ReviewDigital discovery2024
Accelerated chemical science with AI.
Review in Digital discovery, 2024. 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
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
16 citing papers in PubMed.
- Structured HTE Data and Integrated Bayesian Optimization Accelerate Scalable Amidation Process Development.ACS organic & inorganic Au · 2026Article
- Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework.ACS central science · 2026Article
- High-throughput solid microsampling through stochastic robotic automation.Communications chemistry · 2026Article
- Looking back and to the future after four-plus years of language in chemistry.Digital discovery · 2026Review
- BB-EIT: A Generalized Prediction Model for Protein Adsorption on Polymer Brushes Using Augmented Chemical Embeddings.ACS applied materials & interfaces · 2026Article
- Learning Multidimensional Electronegativity of Selected Atom Types in Organic Molecules Using Graph Neural Networks.ACS omega · 2025Article
- Robotic system takes chemistry into hyperspace.Nature · 2025Article
- Designing a large language model for chemists.Patterns (New York, N.Y.) · 2025Article
- Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models.Angewandte Chemie (International ed. in English) · 2025Article
- Miniaturized Supercritical Fluid Chromatography Coupled with Ion Mobility Spectrometry: A Chip-Based Platform for Rapid Chiral and Complex Mixture Analysis.Analytical chemistry · 2025Article
- An active representation learning method for reaction yield prediction with small-scale data.Communications chemistry · 2025Article
- Adapting hybrid density functionals with machine learning.Science advances · 2025Article
- Article
- Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.Briefings in bioinformatics · 2024Review
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine.Pharmaceutics · 2024Review
- Discovery of Crystallizable Organic Semiconductors with Machine Learning.Journal of the American Chemical Society · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.
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.