Make Way for Robot Scientists

King, Ross D.; Rowland, Jem J.; Oliver, Stephen G.; Young, Michael J.; Aubrey, Wayne; Byrne, Emma; Liakata, Maria; Markham, Magdalena; Pir, Pınar; Soldatova, Larisa; Sparkes, Andrew C.; Whelan, Kenneth Edward; Clare, Amanda

doi:10.1126/science.325_945a

Cited by 10 publications

(4 citation statements)

References 1 publication

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“…If this phase can be shortened, it can significantly reduce the iteration time of a DMTA cycle. Laboratory automation has and will continue to play a decisive role in this context 135,136,137,138 . Fast compound synthesis using robust reactions in batch or flow with automated analytics and purification will help speed-up the Make phase.…”

Section: Challenge 4: Reducing Cycle Timesmentioning

confidence: 99%

Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

519

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 4: Reducing Cycle Timesmentioning

confidence: 99%

Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

519

349

View full text Add to dashboard Cite

show abstract

“…For generating hypotheses and verifying them, Ross King and his colleagues have developed a systematic robot scientist that can infer possible biological hypotheses and design simple experiments using a defined-protocol automated system to analyze orphan genes in budding yeast (King et al 2009a(King et al , 2009bKing et al 2004). While this brought only a moderate level of discovery within the defined context of budding yeast genes, the study represented an integration of bioinformatics-driven hypothesis generation and automated experimental processes.…”

Section: Reality Versus Human Cognitionmentioning

confidence: 99%

Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

Kitano

2016

AI Magazine

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This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.

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“…Another recent example is the robot scientists Adam and Eve that automate drug development via cycles of quantitative structure–activity relationship (QSAR) learning and biological testing ( Fig. 3 ) [ 16 – 18 ]. Eve’s selection of compounds was more cost efficient than standard drug screening, and the robotic scientist has identified several new drugs active against tropical disease parasites [ 19 ].…”

Section: Reviewmentioning

confidence: 99%

Biomimetic molecular design tools that learn, evolve, and adapt

Winkler

2017

Beilstein J. Org. Chem.

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A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

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Make Way for Robot Scientists

Cited by 10 publications

References 1 publication

Rethinking drug design in the artificial intelligence era

Rethinking drug design in the artificial intelligence era

Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

Biomimetic molecular design tools that learn, evolve, and adapt

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