interdisciplinary field of chemistry. It expanded from the self-assembly of molecular building blocks based on H-bonding into the realm of complex dynamic material, encompassing both supramolecular noncovalent and molecular covalent regimes. It has paved the path for a more diverse field of research into a new class of polymeric materials, coined dynamic polymers or dynamers. Dynamers are bringing a paradigm shift not only in material science research but also in a broad field of applications from self-healing materials to biocompatible polymeric materials. The present Account presents the evolution of supramolecular polymer chemistry from simple linear polymeric chains to complex dynamic polymers imparting novel functional properties, such as component exchange and self-healing. We explore how SPs led to materials of increasing complexity, starting from simple main-chain polymers to the formation of more complex columnar SPs and lateral SPs. The field has experienced three partially overlapping periods. The main goal was first the generation of polymeric entities from various molecular components connected through noncovalent interactions, especially complementary hydrogen bonding recognition patterns as well as stacked columnar SPs. Thereafter, attention was directed in parallel to the exploration of the properties of SPs and their applications as novel materials. In a third period, the dynamic properties of supramolecular polymers were explored, taking advantage of the lability of noncovalent interactions to perform component rearrangement and exchange. We illustrate how the field of SPs has emerged as a multidisciplinary field of chemistry, biology, and materials science with selected examples from the literature. The SPs, specifically dynamic owing to their inherent reversibility, also pave the path to easier sorting and recycling, as desired in the plastics industry.One of the biggest challenges that the plastics industry is facing today is the end-of-life fate of plastics. Plastics that cannot be recycled end up in landfills or are improperly disposed of in rivers and oceans, polluting and damaging the environmental balance irreversibly. Dynamic polymeric materials presenting inherent dynamicity could pave the way for addressing this long-standing challenge of nonrecyclability of plastics. Dynamers formed via noncovalent interactions or reversible covalent bonds can be broken into components that could be easily recycled and reused. Therefore, dynamers could play a pivotal role toward closing the loop for the plastics industry and provide a solution to an elusive circular economy with plastics being an integral part.

Inspired by the vast array of assemblies present in nature, supramolecular chemistry has attracted significant attention on account of its diverse supra-structures, which include micelles, vesicles, and fibers, in addition to its extensive applications in luminescent materials, sensors, bioimaging, and drug delivery over the past decades. Supramolecular polymers, which represent a combination of supramolecular chemistry and polymer science, are constructed by non-covalent interactions, such as host-guest interactions, hydrogen bonding, hydrophobic or hydrophilic interactions, metal-ligand interactions, π-π stacking, and electrostatic interactions. To date, numerous host-guest recognition systems have been reported, including crown ethers, cyclodextrins, calixarenes, cucurbituril, pillararenes, and other macrocyclic hosts. Among them, crown ethers, as the first generation of macrocyclic hosts, provide a promising and facile alternative route to supramolecular polymers. In addition, the incorporation of fluorophores into supramolecular polymers could endow them with multiple properties and functions, thereby presenting potential advantages in the context of smart materials. Thus, this review focuses on the fabrication strategies, interesting properties, and potential applications of fluorescent supramolecular polymers based on crown ethers. Typical examples are presented and discussed in terms of three different types of building blocks, namely covalently bonded low-molecular-weight compounds, polymers modified by hosts or guests, and supramolecular coordination complexes.

Recent advances in DNA nanotechnology led to the development of biocompatible functional materials targeting different applications, including delivery, precise scaffolding, computation, etc. However, a standard toolbox of building blocks used by chemists is largely limited to canonical nucleobases. To further develop the functional potential of synthetic DNA systems, the use of sequence-specific DNA-polymer conjugates has emerged as an exciting alternative approach. Herein, we demonstrate the synthesis and self-assembly properties of monodisperse DNA-pyrene polyphosphate oligomers as essential units for DNA-grafted supramolecular polymers. Such systems become increasingly important for the creation of dynamic and stimuli-responsible materials. The unique self-recognition properties of oligonucleotides laid the foundation of modern DNA nanotechnology. Over the last two decades, man-made DNA assemblies led to the development of numerous biocompatible functional materials, including drug carriers, nanorobots, and scaffolding platforms. A standard toolbox of building blocks used by chemists in the field is largely limited to four units – A, G, C, T. To further develop the functional potential of synthetic DNA systems, the use of DNA-chromophore conjugates emerges as an exciting approach. Following our previous findings, we demonstrate herein the synthesis of the DNA-pyrene oligomers and the temperature-induced self-assembly of these conjugates into DNA-grafted supramolecular polymers. (see Figure 1). Such systems become increasingly important for the creation of dynamic and stimuli-responsible materials targeting various applications such as delivery and precise scaffolding.

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Learning What Makes Catalysts Good

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

The present work takes an overarching look on how particles can alter the functional and structural properties of shape memory alloys (SMAs). We consider precipitates which form in binary and ternary NiTi-based alloys, including the high-temperature SMAs NiTiHf and NiTiZr. We also take a look at precipitates which form in Cu–Al–Zn, Cu–Al–Ni, and Co–Ni–Ga–SMAs. We take these alloy systems as examples to review different effects of particles on local alloy chemistry and local stress and strain states at particle/matrix interfaces. Their influence on the nucleation and growth of martensite and on the propagation of a martensitic transformation front is discussed, and the consequences for macroscopic functional and structural properties like phase transformation temperatures and widths of thermal hysteresis are highlighted. Emphasis is also placed on particles which form because elements like C and O are picked up during alloy production and affect functional and structural properties. Finally, it is suggested to take a look at additive manufactured SMAs with added inert nano/micro-particles, which would facilitate the investigation of the mechanical constraints, and which may lead to SMAs with improved structural and functional properties.

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

Analytical ultracentrifugation (AUC) is the leading technique for determining the molar mass, state of association in solution, and association constants of biological macromolecules. Even though it is little used for supramolecular polymers, this technique has already been shown to represent one of the best options in the characterization of these systems. The use of supramolecular assemblies in the field of nanotechnology requires the arrangement of such systems on surfaces, from solution. Therefore, the control and investigation of solution properties is of major importance. In this contribution we highlight the applicability and advantages of using AUC by presenting a summary of the results of the past few years on the characterization of various types of synthetic polymers and supramolecular polymer systems.

Computational neuroscience uses computational approach which is study of nervous system and branch of neuroscience. Computational methods include computer simulations, mathematics, statistics and abstractions which are used across many subareas of neuroscience including structure, physiology, development and cognitive abilities of the nervous system. It explains how the brain represents and processes information available through the electrical and chemical signals. Bioinformatics is the wide range of applications in the field of mathematics, science and a core set of problem-solving approaches to the understanding of living systems. Biological research is the most fundamental research to understand the complete mechanism of living system. Deep Learning refers to a set of artificial neural network (ANN) techniques. But ANN researchers are primarily focused on applications and technology, and actually don’t know that much about real brains. Some of their networks, particularly convolutional neural nets, are extremely implausible as models of real biological neural networks. This paper briefly reviews the current status of a field which until that point was referred to by a variety of names, such as neural modelling, brain theory and neural networks. Computational neuroscience (also theoretical neuroscience) studies brain function in terms of the information processing properties of the structures that make up the nervous system. Ideas from computational neuroscience sometimes percolate into related fields such as computer vision, machine learning and artificial intelligence. Right now neural networks are the hottest thing in machine learning.

Cancer heterogeneity presents a major obstacle to effective drug treatment, emphasizing the need for personalized approaches that can accurately predict drug responses. Advances in high-throughput technologies have driven precision medicine initiatives toward integrating multi-omics data, enabling a more comprehensive understanding of tumor biology. However, integration of diverse omics layers poses challenges for computational modeling, as many traditional machine learning (ML) and statistical methods are not designed to capture complex, high-dimensional and multimodal data. This review examines the studies that integrate multi-omics datasets, aiming to enhance drug response prediction (DRP). Specifically, it outlines the most used omics types and computational approaches – classical ML models, as well as advanced deep learning and multimodal integration frameworks for improving DRP, detailing key methodologies and evaluation metrics, such as area under the dose–response curve, F1 score and mean square error, which assess model performance. By summarizing the integrated omics data, computational methods and challenges encountered, this review provides an in-depth overview of the existing landscape of precision medicine and future directions for advancing drug-response prediction.