ReviewJACS Au2021
Chemputation and the Standardization of Chemical Informatics.
Review in JACS Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- Article
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics.Chemical science · 2025Review
- Article
- A Cobotic, Digitally Controlled Schlenk-line Unlocks Access to Elusive Lewis-Base Stabilised Copper Bis(Disilylamides).Angewandte Chemie (International ed. in English) · 2025Article
- Universal peptide synthesis via solid-phase methods fused with chemputation.Nature communications · 2025Article
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- IvoryOS: an interoperable web interface for orchestrating Python-based self-driving laboratories.Nature communications · 2025Article
- Local reaction condition optimization via machine learning.Journal of molecular modeling · 2025Review
- The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies.Globalization and health · 2024Review
- Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis.Angewandte Chemie (International ed. in English) · 2024Article
- An integrated self-optimizing programmable chemical synthesis and reaction engine.Nature communications · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Bayesian Optimization in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Chemical Species Ontology for Data Integration and Knowledge Discovery.Journal of chemical information and modeling · 2023Article
- Robotic Modules for the Programmable Chemputation of Molecules and Materials.ACS central science · 2023Review
- Rapid planning and analysis of high-throughput experiment arrays for reaction discovery.Nature communications · 2023Article
- An Ecosystem for Digital Reticular Chemistry.ACS central science · 2023Article
- Deep learning-based automatic action extraction from structured chemical synthesis procedures.PeerJ. Computer science · 2023Article
- The way to AI-controlled synthesis: how far do we need to go?Chemical science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.
Identifiers
What OpenQuestion holds
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.