The Automation of Science

By Ross D. King, Jem J. Rowland, S. Oliver, Michael Young, Wayne Aubrey, Emma Byrne, Maria Liakata, M. Markham, Pınar Pir, Larisa N. Soldatova and 3 more. Science, 2009

709 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/16f26b309f5d589d7e019ed7fbb1a65525598a03

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Citations in the corpus

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery (2018, methodology): “Examples of softwares used during drug design include ADAM and EVE, used in target and hit finding [211,212].”
Closed-loop optimization of fast-charging protocols for batteries with machine learning (2020, methodology): “Adaptive OED algorithms have been successfully applied to physical science domains, such as materials science 1,2,12–14 , chemistry 15,16 , biology 17 and drug discovery 18 , as well as to computer science domains, such as hyperparameter optimization for machine learning 19,20 .”
Emerging materials intelligence ecosystems propelled by machine learning (2020, methodology): “The first robotic researcher, Adam, was designed for functional genomic research, measuring growth curves of microbial strains in different environments 190 .”
Accelerating the discovery of materials for clean energy in the era of smart automation (2018, methodology): “In 2009, a hypothetico-deductive ‘robot scientist’, named Adam, was developed that could autonomously perform experiments, devise hypotheses and design experiments to validate the hypotheses in the area of functional genomics 58 .”
On scientific understanding with artificial intelligence (2022, background): “If the data-source is an experiment, this future system will require access to complex lab automation with large parameter spaces to explore, as demonstrated recently in biology [61], chemistry [62–67] or physics [68, 69].”
Rethinking drug design in the artificial intelligence era (2019, background): “AI has the potential to support scientists and enable better use of data in greater volumes for decision-making and, thus, lead to more productive discovery teams 142 .”
ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE (2019, background): “[16 , 17] If an extant drug or compound is not found that could appropriately modulate a target, then creating a novel pharmacotherapeutic (i.e., drug) often requires insights obtained from materials science in order to make sure the molecular structure of the drug produced has favorable properties…”
Siri of the cell: what biology could learn from the iPhone. (2014, background): “For instance, IBM’s Watson computer that famously won at Jeopardy! in 2011 or Adam the Robot Scientist that successfully characterized new enzymes in yeast (King et al., 2009), both relied profoundly on ontologies for knowledge representation.”
Scientific discovery in the age of artificial intelligence (2023, background): “For instance, King et al.(204) combined logical AI and robotics to autonomously generate functional genomics hypotheses about yeast and experimentally test the hypotheses using laboratory automation.”
The Promise and Potential of Big Data: A Case for Discovery Informatics (2014, background): “King et al. (2009) have demonstrated the possibility of automating science by building a robot scientist capable of autonomously generating and testing hypotheses, in this instance, concerning the functional genomics of yeast (Saccharomyces cerevisiae).”