diff --git a/entries/string_ppi/__init__.py b/entries/string_ppi/__init__.py
index 71bbd62..754f333 100644
--- a/entries/string_ppi/__init__.py
+++ b/entries/string_ppi/__init__.py
@@ -49,7 +49,7 @@ citation = [('D. Szklarczyk et al., "The STRING database in 2025: protein networ
             ('D. Szklarczyk et al., "The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest." Nucleic Acids Research 51, D638-D646 (2023)', 'https://doi.org/10.1093/nar/gkac1000'),
             ('D. Szklarczyk et al., "The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets." Nucleic Acids Research 49, D605-D612 (2021)', 'https://doi.org/10.1093/nar/gkaa1074'),
             ('D. Szklarczyk et al., "STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." Nucleic Acids Research 47, D607-D613 (2019)', 'https://doi.org/10.1093/nar/gky1131')]
-icon_hash = None
+date_added = '2026-10-27'
 upstream_license = ("CC BY 4.0", "https://creativecommons.org/licenses/by/4.0/")
 upstream_prefix = 'https://stringdb-downloads.org/download'
 _organisms = {"human": 9606, "mouse": 10090, "yeast": 4932, "ecoli": 511145,

diff --git a/entries/string_ppi/__init__.py b/entries/string_ppi/__init__.py
index 41f6ea9..71bbd62 100644
--- a/entries/string_ppi/__init__.py
+++ b/entries/string_ppi/__init__.py
@@ -37,12 +37,12 @@ The entry also contains the networks of the same organisms in the earlier versio
 
 The networks have the following properties:
 
-- `string_id`: the STRING identifier of the protein (the NCBI taxonomy identifier of the organism followed by the protein's identifier, e.g. `9606.ENSP00000000233`).
-- `name`: the preferred name of the protein (e.g. `ARF5`).
-- `size`: the length of the protein, in amino acids.
-- `annotation`: a description of the protein.
-- `combined_score` (edges): the combined confidence score of the association, multiplied by 1000 (from 150 to 999).
-- `neighborhood`, `fusion`, `cooccurence`, `coexpression`, `experimental`, `database`, `textmining` (edges): the scores of the individual channels, multiplied by 1000 (0 if there is no evidence of that kind)."""
+- (Node) `string_id`: the STRING identifier of the protein (the NCBI taxonomy identifier of the organism followed by the protein's identifier, e.g. `9606.ENSP00000000233`).
+- (Node) `name`: the preferred name of the protein (e.g. `ARF5`).
+- (Node) `size`: the length of the protein, in amino acids.
+- (Node) `annotation`: a description of the protein.
+- (Edge) `combined_score`: the combined confidence score of the association, multiplied by 1000 (from 150 to 999).
+- (Edge) `neighborhood`, `fusion`, `cooccurence`, `coexpression`, `experimental`, `database`, `textmining`: the scores of the individual channels, multiplied by 1000 (0 if there is no evidence of that kind)."""
 tags = ['Biological', 'Protein interactions', 'Weighted', 'Metadata']
 url = 'https://string-db.org/cgi/download'
 citation = [('D. Szklarczyk et al., "The STRING database in 2025: protein networks with directionality of regulation." Nucleic Acids Research 53, D730-D737 (2025)', 'https://doi.org/10.1093/nar/gkae1113'),

diff --git a/entries/string_ppi/__init__.py b/entries/string_ppi/__init__.py
new file mode 100644
index 0000000..41f6ea9
--- /dev/null
+++ b/entries/string_ppi/__init__.py
@@ -0,0 +1,119 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2020-2026 Tiago de Paula Peixoto <tiago@skewed.de>
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Affero General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Affero General Public License along
+# with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+from .. import *
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.csv
+import pyarrow.compute as pc
+
+title = "STRING protein association networks (2025)"
+description = r"""Networks of functional associations between the proteins of seven model organisms, from the STRING database, version 12.5. Nodes are proteins, and an undirected edge indicates a functional association between two proteins, i.e. that they contribute jointly to a biological function, which does not necessarily mean that they physically bind each other. STRING combines evidence from several channels: physical interactions from experiments, curated pathway and complex databases, co-expression, conserved genomic neighborhood, gene fusions, phylogenetic co-occurrence, and co-mention in the scientific literature (text mining), including evidence transferred from other organisms by orthology. Each channel gives a confidence score, and the channels are combined into an overall confidence score.
+
+The organisms are human (*Homo sapiens*, `human`), mouse (*Mus musculus*, `mouse`), baker's yeast (*Saccharomyces cerevisiae*, `yeast`), the bacterium *Escherichia coli* K-12 MG1655 (`ecoli`), the fruit fly (*Drosophila melanogaster*, `fly`), the nematode *Caenorhabditis elegans* (`worm`) and the thale cress (*Arabidopsis thaliana*, `arabidopsis`). For each organism there are two networks:
+
+- `<organism>_all`: all the associations in STRING, of any score (the lowest score in STRING is 0.15).
+- `<organism>_min400`: only the associations with a combined score of at least 0.4, STRING's default threshold ("medium confidence").
+
+All networks of an organism have the same nodes, all the proteins of the organism in STRING (proteins without associations are isolated nodes). STRING is available for over 12,000 organisms; only these seven are included here.
+
+The entry also contains the networks of the same organisms in the earlier versions 11.0 (2019), 11.5 (2021) and 12.0 (2023) of STRING, named `<organism>_v<version>_all` and `<organism>_v<version>_min400` (e.g. `human_v11.0_all`), built in the same way; the networks without a version in their names are those of version 12.5. The sets of proteins and the scores differ between versions. Versions before 11.0 are not included.
+
+The networks have the following properties:
+
+- `string_id`: the STRING identifier of the protein (the NCBI taxonomy identifier of the organism followed by the protein's identifier, e.g. `9606.ENSP00000000233`).
+- `name`: the preferred name of the protein (e.g. `ARF5`).
+- `size`: the length of the protein, in amino acids.
+- `annotation`: a description of the protein.
+- `combined_score` (edges): the combined confidence score of the association, multiplied by 1000 (from 150 to 999).
+- `neighborhood`, `fusion`, `cooccurence`, `coexpression`, `experimental`, `database`, `textmining` (edges): the scores of the individual channels, multiplied by 1000 (0 if there is no evidence of that kind)."""
+tags = ['Biological', 'Protein interactions', 'Weighted', 'Metadata']
+url = 'https://string-db.org/cgi/download'
+citation = [('D. Szklarczyk et al., "The STRING database in 2025: protein networks with directionality of regulation." Nucleic Acids Research 53, D730-D737 (2025)', 'https://doi.org/10.1093/nar/gkae1113'),
+            ('D. Szklarczyk et al., "The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest." Nucleic Acids Research 51, D638-D646 (2023)', 'https://doi.org/10.1093/nar/gkac1000'),
+            ('D. Szklarczyk et al., "The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets." Nucleic Acids Research 49, D605-D612 (2021)', 'https://doi.org/10.1093/nar/gkaa1074'),
+            ('D. Szklarczyk et al., "STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." Nucleic Acids Research 47, D607-D613 (2019)', 'https://doi.org/10.1093/nar/gky1131')]
+icon_hash = None
+upstream_license = ("CC BY 4.0", "https://creativecommons.org/licenses/by/4.0/")
+upstream_prefix = 'https://stringdb-downloads.org/download'
+_organisms = {"human": 9606, "mouse": 10090, "yeast": 4932, "ecoli": 511145,
+              "fly": 7227, "worm": 6239, "arabidopsis": 3702}
+def _files(org, t, v):
+    return (f"protein.links.detailed.v{v}/{t}.protein.links.detailed.v{v}.txt.gz",
+            f"protein.info.v{v}/{t}.protein.info.v{v}.txt.gz")
+files = [(_files(org, t, "12.5"), f"{org}_{thres}", None)
+         for org, t in _organisms.items() for thres in ["all", "min400"]]
+# earlier versions
+files += [(_files(org, t, v), f"{org}_v{v}_{thres}", None)
+          for v in ["11.0", "11.5", "12.0"]
+          for org, t in _organisms.items() for thres in ["all", "min400"]]
+
+# minimum combined score of the associations
+min_score = {"all": 0, "min400": 400}
+
+channels = ["neighborhood", "fusion", "cooccurence", "coexpression",
+            "experimental", "database", "textmining", "combined_score"]
+
+def fetch_upstream(force=False):
+    return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+                                force)
+
+@cache_network()
+@coerce_props()
+@annotate()
+def parse(alts=None):
+    name = __name__.split(".")[-1]
+    for (flinks, finfo), alt, fmt in files:
+        if alts is not None and alt not in alts:
+            continue
+        with open_upstream_file(name, finfo, "rb") as f:
+            info = pd.read_csv(f, sep="\t", dtype=str, keep_default_na=False,
+                               quoting=3)
+        info = info.rename(columns={"#string_protein_id": "string_protein_id",
+                                    "protein_external_id": "string_protein_id"})  # v11.0
+        g = Graph(directed=False)
+        g.add_vertex(len(info))
+        g.vp.string_id = g.new_vp("string", vals=info["string_protein_id"].tolist())
+        g.vp.name = g.new_vp("string", vals=info["preferred_name"].tolist())
+        g.vp.size = g.new_vp("int", vals=info["protein_size"].astype(int).to_numpy())
+        g.vp.annotation = g.new_vp("string", vals=info["annotation"].tolist())
+        ids = pa.array(info["string_protein_id"].to_numpy())
+        del info
+
+        with open_upstream_file(name, flinks, "rb") as f:
+            t = pa.csv.read_csv(f, parse_options=pa.csv.ParseOptions(delimiter=" "),
+                                convert_options=pa.csv.ConvertOptions(
+                                    column_types={c: pa.int16() for c in channels}))
+        # each association is listed in both directions
+        mask = pc.and_(pc.less(t["protein1"], t["protein2"]),
+                       pc.greater_equal(t["combined_score"],
+                                        min_score[alt.split("_")[-1]]))
+        nboth = len(t)
+        t = t.filter(mask)
+        assert 2 * len(t) == nboth or alt.endswith("min400")
+        s = pc.index_in(t["protein1"], value_set=ids)
+        u = pc.index_in(t["protein2"], value_set=ids)
+        assert s.null_count == 0 and u.null_count == 0
+        s = s.to_numpy(zero_copy_only=False)
+        u = u.to_numpy(zero_copy_only=False)
+        g.add_edge_list(np.column_stack((s, u)).astype("int64"))
+        for c in channels:
+            g.ep[c] = g.new_ep("int16_t", vals=t[c].to_numpy())
+        del t
+        yield alt, g