Coming of age of Allotrope: Proceedings from the Fall 2020 Allotrope Connect

Millecam, Todd; Jarrett, Austin J.; Young, Naomi; Vanderwall, Dana E.; Corte, Dennis Della

doi:10.1016/j.drudis.2021.03.028

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…The reaction classification is based on the molecular processes (MOP 118 ) and RXNO ontologies, demonstrated by its sample data taken from Reaxys. The analytical method and results type are based on a working draft version of Allotrope Foundation Ontology (AFO 119 ) where duplicate entries exist. However, it should be noted that the way UDM integrates the ontologies is by enumerating the ontological classes as a sub-schema of UDM and tagging them to the XML elements as attributes.…”

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

“…Allotrope Foundation is a collaborative effort from the pharmaceutical industry. 119 Similar to AnIML, it aims to propose a common data exchange format to unify the laboratory information technology (IT) landscape. It started from realizing the vision of Roberts et al 146 , 147 where an XML schema was envisaged to provide a holistic data format.…”

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

“…The same issue was acknowledged by the Allotrope Foundation 119 that there is a trend of making simpler data models for practical applications. One of their partner companies, TetraScience, developed an Intermediate Data Schema (IDS)—a JSON-based schema of analytical data as the precursor of the AFO format.…”

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

“…Allotrope Foundation is a collaborative effort from the pharmaceutical industry . Similar to AnIML, it aims to propose a common data exchange format to unify the laboratory information technology (IT) landscape.…”

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

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From Platform to Knowledge Graph: Evolution of Laboratory Automation

Bai

Cao

Mosbach

et al. 2022

JACS Au

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High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

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Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

Section: Data Representation and Exchange Protocolsmentioning

confidence: 99%

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From Platform to Knowledge Graph: Evolution of Laboratory Automation

Bai

Cao

Mosbach

et al. 2022

JACS Au

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“…The first is Allotrope that is stable for over a decade now and can cover various techniques and scales but is a for-fee license product. 200 On the other hand, AnIML is easy to integrate and open source but has a lower number of supported equipment. 201 Both have the potential to be adopted at an industrial scale, however, neither of them has good semantic coverage for manufacturing, control, system modeling, and simulation, and neither has been evidently…”

Section: Data Collection and Exchange Standardsmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

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Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever‐evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end‐to‐end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

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