ArticleJournal of chemical education2026
Illuminating the Interface of Blocc Chemistry and Data Science: Maximizing Function with ML-Guided Discovery and a Digital Molecule Maker.
Article in Journal of chemical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- AI-assisted development of interactive web simulations for analytical chemistry: a personal account.Analytical and bioanalytical chemistry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
The intersection of automatable blocc chemistry for iterative carbon-carbon bond formation with artificial intelligence is amplifying molecular innovation in new and exciting ways. In this lab, students are introduced to concepts and tools that help them gain familiarity and confidence with this emerging area of chemistry. Students specifically learn about four automated synthesis platforms, each of which stitches together a bounded set of molecular building blocks using just one type of robust bond-forming reaction. Students then analyze a variety of small molecules and biopolymers to identify the most redundant types of bonds in each molecule that are compatible with iterative formation from bifunctional building blocks. Based on their analysis, students then select an appropriate iterative automated synthesis platform and determine which molecular building blocks would be required to assemble the desired targets. Students next investigate two case studies where blocc chemistry was used used in concert with artificial intelligence to discover new molecular functions. In the first case, students identify high-performing blocks from selected data sets to optimize a single objective function related to organic laser properties. In the second case, students engage with a new online platform inspired by Scratch, dubbed the Digital Molecule Maker (DMM), to perform a multi-objective optimization of organic photovoltaic candidates (OPV). To conclude the lab, students manually perform the blocc chemistry that is foundational for the DMM. This activity series is the second installment of a sequence of undergraduate laboratories designed to illuminate the functional discovery-enabling interface of AI and automated blocc chemistry.
Indexed as
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.