The Ultraceuticals Platform

The Tri-Modal Biopharmaceutical Architecture

Four years of proprietary deeptech engineering by Fortem Biosciences have created a unified pharmaceutical operating system. By fusing generative de novo design, knowledge graph repurposing, and solid-state formulation AI, we bypass the conventional multi-billion-dollar trial bottleneck.

Platform Release: ReviveAI v5.0Formulation Engine: EnformisRegulatory Standard: FDA 21 CFR 320/314
PILLAR 01: GENERATIVE DE NOVO DISCOVERY

DiscoveraAI™

DiscoveraAI leverages conditional generative diffusion architectures, DTPred AI target prediction, and multi-modal transformers to explore chemical latent space (over 10^60 small molecules) and targeted biologics. Unlike brute-force virtual screening, DiscoveraAI designs de novo chemical entities that simultaneously optimize pocket affinity, synthesizability, and drug-like ADMET profiles.

Technical Specifications:

Target-conditioned 3D pocket generation
Multi-parameter optimization (Pareto MPO)
Retrosynthetic route verification (SAScore < 2.5)
Sub-nanomolar target binding confirmation
3D Protein Structure Folding (FoldPredict™)
AlphaFold 2 Kinase Pocket (PDB: 7K59)
Conformational Folding TrajectoryRMSD: 0.84 Å | pLDDT Score: 94.6
Active Pocket PredictionHIGH CONFIDENCE (pLDDT: 94.6)
Lead Candidate: ULTR-DISC-098 | Kd: 0.84 nM | LogP: 2.89 | SAScore: 2.01
PILLAR 02: KNOWLEDGE GRAPH CONTRASTIVE REPURPOSING

ReviveAI™ (SCALEX)

ReviveAI is our clinical-grade, computational drug repurposing enterprise platform. Traditional drug repurposing relies on serendipity or basic phenotypic screens. ReviveAI models the human biological interactome as a multi-relational heterogeneous knowledge graph (1.8M nodes, 42M biological edges) and applies node-level contrastive GNNs with bi-encoder architectures.

Technical Specifications:

Bi-Encoder Contrastive Learning Architecture
Modality Dropout Defense against noisy omics
100% Offline / HIPAA-compliant secure deployment
Accelerates Phase II entry by up to 6 years
SCALEX Heterogeneous Graph Node1.8M GRAPH NODES

> Query Target: CHIT1 (Chitinase-1 Macrophage Biomarker)

> Evaluating Cross-Graph Cosine Similarity across 4,200 Approved APIs...

Repurposed Candidate: Scalex-ULTR-08
Cosine Similarity: 0.9412 [P < 10^-8]
Indication Match: Amyotrophic Lateral Sclerosis (ALS)
Validated in In Vitro Microglial Polarization Assays

> Patent Non-Infringement Cleared (Freedom-to-Operate)

PILLAR 03: PREDICTIVE SOLID-STATE & FORMULATION AI

Enformis™

90% of promising drug candidates fail or incur multi-year delays due to poor formulation, low aqueous solubility, or rapid thermal degradation. Enformis is our latest breakthrough: a fine-tuned ChemBERTa solid-state formulation risk predictor. It forecasts thermal onset degradation (Tonset ±3.8°C), hydrolysis pathways, and excipient compatibility matrices with Bayesian active learning precision.

Technical Specifications:

ChemBERTa neural transformer fine-tuned on 106 seed APIs
Thermal onset degradation accuracy: ±3.8 °C RMSE
ICH Q1A Accelerated shelf-life kinetic simulation
Excipient-API co-crystal compatibility score
Solid-State Crystalline Habit & Excipient Packing (Enformis™)
Enformis™ Co-Crystal Matrix (ULTR-FORM-204)
Monoclinic P2₁/c Co-Crystal LatticeThermal Degradation: Tonset = 214.6°C (ICH Q1A Stable)
Thermal Degradation & Shelf-LifeICH Q1A VERIFIED
Tonset: 218.4°C (±3.8°C) | Excipient: PVP K-30 + D-Mannitol | Real-Time Shelf-Life: 36+ Mos

100% Offline & HIPAA Isolated

Our enterprise clusters operate with zero external telemetric dependencies, guaranteeing proprietary clinical and molecular IP remains air-gapped and protected.

NVIDIA High-Throughput Compute

Accelerated with NVIDIA L4 Tensor Core GPUs and CUDA 13.0, running millions of chemical inferences in fractions of a second.

FDA 21 CFR Part 11 Audit Trail

Every inference, scoring iteration, and candidate triaging event is cryptographically logged to fulfill stringent regulatory electronic submission mandates.