Biotechnology, Big Data and Artificial Intelligence

Oliveira, Arlindo L.

doi:10.1002/biot.201800613

Cited by 106 publications

(60 citation statements)

References 10 publications

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“…Appropriate input data are crucial for building useful predictive models for decision-making and generation of NCEs 15,16 . Without an appropriate dataset, and an understanding of the scope and limitations of those data, even a seemingly sophisticated model will not be able to produce useful results 17,18 .…”

Section: Challenge 1: Generating and Obtaining Appropriate Datasetsmentioning

confidence: 99%

Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

566

349

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Perspective │2 Artificial intelligence (AI) tools are increasingly being applied in drug discovery. Whilst some protagonists point to vast opportunities potentially offered by such tools, others remain skeptical, waiting for a clear impact to be shown in drug discovery projects. The truth is probably somewhere between these extremes, but it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' for small-molecule drug discovery with AI and approaches to address them.

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Section: Challenge 1: Generating and Obtaining Appropriate Datasetsmentioning

confidence: 99%

Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

566

349

View full text Add to dashboard Cite

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“…Smart utility of biopharmaceutical data involves mechanistic, data‐driven, or hybrid modeling (Hong et al, 2018; Oliveira, 2019; Steinwandter et al, 2019). In general, mechanistic models have the advantage of being more interpretable and require significantly less data in comparison to a purely data‐driven approach.…”

Section: Data Automation Visualization and Smart Data Utilitymentioning

confidence: 99%

“…Automation and visualization infrastructure provides superior process monitoring and control compared to conventional offline data management practices, thus leading to a more holistic process understanding. Moreover, advanced data‐mining techniques, data or mechanistic modeling, machine learning, and deep learning provide multidimensional perspectives of the process, predictions, and understanding (Oliveira, 2019). Furthermore, a reliable cyber‐physical framework is essential for Industry 4.0 initiatives.…”

Section: Introductionmentioning

confidence: 99%

Technology outlook for real‐time quality attribute and process parameter monitoring in biopharmaceutical development—A review

Wasalathanthri

Rehmann

Song

et al. 2020

Biotech & Bioengineering

113

101

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Real‐time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real‐time release (RTR) of biopharmaceuticals. A well‐defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real‐time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated‐data piping, analysis, aggregation, visualization, and smart utility of data for advanced‐data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated‐sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced‐analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.

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“…Given recent progress and projected trajectories, AI and biotechnology have both been described as key pillars of the fourth industrial revolution, with a growing number of technologies and applications arising at their point of convergence every year. [6][7][8][9]…”

Section: Technological Progressmentioning

confidence: 99%

Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

O’Brien

Nelson

2020

Health Security

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Rapid developments are currently taking place in the fields of artificial intelligence (AI) and biotechnology, and applications arising from the convergence of these 2 fields are likely to offer immense opportunities that could greatly benefit human health and biosecurity. The combination of AI and biotechnology could potentially lead to breakthroughs in precision medicine, improved biosurveillance, and discovery of novel medical countermeasures as well as facilitate a more effective public health emergency response. However, as is the case with many preceding transformative technologies, new opportunities often present new risks in parallel. Understanding the current and emerging risks at the intersection of AI and biotechnology is crucial for health security specialists and unlikely to be achieved by examining either field in isolation. Uncertainties multiply as technologies merge, showcasing the need to identify robust assessment frameworks that could adequately analyze the risk landscape emerging at the convergence of these 2 domains. This paper explores the criteria needed to assess risks associated with AI and biotechnology and evaluates 3 previously published risk assessment frameworks. After highlighting their strengths and limitations and applying to relevant AI and biotechnology examples, the authors suggest a hybrid framework with recommendations for future approaches to risk assessment for convergent technologies.

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Biotechnology, Big Data and Artificial Intelligence

Cited by 106 publications

References 10 publications

Rethinking drug design in the artificial intelligence era

Rethinking drug design in the artificial intelligence era

Technology outlook for real‐time quality attribute and process parameter monitoring in biopharmaceutical development—A review

Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

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