Evidence map›Paper›PMID 41052327›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Graph neural networks for predicting metal-ligand coordination of transition metal complexes.

Jacob W Toney, Roland G St Michel, Aaron G Garrison, Ilia Kevlishvili, Heather J Kulik

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Graph neural networks for predicting metal-ligand coordination of transition metal complexes.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Machine learning in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jacob W ToneyDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0002-6920-1105
Roland G St MichelDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
Aaron G GarrisonDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
Ilia KevlishviliDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
Heather J KulikDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0001-9342-0191

Funding

Alfred P. Sloan Foundation (APSF) Fellowship in ChemistryDow Chemical UPISimon Family Faculty Research Innovation Fund
6 · The paper itself

Abstract

High-throughput virtual screening campaigns are invaluable for surveying the combinatorial space of possible transition metal complexes (TMCs), but they rely on accurate metal-ligand connectivity for meaningful results. Here, we curate a dataset of 70,069 unique ligands of known coordination from experimental structures of TMCs deposited in the Cambridge Structural Database. Using this dataset, we train separate graph neural network models to predict the total number and individual identities of ligand coordinating atoms with high accuracy and precision. Interpreting each model in terms of the learned molecular representations uncovers trends aligned with our understanding of coordination chemistry as well as chemical insights. Next, we integrate the trained models with the high-throughput screening software molSimplify and illustrate their utility by generating 1,175 TMCs and validating their geometries with density functional theory calculations. We anticipate these models will accelerate computational screening of TMCs with de novo combinations of metals and ligands in physically realistic coordination.

Indexed as

graph neural networksmachine learningtransition metal chemistry

Identifiers

PMID41052327
PMCPMC12541316

What OpenQuestion holds

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Registered trials

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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.