ReviewJACS Au2025
A Perspective on Foundation Models in Chemistry.
Review in JACS Au, 2025. 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.
- Artificial Intelligence-Driven Inverse Design of Singlet Fission Candidates in the Acene family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Lessons From Drug Discovery for Cryoprotective Agent Design: An AI-Oriented Perspective.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era.Journal of chemical theory and computation · 2026Review
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Short-Range Machine-Learning Potentials for Aqueous Electrolyte Solutions.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Foundation and Multimodal Models for Drug Discovery in Molecular Informatics: Principles, Evaluation, and Practical Guidance.Molecular informatics · 2026Review
- Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery: From Small Molecules to Biologics.Yonago acta medica · 2026Review
- Elements and roadmap for interactive molecular graphics and modeling "in the Holodeck".Protein science : a publication of the Protein Society · 2026Article
- Nature and Dynamics of Active Sites in Cu-Based Catalysts for the COAccounts of chemical research · 2026Article
- AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics.Chemical science · 2025Review
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
Foundation models are an emerging paradigm in artificial intelligence (AI), with successful examples like ChatGPT transforming daily workflows. Generally, foundation models are large-scale, pretrained models capable of adapting to various downstream tasks by leveraging extensive data and model scaling. Their success has inspired researchers to develop foundation models for a wide range of chemical challenges, from materials discovery to understanding structure-property relationships, areas where conventional machine learning (ML) models often face limitations. In addition, foundation models hold promise for addressing persistent ML challenges in chemistry, such as data scarcity and poor generalization. In this perspective, we review recent progress in the development of foundation models in chemistry across applications of varying scope. We also discuss emerging trends and provide an outlook on promising approaches for advancing foundation models in chemistry.
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.