IDH1-iCCA Research PlatformIDH1-iCCA Forschungsplattform

Research directions under active exploration — from spatial multi-omics to engineered probiotics. Each direction connects to specific molecular targets and therapeutic modalities.Forschungsrichtungen in aktiver Erforschung — von spatialer Multi-Omics bis zu gentechnisch veränderten Probiotika. Jede Richtung ist mit spezifischen molekularen Zielen und therapeutischen Modalitäten verknüpft.

Genes, proteins, and pathways implicated in IDH1-iCCA pathogenesis, scored across multiple evidence dimensions. Primary target — IDH1 (isocitrate dehydrogenase 1), the causally mutated oncogene producing the 2-hydroxyglutarate oncometabolite. Key co-targets — FGFR2 (frequently co-altered in iCCA, ~15%), TP53 (tumor suppressor), ARID1A/BAP1 (chromatin remodeling). Druggable targets — mTOR pathway, VEGFA (angiogenesis), ERBB2 (HER2), BRAF, KRAS, PIK3CA, MET, PDGFRA. Each target is scored on evidence depth, source diversity, druggability, and clinical validation.

IDH1-iCCA clinical trials aggregated from ClinicalTrials.gov via the v2 API with automated daily refresh. Covers all interventional and observational studies related to IDH1-mutant cholangiocarcinoma — from early Phase 1 safety trials through Phase 3 efficacy studies and post-marketing surveillance. Each trial entry includes NCT identifier, phase, enrollment, status, intervention type, primary/secondary outcome measures, and where available, published results with adverse events and participant flow data. Use the filters to explore by phase, status, intervention type, or keyword.

IDH1-iCCA therapies and pipeline candidates tracked with mechanism of action, clinical status, and computational screening data. Ivosidenib (Tibsovo) is the only FDA-approved targeted therapy for IDH1-mutant cholangiocarcinoma. Pipeline strategies include PARP inhibitors (olaparib, exploiting BRCAness), checkpoint immunotherapy (durvalumab, exploiting immune reawakening after 2-HG reduction), IDH1 vaccines (Platten DKFZ), and metabolic targeting (NAMPT inhibitors, glutaminase inhibitors). Treatment sequencing and drug interactions are critical considerations.

PubMed papers and patent literature ingested via automated daily pipeline, each analyzed for structured evidence about IDH1-iCCA biology, treatment strategies, and drug interactions. The ingestion pipeline runs daily at 03:00 UTC, querying PubMed, bioRxiv/medRxiv preprints, ClinicalTrials.gov, and Google Patents. Each abstract passes a two-layer quality filter: first an IDH1-relevance gate (must mention IDH1, cholangiocarcinoma, ivosidenib, or related terms), then a post-extraction quality gate. Sources are linked to their extracted claims — use the "With claims" filter to see which papers have been processed.

Curated omics datasets for IDH1-iCCA research. Tier 1 datasets are directly usable for cholangiocarcinoma molecular profiling and treatment response analysis; Tier 2-3 require additional QC or serve as validation.Kuratierte Omics-Datensätze für die IDH1-iCCA-Forschung. Tier-1-Datensätze sind direkt für molekulare Profilierung und Therapieansprech-Analyse bei Cholangiokarzinom verwendbar; Tier 2-3 erfordern zusätzliche QC oder dienen der Validierung.

Structured scientific assertions extracted from paper abstracts using multi-LLM analysis with rigorous quality filtering. Each claim is a single factual statement that preserves the original authors' hedging language (e.g., "may regulate" stays "may regulate" — never upgraded to definitive). Claims are typed into 12 categories (gene expression, protein interaction, drug efficacy, splicing event, biomarker, etc.), scored for confidence (0–100%), and linked to both their source paper and relevant molecular targets via 200+ alias patterns. The extraction pipeline uses a two-layer quality gate: disease-relevance filtering removes non-IDH1-iCCA contamination, and word-boundary matching prevents false target links. Click any row to see the full provenance chain: paper title, PubMed ID, abstract excerpt, extraction model, and metadata.

Phase 2: Multi-criteria ranked hypotheses scored across evidence depth, source convergence, therapeutic clarity, target strength, and novelty. Tier A = top 5 high-conviction, Tier B = medium priority, Tier C = needs more evidence. Phase 2: Multi-Kriterien-priorisierte Hypothesen bewertet nach Evidenztiefe, Quellen-Konvergenz, therapeutischer Klarheit, Zielstärke und Neuheit. Stufe A = Top 5 hohe Überzeugung, Stufe B = mittlere Priorität, Stufe C = mehr Evidenz benötigt.

Evidence-grounded, falsifiable predictions generated from convergence scoring across 5 dimensions: Volume, Lab Independence, Method Diversity, Temporal Trend, and Replication.Evidenzbasierte, falsifizierbare Vorhersagen aus Konvergenzbewertung über 5 Dimensionen: Volumen, Labor-Unabhängigkeit, Methodenvielfalt, zeitlicher Trend und Replikation. All scoring weights are transparent methodology. Each card links every claim to its source paper.

Interactive visualization of the IDH1-2HG metabolic axis — the platform’s core mechanistic model. IDH1 R132 mutation → 2-HG accumulation → epigenetic dysregulation (TET2/KDM inhibition) → blocked differentiation + DNA repair impairment (BRCAness). Toggle between IDH1-iCCA and cross-cancer views to compare pathway dysregulation across glioma, AML, and cholangiocarcinoma. Click any node for evidence details.

Multi-dimensional evidence convergence scoring across thousands of curated, quality-filtered claims extracted from 6,400+ PubMed sources. Each of the 58 molecular targets is scored across five independent dimensions: Claim Volume (raw evidence mass — how many distinct assertions support this target), Lab Independence (number of unique research groups reporting findings — guards against single-lab bias), Method Diversity (range of experimental approaches: in vitro, animal model, patient data, computational — cross-validated findings score higher), Temporal Trend (whether evidence is growing, stable, or declining over recent years — captures scientific momentum), and Replication (how often key findings have been independently confirmed across different studies and model systems). Scores are weighted and combined into a composite convergence score (0–100). All weights and methodology are fully transparent and transparent methodology. The engine generates falsifiable predictions grounded in evidence — each prediction card links every supporting claim back to its source paper for full traceability.

Bayesian back-testing of convergence scores against known drug outcomes — the critical self-check that separates rigorous research from speculation. For each drug with a known clinical outcome (approved, failed in Phase 2/3, or preclinical only), the platform asks: did our evidence scoring predict the right outcome? The calibration process works as follows: (1) Outcome collection — gather real-world drug approval/failure data from ClinicalTrials.gov and FDA records for all 21 tracked drugs. (2) Score comparison — compare each drug's convergence score against its actual clinical outcome. Approved drugs (ivosidenib, gemcitabine+cisplatin, pemigatinib) should score high; failed drugs should score low. (3) Bayesian updating — use the comparison to compute posterior probabilities, measuring how well evidence mass predicts clinical success. (4) Grade assignment — the system earns a calibration grade (A–F) based on concordance between predicted and actual outcomes. Grade A (current: 89.8%) means the scoring reliably separates successful from unsuccessful therapeutic approaches. A well-calibrated platform means researchers can trust the convergence scores when evaluating novel, untested targets.

Multi-criteria scoring across 7 dimensions: evidence strength, biological coherence, fragility relevance, interventionability, translational feasibility, novelty, and contradiction risk. Composite score determines Phase 3 priority. Multi-Kriterien-Bewertung über 7 Dimensionen: Evidenzstärke, biologische Kohärenz, Fragilitätsrelevanz, Interventionsfähigkeit, translationale Machbarkeit, Neuheit und Widerspruchsrisiko. Der Gesamtwert bestimmt die Phase-3-Priorität.

Multi-criteria decision engine integrating 6 data dimensions: evidence convergence (25%), druggability via DiffDock screening (20%), ESM-2 structural uniqueness (15%), clinical validation from drug outcomes (15%), cross-species conservation (10%), and target novelty (15%). Multi-Kriterien-Entscheidungsengine mit 6 Datendimensionen: Evidenzkonvergenz (25%), Adressierbarkeit via DiffDock-Screening (20%), ESM-2-strukturelle Einzigartigkeit (15%), klinische Validierung aus Wirkstoff-Ergebnissen (15%), Spezieskonservierung (10%) und Zielneuheit (15%).

The evidence graph connects claims to their supporting sources. Each assertion is backed by traceable references (PMIDs, clinical trial results). Grouped by source paper, sorted by claim count.Der Evidenzgraph verbindet Behauptungen mit ihren Belegen. Jede Aussage wird durch nachverfolgbare Referenzen (PMIDs, Studienergebnisse) gestützt. Gruppiert nach Quellenpublikation, sortiert nach Behauptungsanzahl.

This pipeline computationally filters thousands of ChEMBL compounds down to the best candidates for IDH1-iCCA drug discovery. The process runs in six steps: (1) ChEMBL query — compounds bioactive against top-scored IDH1-iCCA targets are fetched with their SMILES strings; (2) RDKit descriptor calculation — molecular weight, LogP, rotatable bonds, H-bond donors/acceptors, TPSA, and QED are computed from SMILES; (3) Lipinski Rule of 5 — MW < 500, LogP < 5, HBD ≤ 5, HBA ≤ 10; compounds failing two or more rules are flagged as non-drug-like; (4) Hepatic bioavailability estimate — TPSA and MW thresholds are used as heuristics for oral absorption and hepatic distribution (note: BBB penetration is not required for liver cancer); (5) Drug-likeness MPO score — a 0–6 composite of LogP, LogD, MW, TPSA, HBD, and pKa tuned for oncology drug development; (6) PAINS filter — substructure alerts for pan-assay interference compounds that cause false positives in biochemical screens.

Drug delivery considerations for IDH1-iCCA: Intrahepatic cholangiocarcinoma is located within the liver — where drugs must achieve adequate hepatic concentrations. Unlike CNS tumors, BBB penetration is not a barrier. However, liver-first-pass metabolism and CYP3A4 interactions are critical considerations. Ivosidenib has good oral bioavailability but is extensively metabolized by CYP3A4. Compounds must achieve adequate hepatic concentrations through portal blood flow and avoid excessive CYP3A4 metabolism.

Score glossary: Lipinski — binary pass/fail for oral bioavailability potential. Hepatic Bioavail. — heuristic estimate of hepatic distribution (TPSA + MW; note: BBB is not critical for liver cancer). Drug-likeness MPO — 0–6 score; ≥ 4 is considered drug-optimized. QED — 0–1 drug-likeness estimate combining eight Lipinski-adjacent properties; ≥ 0.5 is high quality. PAINS — substructure alert for reactive or promiscuous scaffolds that should be deprioritized. Diese Pipeline filtert computergestützt Tausende von ChEMBL-Verbindungen zu den besten Kandidaten für die IDH1-iCCA-Wirkstoffforschung. Der Prozess läuft in sechs Schritten: (1) ChEMBL-Abfrage — bioaktive Verbindungen gegen höchstbewertete IDH1-iCCA-Ziele werden mit SMILES abgerufen; (2) RDKit-Deskriptorberechnung — Molekulargewicht, LogP, drehbare Bindungen, H-Brücken-Donoren/-Akzeptoren, TPSA und QED werden aus SMILES berechnet; (3) Lipinski Rule of 5 — MW < 500, LogP < 5, HBD ≤ 5, HBA ≤ 10; (4) Hepatische Bioverfügbarkeit — TPSA- und MW-Schwellenwerte als Heuristiken; (5) Drug-likeness MPO Score — 0–6 Komposit für Onkologie-Wirkstoffentwicklung; (6) PAINS-Filter — Substruktur-Alarme für Pan-Assay-Interferenz-Verbindungen.

Note: Drug-likeness predictions use rule-based heuristics (Lipinski Rule of 5, TPSA-based bioavailability estimate, QED score). These are filtering tools, not validated PK/tox models.

Top Candidates

Drug repurposing means finding new therapeutic uses for existing approved drugs — bypassing the 10–15 years and $1–2B typically required for de novo drug development. Repurposed drugs have already passed safety trials, so clinical translation is dramatically faster: Phase I is often skipped and Phase II can start in 2–3 years rather than 10+.

The platform identifies IDH1-iCCA treatment candidates through three convergent strategies: (1) Cross-disease mining — drugs approved or in trials for related IDH1-mutant cancers (glioma, AML) that share molecular targets with IDH1-iCCA; (2) ChEMBL bioactivity — known compounds with high pChEMBL values (≥ 6.0, corresponding to IC₅₀ ≤ 1 µM) against top-scored IDH1-iCCA targets; (3) Pathway overlap — compounds whose known mechanism overlaps with the metabolic, DNA repair, or immune pathways dysregulated in IDH1-iCCA.

Precedent in IDH1-iCCA: Olaparib, originally a BRCA-mutant cancer drug, shows efficacy in IDH1-mutant CCA through BRCAness — its exploitation of the BRCAness phenotype created by 2-HG enables tumor-selective killing. Erlotinib showed benefit in a subset of BTC patients. Bevacizumab combined with GemCis is being evaluated in clinical trials. The platform extends this approach computationally, scoring each candidate 0–1 based on target relevance, potency, clinical phase, and pathway convergence. Click any row to see full rationale, mechanism, and target link.

Top Candidates

In drug discovery, a hit is a compound that shows measurable activity against a target of interest and passes initial computational filters. This section is the unified ranked list — the best compounds from all analysis pipelines: ChEMBL screening, cross-disease repurposing, and DiffDock virtual binding.

Each candidate passes through a 6-stage validation pipeline: (1) Computational — drug-likeness filters (Lipinski, QED, PAINS), hepatic bioavailability scoring; (2) Structural — DiffDock pose prediction against IDH1-iCCA target binding pockets, confidence scoring; (3) Analog search — ChEMBL SAR analysis to identify structurally similar compounds with known IDH1-iCCA-relevant activity; (4) ADMET prediction — rule-based absorption, distribution, metabolism, excretion, and toxicity estimates; (5) Literature review — automated PubMed search for the compound + IDH1-iCCA target co-occurrence; (6) Experimental design — suggested assay types (2-HG measurement, cell viability, immune markers) for validation.

Candidates are scored 0–1 (integrated score) and assigned a tier: Tier A (≥ 0.6) — strong multi-dimensional evidence, prioritized for experimental follow-up; Tier B (0.4–0.6) — moderate evidence, worth secondary screening; Tier C (< 0.4) — computational-only signal, lower priority. Click any row to see full molecular properties, hepatic bioavailability, DiffDock score, validation stage, and target link.

Note: ADMET predictions use rule-based heuristics (Lipinski Rule of 5, TPSA-based bioavailability estimate, QED score, PAINS substructure filters). These are computational filtering tools, not validated pharmacokinetic or toxicology models.

Ranked Candidates

Positive binding predictions from AI-driven virtual screening. Each hit goes through a 6-stage validation pipeline: computational validation, structural analysis, analog search, ADMET prediction, literature review, and experimental design.Positive Bindungsvorhersagen aus KI-gestütztem virtuellem Screening. Jeder Treffer durchläuft eine 6-stufige Validierungspipeline: Computergestützte Validierung, Strukturanalyse, Analogsuche, ADMET-Vorhersage, Literaturbewertung und Experimentdesign.

Note: ADMET predictions in the pipeline use rule-based heuristics (Lipinski, TPSA, PAINS), not validated PK/tox models.

Interactive network of IDH1-iCCA molecular targets connected by protein-protein interactions (STRING), shared pathways (KEGG/UniProt), and compound bioactivity (ChEMBL). Click a node to highlight its connections. Interaktives Netzwerk molekularer IDH1-iCCA-Ziele, verbunden durch Protein-Protein-Interaktionen (STRING), gemeinsame Signalwege (KEGG/UniProt) und Verbindungs-Bioaktivität (ChEMBL). Klicken Sie auf einen Knoten, um seine Verbindungen hervorzuheben.

Structured database of drug successes and failures in IDH1-iCCA research. Every outcome traces back to a source paper — capturing not just what worked, but why compounds failed (toxicity, bioavailability, efficacy). Strukturierte Datenbank über Wirkstoff-Erfolge und -Misserfolge in der IDH1-iCCA-Forschung. Jedes Ergebnis ist rückverfolgbar zu einer Quellenpublikation — erfasst nicht nur was funktioniert hat, sondern warum Verbindungen scheiterten (Toxizität, Bioverfügbarkeit, Wirksamkeit).

Cross-species conservation mapping of IDH1-iCCA-relevant molecular targets across 7 model organisms. Each organism offers unique advantages for IDH1-iCCA research: mice (Mus musculus) serve as the primary disease model for IDH1-mutant cholangiocarcinoma (patient-derived xenografts and genetically engineered models); zebrafish (Danio rerio) enable rapid drug screening with IDH1-mutant liver models; the rat (Rattus norvegicus) provides orthotopic iCCA models. Conservation scores are computed from NCBI Ortholog data — a score of 71% or higher indicates strong evolutionary conservation, suggesting the target's function is preserved across species and findings from model organisms are likely translatable to humans. Click any species card to see which IDH1-iCCA targets have orthologs in that organism, or click any heatmap cell to view the specific ortholog with links to NCBI Gene and STRING-DB.

Conservation Heatmap (click cells for details)

16 research directions spanning metabolic targeting, immunotherapy combinations, and evidence-based treatment strategies for IDH1-iCCA. Click any direction to see connected targets, claims, and hypotheses.16 Forschungsrichtungen von metabolischem Targeting über Immuntherapie-Kombinationen bis zu evidenzbasierten Behandlungsstrategien für IDH1-iCCA. Klicken Sie auf eine Richtung, um verknüpfte Ziele, Behauptungen und Hypothesen zu sehen.

Essential databases, tools, registries, and organizations for IDH1-iCCA researchers. Open as standalone pageWichtige Datenbanken, Tools, Register und Organisationen für IDH1-iCCA-Forscher. Als eigenständige Seite öffnen

Ask any question about IDH1-iCCA research or search across thousands of evidence claims and thousands of sources. Ask follow-up questions to go deeper.Stellen Sie Fragen zur IDH1-iCCA-Forschung oder durchsuchen Sie Tausende von Evidenz-Behauptungen und Quellen. Stellen Sie Folgefragen für tiefere Einblicke.

Generate publication-ready evidence summaries for any IDH1-iCCA target or topic. Powered by Claude Sonnet synthesizing across all platform data (claims, hypotheses, trials, drug outcomes).Erstellen Sie publikationsreife Evidenz-Zusammenfassungen für jedes IDH1-iCCA-Ziel oder -Thema. Angetrieben von Claude Sonnet, das alle Plattformdaten synthetisiert (Behauptungen, Hypothesen, Studien, Wirkstoff-Ergebnisse).

Browse 800+ AI-generated and computationally screened molecules for IDH1-iCCA drug targets. Includes MolMIM scaffold decorations, GenMol analogs, DiffDock docking results, and ML-proxy 100k virtual screen hits. Filter by target, drug-likeness, hepatic bioavailability, and more. Export as CSV (researchers) or SDF (chemists).Durchsuchen Sie 800+ KI-generierte und computergestützt gescreente Moleküle für IDH1-iCCA-Wirkstoffziele. Enthält MolMIM-Gerüstdekorationen, GenMol-Analoga, DiffDock-Docking-Ergebnisse und ML-Proxy-100k-Screeningtreffer. Filtern nach Ziel, Wirkstoffähnlichkeit, hepatischer Bioverfügbarkeit und mehr.

CRISPR guide RNA design for IDH1 R132 mutation site. Strategies: precision base editing to correct R132C/H mutations, CRISPRi to suppress mutant IDH1 expression, CRISPR-mediated gene knockout of mutant allele. 20 nt protospacer + NGG PAM, GC 40-70%, polyT filtered.CRISPR-Guide-RNA-Design für die IDH1-R132-Mutationsstelle. Strategien: Präzisions-Baseneditierung zur Korrektur von R132C/H-Mutationen, CRISPRi zur Unterdrückung der mutierten IDH1-Expression, CRISPR-vermittelter Gen-Knockout des mutierten Allels.

IDH1 R132 Mutation Site Motifs

Top Guides (All Strategies)

AAV serotype evaluation for liver-targeted gene therapy delivery. 9 capsids scored across hepatocyte tropism, liver targeting, immunogenicity (NAb seroprevalence), manufacturing feasibility, and packaging capacity. AAV8 and AAV-LK03 show highest liver tropism.AAV-Serotyp-Bewertung für lebergerichtete Gentherapie-Lieferung. 9 Kapside bewertet nach Hepatozyten-Tropismus, Leberausrichtung, Immunogenität (NAb-Seroprävalenz), Herstellbarkeit und Verpackungskapazität.

Capsid Rankings

"GitHub for Life" — every IDH1 variant is a deterministic commit with a SHA-256 hash. The oncogenic mutation (R132C/H) is a single-amino-acid gain-of-function change. Therapeutic edits are patches that restore wild-type enzyme function. Track the lineage from wild-type IDH1 through R132 mutations to corrected variants."GitHub for Life" — jede IDH1-Variante ist ein deterministischer Commit mit SHA-256-Hash. Die onkogene Mutation (R132C/H) ist eine Einzel-Aminosäure-Gain-of-Function-Änderung. Therapeutische Editierungen sind Patches zur Wiederherstellung der Wildtyp-Enzymfunktion.

Version Tree

Click any row to expand the full sequence diff, clinical significance, and population frequency.

Sequence Diffs

Each diff shows the exact nucleotide changes between parent and child variant. Position numbers refer to the IDH1 exon 4 coordinate system.

Pharmacophore-based docking score prediction for IDH1-iCCA drug candidates against 7 target binding pockets. Scores compounds from the molecule_screenings database by shape complementarity, H-bond potential, hydrophobic match, electrostatic alignment, and strain penalty.Pharmakophor-basierte Docking-Score-Vorhersage für IDH1-iCCA-Wirkstoffkandidaten gegen 7 Ziel-Bindungstaschen. Bewertet Verbindungen aus der molecule_screenings-Datenbank nach Formkomplementarität, H-Brücken-Potential und elektrostatischer Ausrichtung.

Top Predicted Binders

Machine learning surrogate trained on 4,116 DiffDock v2.2 results. Uses RDKit Morgan fingerprints (ECFP4, 2048-bit) + RandomForest to predict binding confidence ~1000x faster than physics-based docking. Enables screening millions of molecules on CPU in minutes.Machine-Learning-Surrogat trainiert auf 4.116 DiffDock-v2.2-Ergebnissen. Nutzt RDKit Morgan Fingerprints (ECFP4, 2048-Bit) + RandomForest zur Vorhersage der Bindungskonfidenz ~1000x schneller als physikbasiertes Docking.

Actual vs Predicted (Training Set)

Top 20 Feature Importances

Target Distribution

Prime editing (PE2/PE3/PEmax) assessment for IDH1-iCCA: R132C correction (restoring wild-type IDH1), allele-specific editing of the mutant allele, and 2-HG pathway disruption. Compared with approved therapies. Prime editing = reverse transcriptase + Cas9 nickase + pegRNA — no double-strand breaks.Prime-Editing (PE2/PE3/PEmax)-Bewertung für IDH1-iCCA: R132C-Korrektur (Wiederherstellung von Wildtyp-IDH1), allelspezifische Editierung des mutierten Allels und 2-HG-Signalweg-Unterbrechung. Prime Editing = Reverse Transkriptase + Cas9-Nickase + pegRNA — keine Doppelstrangbrüche.

Therapy Comparison

Molecular Dynamics (MD) simulations model how proteins move, fold, and interact with drug molecules over time. Each simulation runs on GPU hardware using OpenMM, tracking every atom at femtosecond resolution.Molekülardynamik (MD)-Simulationen modellieren, wie Proteine sich bewegen, falten und mit Wirkstoffmolekülen interagieren. Jede Simulation läuft auf GPU-Hardware mit OpenMM und verfolgt jedes Atom in Femtosekunden-Auflösung.

Phase 7.1 — Drug penetration modeling across liver microanatomy. Maps which IDH1-iCCA drugs reach tumor compartments based on molecular properties, hepatic bioavailability, and portal blood flow. Identifies therapeutic challenges in fibrotic/cirrhotic liver tissue.Phase 7.1 — Wirkstoff-Penetrationsmodellierung über die Leber-Mikroanatomie. Kartiert, welche IDH1-iCCA-Wirkstoffe Tumorkompartimente basierend auf Moleküleigenschaften, hepatischer Bioverfügbarkeit und Pfortaderblutfluss erreichen.

Liver Microanatomical Zones

Drug Penetration

Silent Zones

Phase 7.2 — Cross-cancer pathway analysis comparing IDH1-mutant tumor biology across glioma, AML, and cholangiocarcinoma. Identifies conserved repair pathways that are dysregulated in IDH1-iCCA and could be therapeutically targeted.Phase 7.2 — Krebsübergreifende Signalweg-Analyse, die IDH1-mutierte Tumorbiologie über Gliom, AML und Cholangiokarzinom vergleicht. Identifiziert konservierte Reparaturwege, die bei IDH1-iCCA dysreguliert sind.

Regeneration Genes

Pathway Comparisons

Phase 7.3 — Tumor-stroma signaling and immune microenvironment interactions. Analyzes bidirectional signaling between IDH1-mutant tumor cells and surrounding hepatic stellate cells, immune cells, and fibroblasts.Phase 7.3 — Tumor-Stroma-Signalgebung und Immun-Mikroumgebungs-Interaktionen. Analysiert bidirektionale Signalgebung zwischen IDH1-mutierten Tumorzellen und umgebenden hepatischen Sternzellen, Immunzellen und Fibroblasten.

Retrograde Signals

EV Therapeutic Cargo

Organ-on-Chip Models

Phase 7.4 — IDH1-mutant iCCA affects multiple systems beyond the bile ducts. Metabolic dysregulation, immune suppression, and epigenetic changes create systemic vulnerabilities. Models the full systemic picture and combination therapy strategies.Phase 7.4 — IDH1-mutiertes iCCA betrifft mehrere Systeme jenseits der Gallengänge. Metabolische Dysregulation, Immunsuppression und epigenetische Veränderungen schaffen systemische Verwundbarkeiten.

Affected Organ Systems

Combination Therapies

Each strategy targets multiple disease axes simultaneously. Click a card to see drugs involved, mechanism rationale, and clinical evidence.

Energy Budget Model

IDH1-mutant tumor cells have altered energy metabolism: 2-HG production depletes NADPH, glutamine addiction drives anaplerosis, and mitochondrial dysfunction limits oxidative phosphorylation. The metabolic model compares energy supply vs. demand across normal cholangiocytes, IDH1-mutant tumor cells, and treated cells.

Phase 7.5 — Ion channel expression and membrane potential analysis in IDH1-mutant cholangiocytes. IDH1 mutation alters cellular metabolism and membrane dynamics — understanding these changes reveals additional therapeutic vulnerabilities.Phase 7.5 — Ionenkanal-Expression und Membranpotential-Analyse in IDH1-mutierten Cholangiozyten. IDH1-Mutation verändert zellulären Stoffwechsel und Membrandynamik — das Verständnis dieser Veränderungen enthüllt zusätzliche therapeutische Verwundbarkeiten.

Ion Channels

Vmem States

Electroceuticals

Phase 9.3 — Cross-cancer IDH1 analysis: glioma, AML, and cholangiocarcinoma share IDH1 mutations but show different therapeutic responses. This module maps splicing differences across IDH1-mutant cancer types to identify tumor-type-specific metabolic vulnerabilities.Phase 9.3 — Krebsübergreifende IDH1-Analyse: Gliom, AML und Cholangiokarzinom teilen IDH1-Mutationen, zeigen aber unterschiedliche therapeutische Antworten. Kartiert Spleiß-Unterschiede über IDH1-mutierte Krebsarten.

Phase 9.4 — Predicts molecular binding affinity of compounds toward IDH1 active site and allosteric pockets. Target sites mapped include the catalytic pocket (R132 mutation site), the dimer interface, NADPH binding site, and substrate binding region. Benchmarks against known inhibitors like ivosidenib and olutasidenib.Phase 9.4 — Sagt molekulare Bindungsaffinität von Verbindungen zur IDH1-Aktivstelle und allosterischen Taschen vorher. Kartierte Zielstellen umfassen die katalytische Tasche (R132-Mutationsstelle), die Dimer-Grenzfläche, NADPH-Bindungsstelle und Substrat-Bindungsregion.

Binding Sites in IDH1

Known IDH1 Inhibitors

Phase 6.1 — Compounds that simultaneously target IDH1 AND a co-occurring vulnerability (FGFR2, PARP, mTOR, or immune checkpoint). Dual-target approaches address the heterogeneous biology of IDH1-mutant iCCA.Phase 6.1 — Verbindungen, die gleichzeitig IDH1 UND eine begleitende Verwundbarkeit adressieren (FGFR2, PARP, mTOR oder Immun-Checkpoint). Dual-Target-Ansätze behandeln die heterogene Biologie von IDH1-mutiertem iCCA.

Phase 10.3 — Multi-scale computational model of the IDH1-mutant tumor cell. Simulates drug combinations across 5 compartments (tumor cell, stroma, vasculature, immune cells, bile duct lumen) and 8 signaling pathways. Predicts synergistic drug combinations in silico.Phase 10.3 — Multiskaliges Computermodell der IDH1-mutierten Tumorzelle. Simuliert Wirkstoffkombinationen über 5 Kompartimente (Tumorzelle, Stroma, Gefäßsystem, Immunzellen, Gallenganglumen) und 8 Signalwege.

Tumor Cell Compartments

Signaling Pathways

Optimal Drug Combinations

Phase 10.4 — Open-source experiment design automation. 8 standardized IDH1-iCCA assays with timeline and protocol specifications. 3 cloud lab integrations (Emerald Cloud Lab, Strateos, Opentrons). Generates complete experiment designs from hypothesis text.Phase 10.4 — Open-Source-Experimentdesign-Automatisierung. 8 standardisierte IDH1-iCCA-Assays mit Zeitplan und Protokollspezifikationen. 3 Cloud-Labor-Integrationen (Emerald Cloud Lab, Strateos, Opentrons).

IDH1-iCCA Assay Library

Cloud Lab Integrations

Phase 10.5 — Zero-knowledge data sharing framework for IDH1-iCCA research. Enables cross-institutional collaboration without sharing raw patient data. Federated learning protocols, OMOP/OHDSI data model mapping, privacy budget calculator, and 4-tier data sharing framework.Phase 10.5 — Zero-Knowledge-Datenfreigabe-Framework für IDH1-iCCA-Forschung. Ermöglicht institutionsübergreifende Zusammenarbeit ohne Rohdaten-Weitergabe. Föderierte Lernprotokolle, OMOP/OHDSI-Datenmodell-Mapping und 4-stufiges Datenfreigabe-Framework.

Federated Learning Protocols

Data Sharing Tiers

OMOP/OHDSI Mappings

Phase 11 — Translating platform discoveries into real-world impact. Regulatory pathway mapping (FDA/EMA), grant application templates, and a 5-level hypothesis validation pipeline from computational validation to IND filing.Phase 11 — Übersetzung von Plattform-Entdeckungen in reale Wirkung. Regulatorische Signalweg-Kartierung (FDA/EMA), Förderantrags-Vorlagen und eine 5-stufige Hypothesen-Validierungspipeline von computergestützter Validierung bis IND-Einreichung.

Regulatory Pathways

Grant Templates

Validation Pipeline

Gold-standard computational predictions from DiffDock, SpliceAI, ESM-2, and Cas-OFFinder. Every result is traceable to its tool version, parameters, and input data.Gold-Standard-Berechnungsvorhersagen von DiffDock, SpliceAI, ESM-2 und Cas-OFFinder. Jedes Ergebnis ist rückverfolgbar zu Toolversion, Parametern und Eingabedaten. View GPU scripts on GitHub →

Questions about the platform, data, or collaboration? Send us a message.Fragen zur Plattform, zu Daten oder Zusammenarbeit? Senden Sie uns eine Nachricht.

Research highlights, computational discoveries, and platform updates. Each post documents a specific finding with full methodology and source citations. Comment on findings and join the discussion.Forschungshighlights, computergestützte Entdeckungen und Plattform-Updates. Jeder Beitrag dokumentiert einen spezifischen Befund mit vollständiger Methodik und Quellenzitaten. Kommentieren Sie Ergebnisse und nehmen Sie an der Diskussion teil.

Predicted 3D structures for all IDH1-iCCA research targets using AlphaFold2, ESMfold, and Boltz-2. Each structure is scored by pLDDT (predicted Local Distance Difference Test) — a per-residue confidence metric that tells you how reliable each part of the structure is for drug design.

Structures predicted via AlphaFold DB v6 (EMBL-EBI), ESMfold v1, and Boltz-2 (Chai Discovery). Method badges indicate prediction source. pLDDT scores from predicted structures. MW estimates: ~110 Da per residue. Pre-existing PDB structures retain original experimental resolution.

P2Rank-predicted binding pockets across IDH1-iCCA target protein structures. Identifies the cavities on protein surfaces where small molecules can bind — the first step in structure-based drug design.

Pocket predictions via P2Rank 2.5.1 with AlphaFold-optimized configuration. Druggable flag requires score > 50 AND probability > 0.8. SAS points = Solvent Accessible Surface connolly dots defining pocket boundary.

ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions for 21,000+ compounds across all IDH1-iCCA targets. Compounds are scored for drug-likeness (QED), hepatic bioavailability, Drug-likeness Multi-Parameter Optimization (MPO), Lipinski Rule-of-Five compliance, and physicochemical properties (MW, LogP, TPSA, HBD, HBA). Filter by properties to identify the most promising liver-targeted drug candidates for IDH1-iCCA.

Non-obvious connections across thousands of curated claims from different papers — the platform's core differentiator. While individual papers report isolated findings, cross-paper synthesis reveals hidden patterns: targets that co-occur in unrelated studies, shared mechanisms between seemingly independent pathways, and transitive bridges (if Paper A links X→Y and Paper B links Y→Z, the platform discovers X→Z). The analysis builds a co-occurrence matrix across all claims, identifies statistically significant target pairs, and generates synthesis cards that explain the biological connection with full citation trails. This is how the platform discovered the IDH1-2HG-TET2-BRCAness axis as a therapeutic vulnerability — no single paper described the complete pathway, but the synthesis engine connected findings from 12+ independent publications.

AI-predicted drug-target synergy scores combining docking affinity, literature evidence, pathway overlap, and claim support. Identifies the most promising multi-mechanism therapeutic combinations for IDH1-iCCA.KI-vorhergesagte Wirkstoff-Ziel-Synergiewerte aus Docking-Affinität, Literaturbefunden, Signalweg-Überlappung und Behauptungsunterstützung. Identifiziert vielversprechendste Multi-Mechanismus-Therapiekombinationen für IDH1-iCCA.KI-vorhergesagte Wirkstoff-Ziel-Synergiewerte, die Docking-Affinität, Literaturbefunde, Signalweg-Überlappung und Behauptungsunterstützung kombinieren. Identifiziert die vielversprechendsten Multi-Mechanismus-Therapiekombinationen für IDH1-iCCA.

DiffDock v2.2 docking predictions. Extended campaign: 224 dockings across 8 targets (IDH1, FGFR2, PARP1, mTOR, and more), plus 378-compound batch screen.DiffDock v2.2-Docking-Vorhersagen. Erweiterte Kampagne: 224 Dockings über 8 Ziele (IDH1, FGFR2, PARP1, mTOR und mehr), plus 378-Verbindungen-Batch-Screening. View protein binders and AI-generated molecules in GPU Results →

Auto-generated comprehensive research summary for external collaborators, professors, and grant reviewers.Automatisch generierte Forschungszusammenfassung für externe Mitarbeiter, Professoren und Gutachter.

Real-time summary of platform capabilities, evidence depth, and research progress.Echtzeitübersicht über Plattformfähigkeiten, Evidenztiefe und Forschungsfortschritt.

What this platform has computed since launch. Live numbers from the database, factual milestones, and infrastructure used.Was diese Plattform seit dem Start berechnet hat. Live-Zahlen aus der Datenbank, Meilensteine und genutzte Infrastruktur.

Today's Numbers

Growth Timeline

Pipeline Stats

Computational Resources Used

Query our evidence graph programmatically. No authentication required for read access. All endpoints return JSON underProgrammgesteuert unseren Evidenzgraphen abfragen. Keine Authentifizierung für Lesezugriff nötig. Alle Endpunkte liefern JSON unter /api/v2.

Core Data Endpoints

Computational Biology

Data Export

Claim Type Reference

Python Example

import requests

BASE = "https://idh1-research.info/api/v2"

# Get scored and ranked targets
scores = requests.get(f"{BASE}/scores", params={"mode": "discovery"}).json()

for t in scores[:10]:
    print(f"{t['symbol']:10s} score={t['composite_score']:.3f}")

# Search high-confidence drug efficacy claims
claims = requests.get(f"{BASE}/claims", params={
    "claim_type": "drug_efficacy",
    "confidence_min": 0.8,
    "enriched": True,
    "limit": 100
}).json()

for c in claims:
    print(f"[{c['confidence']:.2f}] {c['predicate'][:80]}")

# Deep-dive: full evidence for a target
target = requests.get(f"{BASE}/targets/symbol/IDH1").json()
dive = requests.get(f"{BASE}/targets/{target['id']}/deep-dive").json()
print(f"Claims: {len(dive['claims'])}, Hypotheses: {len(dive['hypotheses'])}")

R Example

library(httr)
library(jsonlite)

base_url <- "https://idh1-research.info/api/v2"

# All targets with discovery-mode scores
scores <- fromJSON(content(
  GET(paste0(base_url, "/scores"), query = list(mode = "discovery")),
  "text"
))

# Top 10 by composite score
top10 <- head(scores[order(-scores$composite_score), ], 10)
print(top10[, c("symbol", "composite_score")])

# Export as CSV
resp <- GET(paste0(base_url, "/export/targets"), query = list(fmt = "csv", limit = 5000))
writeLines(content(resp, "text"), "idh1_targets.csv")

Rate Limits & Access

Citation

Fischer, C. (2026). IDH1-iCCA Research Platform — Open Evidence Graph
for IDH1-mutant Cholangiocarcinoma. https://idh1-research.info
Bryzant Labs. Accessed [date].

@misc{fischer2026idh1,
  author = {Fischer, Christian},
  title = {{IDH1-iCCA Research Platform --- Open Evidence Graph for IDH1-iCCA}},
  year = {2026},
  url = {https://idh1-research.info},
  note = {Accessed: 2026-03-25}
}

Try It Live

Select an endpoint and click Send to try the API.

Full documentation: Swagger UI | ReDoc | Last updated: 2026-03-25