ArticleChemistry Central journal2015
Lost in chemical space? Maps to support organometallic catalysis.
Article in Chemistry Central journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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Who cites it
11 citing papers in PubMed.
- Microkinetic Assessment of Ligand-Exchanging Catalytic Cycles.ACS catalysis · 2025Article
- Enhancing the photocatalytic upcycling of polystyrene to benzoic acid: a combined computational-experimental approach for acridinium catalyst design.Chemical science · 2024Article
- HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts.Journal of chemical information and modeling · 2023Article
- Article
- Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources.Journal of the American Chemical Society · 2022Article
- Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem.International journal of molecular sciences · 2021Review
- Computational Mapping of Dirhodium(II) Catalysts.Chemistry (Weinheim an der Bergstrasse, Germany) · 2021Article
- tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes.Journal of chemical information and modeling · 2020Article
- Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.ACS catalysis · 2020Article
- Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex.Chemical science · 2020Article
- Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis.Chemical science · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Descriptors calculated from molecular structures have been used to map different areas of chemical space. A number of applications for such maps can be identified, ranging from the fine-tuning and optimisation of catalytic activity and compound properties to virtual screening of novel compounds, as well as the exhaustive exploration of large areas of chemical space by automated combinatorial building and evaluation. This review focuses on organometallic catalysis, but also touches on other areas where similar approaches have been used, with a view to assessing the extent to which chemical space has been explored. Graphical abstractCartoon representation of a chemical space map.
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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.