ReviewACS central science2018
Designing Algorithms To Aid Discovery by Chemical Robots.
Review in ACS central science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- Accelerated Chemical Reaction Optimization Using Multi-Task Learning.ACS central science · 2023Article
- Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Polygrammar: Grammar for Digital Polymer Representation and Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Machine Learning Applications for Chemical Reactions.Chemistry, an Asian journal · 2022Review
- Review
- GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics.Journal of cheminformatics · 2021Article
- Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions.Nanomaterials (Basel, Switzerland) · 2021Review
- Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.The Biochemical journal · 2020Review
- A curious formulation robot enables the discovery of a novel protocell behavior.Science advances · 2020Article
- The good, the bad, and the ugly in chemical and biological data for machine learning.Drug discovery today. Technologies · 2019Article
- Nanosystems, Edge Computing, and the Next Generation Computing Systems.Sensors (Basel, Switzerland) · 2019Review
- Article
- Harnessing liquid-in-liquid printing and micropatterned substrates to fabricate 3-dimensional all-liquid fluidic devices.Nature communications · 2019Article
- Capturing chemical intuition in synthesis of metal-organic frameworks.Nature communications · 2019Article
- ACS Central Science Virtual Issue on Machine Learning.ACS central science · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.