The average drug takes 12–15 years and $2.6 billion to reach patients. 90% of candidates fail in clinical trials — most because the underlying biology was misunderstood at the hypothesis stage. The bottleneck is not chemistry or manufacturing. It is the speed and quality of mechanistic reasoning at the earliest stage of discovery.
Average cost to develop a new drug
$2.6B
Deloitte Centre for Health Solutions, 2023
Clinical trial failure rate
90%
Hay et al., Nature Biotechnology
Average time from discovery to approval
12–15 yrs
FDA Drug Development Process
Failures due to poor target selection
~40%
Sun et al., Drug Discovery Today
Step 2 of 5 · The System
Your autonomous scientific partner.
SomaNav is not a search engine or a literature summariser. It is an autonomous reasoning system that generates novel mechanistic hypotheses, validates them against live evidence, and continuously improves its own knowledge model. It does not wait to be asked — it identifies what to research next.
Every iteration of SomaNav's research loop follows a four-phase cycle. Each phase is executed by a specialised reasoning module. The loop runs autonomously — no human prompt required between iterations. The system decides what to investigate, retrieves live evidence, evaluates its own output, and updates its knowledge model.
01
Plan
SomaNav decomposes the research goal into specific sub-hypotheses and identifies which mechanisms, pathways, and entities to investigate in this iteration.
02
Execute
Live queries to PubMed, Semantic Scholar, and the Soma Knowledge Graph. Evidence is retrieved, cross-referenced, and synthesised into mechanistic reasoning.
03
Critique
An independent evaluation pass scores the execution output on mechanistic coherence, evidence quality, novelty, and clinical relevance. Scores below 6 trigger a recovery strategy.
04
Reflect
SomaNav updates its internal model: new nodes are added to the SKG, memory entries are written, and the next iteration's strategy is adjusted based on what was learned.
Self-Improvement
SomaNav designed its own iteration allocation strategy — prioritising self-improvement sessions, merging overlapping research streams, and protecting paused sessions from premature restart. This is not scripted behaviour. It is the system reasoning about its own resource allocation.
Step 4 of 5 · Validated Insights
Hypotheses that matched the evidence.
These insights were generated autonomously by SomaNav before the clinical papers cited below were reviewed. Each independently converged on a mechanism subsequently validated in published research. Specific molecular pathway details are redacted pending patent filing — full mechanistic data is available under NDA.
GLP-1 / Alzheimer's · Iteration 4
GLP-1 Receptor Agonism in Neurodegeneration — Mechanistic Convergence
SomaNav identified a specific receptor-mediated signalling cascade linking an established metabolic target to a key neurodegeneration mechanism. This pathway was subsequently confirmed in published clinical research. Full mechanistic detail available under NDA.
🔒Molecular pathway redacted — NDA required for full mechanistic detail
Confidence: 81%
Novelty: 8/10
GLP-1 / Alzheimer's · Iteration 7
GLP-1 Receptor Agonism — Neuroprotective Axis
SomaNav identified a specific receptor-mediated signalling cascade linking an established metabolic target to a key neurodegeneration mechanism. This pathway was subsequently confirmed in published clinical research. Full mechanistic detail available under NDA.
🔒Molecular pathway redacted — NDA required for full mechanistic detail
SomaNav identified a receptor-mediated anti-inflammatory pathway relevant to neurodegeneration. The specific inflammatory mediators and signalling intermediates are available under NDA.
🔒Molecular pathway redacted — NDA required for full mechanistic detail
SomaNav identified a dual-target combination approach for lipid modulation with mechanistic synergy across two independent pathways. Specific targets and proposed combination are available under NDA.
🔒Molecular pathway redacted — NDA required for full mechanistic detail
Confidence: 88%
Novelty: 9/10
Longevity / NAD+ · Iteration 45
NAD+ Precursor — Mitochondrial Biogenesis Cascade
SomaNav identified a specific NAD+ precursor-mediated cascade linking cellular energy metabolism to mitochondrial biogenesis. The full pathway and intervention proposal are available under NDA.
🔒Molecular pathway redacted — NDA required for full mechanistic detail
Confidence: 84%
Novelty: 8/10
Step 5 of 5 · Translational Bridge
From hypothesis to the lab bench.
Computational hypotheses only create value when they reach experimental validation. SomaNav is designed to be the first stage of a translational pipeline — not a standalone tool. The roadmap below shows how SomaNav's outputs connect to experimental and clinical validation.
The Translational Pipeline
SomaNav operates at Stage 1 — hypothesis generation and mechanistic reasoning. Its outputs are structured to be directly actionable: each insight includes the specific molecular targets, pathways, and proposed interventions that a CRO or academic lab can test. The Formulation Lab extends this into physicochemical and bioactivity profiling, providing the data needed to prioritise candidates for in vitro validation.
Stage 1 — Now
Hypothesis Generation
→
Stage 2 — Roadmap
ADMET Prediction
→
Stage 3 — Roadmap
CRO In Vitro Validation
→
Stage 4 — Future
Clinical Translation
Investment Thesis
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 by grounding every claim in live evidence and mechanistic reasoning. The system gets better with every iteration — not because it is retrained, but because it accumulates structured knowledge in the Soma Knowledge Graph that informs every subsequent session.