Purpose-Built Models
Domain-specific models fine-tuned for pharmaceutical reasoning. Every output is checked against our physics engine before users see it.
We pair LLM reasoning with deterministic physics solvers. The AI proposes; physics validates.
The agent proposes a molecule. The physics engine calculates the energy barrier. If the physics fail, the agent is corrected immediately: a feedback loop that gets more accurate with each iteration.
Data flows up. Constraints flow down.
Formulation is a trade-off game (e.g., Solubility vs. Stability). We employ Pareto-Frontier Optimization to identify the non-dominated set of optimal formulations. Instead of a single guess, we present scientists with the mathematical best-case scenarios for their specific constraints.
Generative models are prone to unfeasible outputs. We prevent impossible structures before generation. Hard rules on Valency, Stoichiometry, and Solubility Principles are built into the decoder, not applied as post-filters.
Standard LLMs hallucinate because they lack physical intuition. We solve this by coupling the generative engine with external thermodynamic solvers. This allows the model to 'feel' the energy barriers of a formulation, forcing it to validate its own hypotheses against physical laws before it generates a single token.
We transform unstructured biomedical data into a high-fidelity, vector-embedded ontology. Unlike standard vector databases, our fabric enforces strict chemical logic, mapping disparate terminologies into single entities. We ensure the model understands that 'functionally equivalent' is not the same as 'textually related.'
Traditional Design of Experiments (DoE) is static and data-hungry, contributing to the $3.6M cost crisis. We use Bayesian Optimization to explore the chemical search space, autonomously picking the next simulation that maximizes information gain with minimal compute.
Traditional DoE samples uniformly across the entire search space, testing many low-value regions.
Our Bayesian approach intelligently concentrates samples in high-probability regions, maximizing information gain.
Beyond prediction: the model calculates its own confidence interval, flagging low-confidence regions for targeted physics simulation.
Instead of random sampling, we use sophisticated acquisition functions (Expected Improvement / Upper Confidence Bound) to target only the areas of the chemical space with the highest probability of success.
While competitors need thousands of data points to converge, our Adaptive Sampling engine can identify optimal formulation candidates with 80% fewer simulations, reducing computational overhead and time to results by 80%.
“99% accuracy is not enough.”
Large Language Models maximize the plausibility of the next token, not the truthof the statement. In pharmaceutical development, this “hallucination” is a catastrophic liability.
A single hallucinated citation or unfeasible reaction step triggers a Clinical Hold or a Complete Response Letter (CRL), delaying market entry by years.
“Generative models hallucinate molecular structures at a rate of 15-40% without external constraints. Standard retrieval systems fail to detect contradictory evidence in 36% of pharmaceutical formulation queries.”
Fig 02: The Intervention Layer. The Reality Anchor sits between the LLM and the User, enforcing dual validation streams: physics verification for molecular structures and evidence verification for formulation information, before output reaches users.
When the evidence check finds a citation that conflicts with a higher-tier study, the Reality Anchor flags the conflict and surfaces the rejection rather than returning a confident-but-wrong answer.
ECHA IUCLID Dossier 100.005.235
NOAEL (rat, 90-day oral) · ECHA REACH
GLP-compliant 90-day oral gavage study in Sprague-Dawley rats (5/sex/dose) at 0, 50, 150, 500 mg/kg/day. NOAEL of 150 mg/kg/day established; LOAEL of 500 mg/kg/day driven by hepatic centrilobular hypertrophy.
EFSA peer-reviewed re-evaluation (2023)
NOAEL (rat, 90-day oral) · EFSA
Authoritative re-evaluation citing the underlying ECHA dossier. Concurs with NOAEL of 150 mg/kg/day for the same critical effect.
Mortelmans et al. PMID 3781131
NOAEL (rat, 90-day oral) · PubMed
Peer-reviewed primary publication of the same 90-day oral toxicity study program. Methodology documented in detail; conclusions match the ECHA dossier.
NTP TR-403 (secondary citation, trade publication 2019)
NOAEL (rat, 90-day oral) · Industry whitepaper
Reports a NOAEL of 50 mg/kg/day citing NTP TR-403. The cited NTP study uses a different strain (F344, not Sprague-Dawley) and a different route (drinking water, not gavage).
Rejected by Reality Anchor: Strain and route mismatch; superseded by Ks 1 ECHA dossier.
Fig. Klimisch citation list with the rejected entry surfaced.
The entire pharmaceutical industry runs on RAG. And RAG is a ticking regulatory bomb.
Retrieval-Augmented Generation was built for customer support tickets and blog summarization, not for life-or-death formulation decisions. It assumes retrieved evidence is correct. It ignores contradictions. It treats 2019 guidance identically to 2024 safety warnings.
In pharma, this isn't a feature gap. It's malpractice.
“Standard RAG exhibits three fatal flaws in pharmaceutical contexts: confirmation bias, temporal blindness, and authority collapse. These design choices render RAG architecturally unsuitable for regulated healthcare.”
The first system in pharma to actively hunt for contradictions. Parallel support and refutation channels search for both confirming and contradictory evidence simultaneously.
Hard temporal cut-offs that standard RAG cannot enforce. 365-day validity windows ensure evidence respects regulatory update cycles and safety findings.
Regulatory sources dominate by design, not by chance. Authority-aware ranking ensures FDA guidance and compendial monographs outrank secondary materials.
Entailment-aware precision layer. Natural language inference verifies logical consistency between claims and evidence before presentation.
Retrieval accuracy vs. industry-standard baseline (nDCG: 0.4124 → 0.8335)
Contradiction detection in formulation queries (nDCG@6)
Model confidence reliability (ECE) for safety-critical decisions
Adversarial perturbation resistance (numeric dosage variations)
This is not an incremental RAG improvement.
We rebuilt retrieval from first principles for regulated environments.
Fig 01: Validated Performance. Tested on FFS-Lite benchmark (516 pharmaceutical formulation claims, 2,847 regulatory documents). 102% improvement over industry-standard retrieval demonstrates that contradiction-aware architecture produces meaningful accuracy gains.
DeepCeutix is a governance layer for pharmaceutical AI. It lets pharma companies deploy generative AI without scientific error or IP exposure.
Domain-specific models fine-tuned for pharmaceutical reasoning. Every output is checked against our physics engine before users see it.
Every generated output is treated as "untrusted" until it passes our validation layer. No hallucination makes it to the user interface. If the physics check fails, the token is rejected.
Every decision is logged with a chain of reasoning, peer-reviewed citations, and physics checks. This provides a complete audit trail (Audit Trail Review) required for FDA regulatory submission.
Standard RAG only searches for supporting evidence. We built bi-directional retrieval: searching for both confirmation and contradiction. Temporal validity enforced at the architecture level.
Tell us what you are working on (molecule, indication, stage, open question).
A formulation lead will be in touch within a business day.