Impact Factor
4.61
Journal Citation Impact
Estimated influence, citation density, publishing volume, and citation integrity from the analytics table.
Citation rate
13.99
4,559 total citations
h5-index
91
h5 depth signal
Cited share
65.1%
237 of 364
Total citations
4,559
Citation footprint
Metric signal
13.71
External metric anchor
Self-citation
1.6%
72 self-citations
Citations Trend
Monthly Publications Cadence
Citing Geographic Demographics
United States
380
China
199
United Kingdom
73
Germany
62
Switzerland
38
Citing Fields of Research
AI & Machine Learning
365
Genetics & Genomics
161
Physical Chemistry
133
Protein Structure & Folding
73
Neurosciences
55
Publishing Activity & Reach
Volume, recency, access pattern, and citing-community signals from the journal analytics record.
All-time article footprint
1,064
Articles tracked across the journal lifetime
Indexed publication set
364
237 cited publications
Last 12 months
183
15.3 publications/month
Open access share
19.1%
35 OA publications in the last year
Leading citing country
United States
380 citing publications
Leading citing field
AI & Machine Learning
365 citing publications
Latest tracked publication
15 October 2026
10 recent records available
Coverage & Indexing History
v1n1, Jan. 2021-
v1n1, Jan. 2021-
Research Focus & Classification
Indexed Subjects
Multidisciplinary
Issuing & Sponsoring Bodies
publisher
Springer Nature (Firm)
Broad Categories
Computational Biology
Science
Multidisciplinary
MeSH Descriptors
Computing Methodologies.
Models, Theoretical
Science
Technology
Latest Publications
Recent article records with DOI-first linking and condensed abstracts.
01
DOI 10.1038/s43588-026-01067-w
Open DOI
Hybrid modeling for improved structure prediction of large protein complexes
02
03
DOI 10.1038/s43588-026-01044-3
Open DOI
LLMs predict material properties based on atom types and not atom positions
04
DOI 10.1038/s43588-026-01066-x
Open DOI
HIP: Hessian Interatomic Potentials without derivatives
Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally expensive to calculate and scale poorly with system size, wh...
05
DOI 10.1038/s43588-026-01045-2
Open DOI
Less can be more for predicting properties with large language models
Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from cry...
06
DOI 10.1038/s43588-026-01071-0
Open DOI
Photonic circuits become trainable AI hardware
07
DOI 10.1038/s43588-026-01070-1
Open DOI
Epidemiological modeling in the age of artificial intelligence
08
DOI 10.1038/s43588-026-01064-z
Open DOI
Steering complex rhythms
09
DOI 10.1038/s43588-026-01063-0
Open DOI
Advancing chemical reaction prediction in data-scarce drug discovery with active and geometric deep learning
The success of generative molecular design depends on the synthetic accessibility of the molecules it creates. While computational models can help predict chemical reactions, their current effectiveness is limited by a lack of reference ...
10
DOI 10.1038/s43588-026-01057-y
Open DOI