Our Science · Innovation Engineering Applied to Biotechnology

Q-REVEL+™ screening small molecules at digital scale.

Modular discovery platforms where innovation engineering, quantum-inspired computation and AI are applied directly to biotechnology decisions.

PharmGenAI designs decision engines, not just dashboards: systems that capture biological hypotheses, encode constraints, and return explainable, auditable outputs that can be executed by R&D, clinical and regulatory teams.

Innovation engineering Quantum & AI discovery Biotech-ready decisions

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Who our Licensed Candidate Programs are for

A cross-functional ecosystem of technical-educational and licensing-ready programs for organizations that need engineered, auditable decisions from biological hypothesis to portfolio governance – all grounded in biotechnology reality.

Biology & Mechanism Exploration

For discovery biology and systems teams

Programs that structure target–mechanism hypotheses, pathways, subcellular context and experiment trees, reducing noise and bias in early-stage biological decisions.

Mechanism maps Hypothesis trees

Design & Modality Engineering

For chemistry and modality design teams

Candidate programs that define design spaces, constraints, tolerance windows and prioritization rules for entities, scaffolds and formats across multiple technology platforms.

Design space Constraint mapping

Data, Modeling & In Silico

For data science and modeling teams

Programs that expose feature weights, decision matrices and model audit trails, turning complex in silico pipelines into transparent, reviewable decision-support systems.

Model audit Decision matrix

CMC, Formulation & Process

For development and manufacturing teams

Programs that organize CQAs, CPPs and control strategies, supporting robust process development, comparability thinking and lifecycle quality management.

CQAs & CPPs Control strategy

Safety, Toxicology & Risk

For non-clinical safety and risk teams

Licensed programs that highlight safety windows, uncertainty layers and structured risk registers, connecting mechanistic concerns to monitoring plans and escalation rules.

Safety windows Risk registers

Translational & Clinical Strategy

For translational, clinical and biomarker teams

Programs that connect biological rationale, biomarkers, endpoints and eligibility criteria, supporting protocol concept, feasibility thinking and cross-program comparability.

Endpoint strategy Eligibility logic

Regulatory, Access & Policy

For regulatory, access and policy teams

Candidate programs mapped to evidence tiers, scenarios and jurisdictional pathways, helping align development plans with regulatory expectations and access narratives.

Evidence tiers Pathway mapping

Technical Education & Enablement

For scientific training and onboarding

Single-program learning tracks that explain context, biomarkers, CQAs and decision trails, giving heterogeneous teams a common scientific language and shared baselines.

Scientific onboarding Shared grammar

Portfolio, Governance & Capital

For executives, investors and boards

Programs packaged with scorecards, heatmaps and decision trails that make risk–return, maturity and strategic fit visible across assets, lines of research and geographies.

Portfolio view Strategic fit

Technology Readiness Level of our Candidate Programs

We position our candidate programs as TRL 3 technology in the classical TRL 1–9 scale: experimental proof-of-concept, with structured biology, mechanisms and decision trails that can be extended by independent R&D, clinical and regulatory teams.

At this level, the program delivers a scientifically auditable starting point – not a finished clinical or commercial product. It organizes hypotheses, variables, biomarkers and endpoints to reduce uncertainty and accelerate downstream laboratory, prototype and regulatory work.

Technology Readiness Levels (TRL 1–9)

1Basic principles

2Concept formulated

3Experimental PoC

4Lab validation

5Relevant env. tests

6Prototype in use

7System demo

8Qualified system

9Full deployment

TRL 3 – candidate programs and experimental proof-of-concept.
TRL 1–2 – fundamental science and conceptual exploration.
TRL 4–6 – laboratory, pilot and prototype validation.
TRL 7–9 – regulatory-grade systems and market deployment.

Our innovation-engineering approach compresses the path from TRL 1–2 to TRL 3 with clear, reviewable decision trails. Licensees extend the asset toward TRL 4–9 through their own wet-lab, clinical, manufacturing and regulatory execution.

Q-REVEL+ Methodology — Innovation in Bioactive Discovery

Q-REVEL+ is our in silico discovery stack: quantum-inspired physics, deep learning and curated evidence working together to explore bioactive space up to 100× faster for aesthetics, cosmetics, rare diseases, neurodegenerative disorders and beyond.

Molecular Precision
Deep learning tuned to atomic-level features, guiding discovery toward candidates with higher biological plausibility.
Extreme Acceleration
Digital screening that compresses months of exploratory work into days, optimizing timelines and upstream costs.
Broad Applications
Architected for biotech – from cosmetic and dermatologic programs to complex CNS and rare-disease questions.

Unmatched Atomic Precision
Workflows that approximate supercomputer-grade detail while remaining accessible to innovation teams.
Speed & Originality
Search strategies oriented to novelty, freedom-to-operate and patentability, not only to “known” space.
Transform Bioactive Discovery
Q-REVEL+ lets startups and biotechs apply innovation engineering to bioactive design with a practical, auditable stack.

SBT Discovery Platform™ — Subcellular Decision Engine

SBT translates innovation engineering into subcellular logic: from one input question to a complete, explainable decision chain – microenvironment reasoning, conditional activation, candidate generation, docking/MD consensus, reinforced ADMET/Tox, barriers & solutions and an executive-ready report backed by ≥30 references.

Single input

Integrated phases

≥30

References

Executive report

Microenvironment → Logic
Context-before-target: pH, ROS, proteases, efflux and trafficking define AND/OR/NOT gates (τ thresholds, t_on/t_off) for subcellular selectivity.
Candidates & Modules
3–5 candidates (SMILES) with pairing rationale and modular design – e.g., trig_pH, trig_ROS, anchor_TPP, linkers and constraints matched to the microenvironment.
Docking/MD Consensus
Multiple scoring functions plus MD stability (RMSD/occupancy) combined by simple consensus, yielding approximate K_d (nM) and subcellular A/E metrics.

Reinforced ADMET/Tox
Risk flags (hERG, P-gp, Ames, DILI), PPB%, clearance and mitigations inform safety and PK subscores with transparent penalties and notes.
Idealness & L9 Plan
Multicriteria decision matrix (weights for safety, efficacy, PK, novelty, synthesis, selectivity) with GAI/CSI gates and Taguchi L9 for fast robustness scanning.
Synthesis & Executive
Minimal route briefing (2–5 steps), EHS and scale-up notes; one executive report with audit trail and ≥30 field references.

Single input → dense introduction → microenvironment & AND pairs → candidates (SMILES) → docking/MD (consensus) → ADMET/Tox (reinforced) → barriers & solutions → decision matrix (idealness; GAI/CSI) → L9 plan → synthesis briefing → executive report → audit & ≥30 references.

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Open a WhatsApp conversation in one click and explore how innovation engineering applied to biotechnology can support your pipeline, therapeutic area or investment thesis.

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