De-risked
Discovery.
SomaNav is an autonomous scientific partner that generates novel, mechanistically grounded hypotheses — and improves with every iteration.
SomaNav is not a tool. It is a partner. It does not wait to be asked — it identifies what to research next, executes the investigation, critiques its own output, and updates its knowledge model. Every iteration, it becomes more capable. Every session, the graph grows richer.
The bottleneck in drug discovery is not capital. It is the quality of the hypothesis that capital is deployed against.
SomaNav compresses the hypothesis generation phase from months to hours — and improves hypothesis quality with every iteration.
Every session runs the same four-phase cycle — autonomously, without human prompting between iterations. Each phase is executed by a specialised reasoning module. The loop is self-directing: it decides what to investigate, retrieves live evidence, evaluates its own output, and updates its knowledge model.
SomaNav decomposes the research goal into specific sub-hypotheses and identifies which mechanisms, pathways, and entities to investigate in this iteration.
Live literature retrieval, structural biology enrichment, and mechanistic reasoning. Every claim is grounded in peer-reviewed evidence with full citation provenance.
The Critic module evaluates the output against five dimensions: mechanistic coherence, evidence quality, novelty, testability, and commercial relevance.
The system updates its knowledge model, writes memory entries, and adjusts the next iteration's strategy based on what was learned — and what was contested.
A self-improving research intelligence that surfaces novel, testable hypotheses — grounded in peer-reviewed literature, enriched with structural biology, and refined across every session.
Novel, testable scientific hypotheses with confidence scores, supporting evidence, and full citation provenance from peer-reviewed literature.
A persistent, cross-session graph of every entity, relationship, and contested claim — growing richer with every iteration.
Key protein nodes enriched with AlphaFold pLDDT confidence scores and PAE heatmaps — structural evidence layered directly into the knowledge graph.
20 iterations. 114 Soma nodes. 46 hypotheses. The system mapped the full intracellular cascade, identified three high-confidence therapeutic mechanisms, and flagged the amyloid hypothesis as contested — without human direction at any step.
GLP-1R activation in hippocampal neurons upregulates BDNF expression via cAMP/PKA/CREB signalling, promoting neuronal survival and synaptic plasticity.
Semaglutide-mediated GLP-1R activation inhibits GSK-3β, reducing tau hyperphosphorylation at Ser396/Thr231 and slowing neurofibrillary tangle formation.
GLP-1R agonism suppresses neuroinflammatory signalling through TNF-α and IL-6 pathway inhibition, reducing microglial activation in high amyloid-β burden regions.
SomaNav's session independently converged on the GSK-3β / Tau phosphorylation axis and the BDNF / TrkB neuroprotection pathway. Below is how those findings align with the published clinical and preclinical record.
GSK-3β hyperactivation drives Tau hyperphosphorylation at Ser396/Thr231. GLP-1R agonism restores AKT → GSK-3β inhibition, reducing neurofibrillary tangle formation. BDNF / TrkB upregulation provides parallel neuroprotection via CREB-mediated transcription.
Liraglutide ameliorates neuronal insulin resistance and reduces Tau hyperphosphorylation via AKT–GSK-3β signalling. Phase IIb trial (ELAD, 2024) showed liraglutide slowed cognitive decline in early Alzheimer's patients. Semaglutide associated with 40–70% reduced risk of first-time Alzheimer's diagnosis in T2DM cohorts.
SomaNav's mechanistic pathway was generated autonomously without access to these papers. The convergence with published clinical evidence is a measure of mechanistic coherence, not a claim of clinical efficacy. All SomaNav outputs are research hypotheses and require independent experimental validation.
15 iterations. The system independently identified ANGPTL3 as an upstream regulator of LPL activity and proposed combination therapy with Inclisiran and Evinacumab — a synergistic mechanism supported by ELIPSE trial data.
Dual PCSK9/ANGPTL3 inhibition achieves synergistic LDL-C reduction exceeding 70% in statin-refractory patients through complementary mechanisms of action.
SomaNav independently identified ANGPTL3 as an upstream LPL regulator and proposed dual inhibition with Inclisiran and Evinacumab. Here is how that maps to the clinical evidence base.
ANGPTL3 inhibits LPL activity, raising TG and LDL-C. Dual blockade of PCSK9 (via Inclisiran siRNA) and ANGPTL3 (via Evinacumab) achieves synergistic LDL-C reduction exceeding 70% in statin-refractory patients through complementary mechanisms of action.
ELIPSE HoFH trial: Evinacumab reduced LDL-C by ~49% in homozygous FH patients on maximally tolerated statins. ANGPTL3 inhibitors achieved greater TC reductions than PCSK9 inhibitors alone (−49.9% vs −21.2%).
SomaNav's dual-inhibition hypothesis was generated before these meta-analyses were published. The mechanistic logic is consistent with the ELIPSE trial data. All outputs are research hypotheses requiring independent experimental validation.
Every research tool tells you how to investigate a question. SomaNav is the first to tell you which question to investigate. After each session, Soma Compass analyses the entire knowledge graph, identifies the three highest-value unexplored territories, and proposes the next sessions — each with a mechanistic rationale, a pre-filled goal, and a one-click approval.
After each session, SomaNav reads every node, edge, confidence score, and contested relationship in the knowledge graph. It measures density, uncertainty, and commercial relevance across the entire network.
A four-dimension priority function scores each unexplored territory: knowledge gap density, confidence deficit, commercial relevance, and template coverage. The top three candidates are selected.
For each candidate, a mechanistic rationale is generated — identifying the specific Soma nodes driving the gap and writing a pre-filled goal statement ready to launch immediately.
Three proposals. You read the rationale, review the gap nodes, and click Approve. A session is created instantly with the pre-filled goal. The system executes. You direct.
Inverse edge count per node. Sparse areas of the Soma graph score highest. Contested nodes receive a 1.5× multiplier — unresolved contradictions are high-value targets.
High-weight nodes with low confidence represent important concepts that are not yet well-established. Score: (1 − confidence) × weight / 10.
Domain-weighted relevance to your active research programmes. Cross-domain proposals receive a 1.5× novelty multiplier for bridging separate fields.
Unused session templates score 0.8. Templates run once score 0.5. Templates run three or more times score 0.2. Ensures systematic coverage of the knowledge space.
SomaNav's drug discovery pipeline surfaces validated targets and hit compounds from live bioactivity databases. Every molecule is rendered from crystallographic data — the same structures used by medicinal chemists at leading pharma companies.
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