Demo Data from EDBT 2026 / reviewers in the wild / expert
Jonathan Feldman 0001
dblp:53/9014-1
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0003-4130-6447ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein structure prediction
protein complex structure prediction |
0.9 | 1 | 2025 | AF3Complex yields improved structural predictions of protein complexes · Bioinform. 2025 |
Bioinformatics and computational biology
structural bioinformatics |
0.9 | 1 | 2025 | AF3Complex yields improved structural predictions of protein complexes · Bioinform. 2025 |
Methods — techniques the papers use, named apart from their topics
deep learning · 0.9alphafold3 · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | AF3Complex yields improved structural predictions of protein complexesabstractMOTIVATION: Accurate structures of protein complexes are essential for understanding biological pathway function. A previous study showed how downstream modifications to AlphaFold 2 could yield AF2Complex, a model better suited for protein complexes. Here, we introduce AF3Complex, a model equipped with both similar and novel improvements, built on AlphaFold 3. RESULTS: Benchmarking AF3Complex and AlphaFold 3 on a large dataset of protein complexes, it was shown that AF3Complex outperforms AlphaFold 3. Moreover, by evaluating the structures generated by AF3Complex on datasets of protein-peptide complexes and antibody-antigen complexes, it was established that AF3Complex could create high-fidelity structures for these challenging complex types. Additionally, when deployed to generate structural predictions for protein complexes used in the recent CASP16 competition, AF3Complex yielded structures that would have placed it among the top models in the competition. AVAILABILITY AND IMPLEMENTATION: The AF3Complex code is freely available at https://github.com/Jfeldman34/AF3Complex.git. Jonathan Feldman 0001, Jeffrey Skolnick |
Bioinform. | 1 |