Data-Driven Methods for Accelerating Polymer Design

Patra, Tarak K.

doi:10.1021/acspolymersau.1c00035

Cited by 69 publications

(69 citation statements)

References 211 publications

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“…The availability of EoS parameters for several polymeric systems is still an issue, which could be solved using Group Contribution, , ML, or molecular simulation methods. Data-driven methods have recently been proposed to design polymers with specific properties …”

Section: Models and Simulationmentioning

confidence: 99%

A View on the Future of Applied Thermodynamics

Hemptinne

Kontogeorgis

Dohrn

et al. 2022

Ind. Eng. Chem. Res.

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Thermodynamics is the science of the interactions between energy and matter. It was formalized in the late 19th century and remains an essential piece in solving many technological challenges that society faces today. Yet, it is often considered complex and challenging, perhaps because it is often taught within a rigid mathematical framework, without highlighting the extensive range of applications and the tools that it offers for understanding and elaborating a sustainable future. The authors of this paper have performed an industrial survey (Kontogeorgis et al., Ind. Eng. Chem. Res., 2021, 60, 13, 4987-5013), which pointed out that thermodynamics is indeed a cornerstone of many processes in a large range of industries, but that as of today, many questions and needs remain unanswered. Some missing answers are caused by a lack of knowledge of the existing tools (educational issue), some by the unavailability of models, parameters or by the lack of transferability of the concepts from one system to another. In other cases, simply, no generally accepted approach exists, and fundamental research is required for understanding the phenomena. In all cases, data are needed, either to understand, develop, or validate the models. Specific recent examples of applied thermodynamics research relevant to industrial practice are discussed. This manuscript aims not only at promoting research but also at encouraging highly trained professionals to engage in education, laboratory work, fundamental developments, and/or model validation. Such professionals should find positions both in academia and in industry, as well as with software vendors. Collaboration between academia, industry, and software vendors is essential in order to foster new developments and serve the goals of sustainable development and circular economy.

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Section: Models and Simulationmentioning

confidence: 99%

A View on the Future of Applied Thermodynamics

Hemptinne

Kontogeorgis

Dohrn

et al. 2022

Ind. Eng. Chem. Res.

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“…For a binary copolymer with 50 repeat units, over 2 49 (approximately 10 14 ) unique sequences exist. 207 In reality copolymers may contain more than two types of monomers and span lengths of at least hundreds of repeat units. Given this astronomical sequence space, computer simulations must be capable of predicting sequence-dependent polymer properties using the least number of in silico screening runs.…”

Section: Data-driven Design Of Polymeric Vectorsmentioning

confidence: 99%

“…Minor changes in copolymer sequence can result in wide variations in phase behavior, interfacial properties, and biological performance. For a binary copolymer with 50 repeat units, over 2 49 (approximately 10 14 ) unique sequences exist . In reality copolymers may contain more than two types of monomers and span lengths of at least hundreds of repeat units.…”

Section: Data-driven Design Of Polymeric Vectorsmentioning

confidence: 99%

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Kumar

2022

ACS Appl. Bio Mater.

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Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic−hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

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“…A major challenge in the development of bespoke ML models for polymer property prediction is the lack of a general polymer representation. [48][49][50][51][52] In fact, almost all ML models currently used for polymer property predictions do not capture the ensemble nature of the polymeric material, even when predicting properties of the ensemble rather than sequence-dened oligomers. The vast majority of past studies have relied on molecular representations of repeating units alone, even though such approaches cannot distinguish between alternating, random, block, or gra copolymers.…”

Section: Introductionmentioning

confidence: 99%