PRECISION BIOTECH
Engineering at the Molecular Scale
Computational biology, molecular simulation, and AI-driven drug discovery
Our precision biotech programme spans the full discovery-to-clinic arc. TargetIQ answers which target is worth investing in — multi-omics integration and knowledge-graph GNNs deliver 8–15 ranked targets in 2–4 weeks. HelixForge, our flagship computational drug discovery platform, delivers ranked small-molecule candidates via GNNs, molecular docking, and closed-loop active learning. GeneForge specialises in CRISPR guide design and gene therapy payloads; ProteinForge in antibody and protein engineering; ClinicalSim in PK/PD, toxicity, and trial success simulation before Phase II. All platforms are production-ready with senior-led delivery.
Our methodology
Biotech work follows the same epistemics as our AI and quantum lines: rigorous benchmarking, published methods, and a clear line from research to production deployment. Programs flow TargetIQ → modality platform (HelixForge / GeneForge / ProteinForge) → ClinicalSim where clinical de-risking is required.
Research pillars
How we work in precision biotech.
TargetIQ — target discovery
Ranked, druggable disease targets with evidence dossiers in 2–4 weeks. Multi-omics integration, knowledge-graph GNNs, and druggability scoring — before any chemistry begins.
HelixForge — small molecule discovery
AI pipeline delivering ranked small molecule candidates in 2–4 weeks. GNNs, molecular docking, MD simulation, and closed-loop active learning. 80–90% in vitro confirmation rate.
GeneForge & ProteinForge — modality platforms
GeneForge: CRISPR guides and codon-optimised gene therapy payloads in 10–14 days. ProteinForge: ranked antibodies and engineered proteins in 2–4 weeks with developability-first scoring.
ClinicalSim — clinical simulation
PK/PD modelling, toxicity prediction, and Monte Carlo virtual trials predict Phase II/III outcomes before enrollment. 73–93% concordance with observed clinical outcomes.
Capabilities
What we deliver.
- Disease target discovery (TargetIQ)
- AI-powered small molecule discovery (HelixForge)
- CRISPR guide design and gene therapy payloads (GeneForge)
- Antibody and protein engineering (ProteinForge)
- Clinical trial outcome simulation (ClinicalSim)
- Multi-omics integration and knowledge-graph GNNs
- Molecular docking and MD simulation
- Closed-loop active learning for discovery pipelines
- Quantum-accelerated molecular dynamics (research)
Portfolio · Precision Biotech
Projects in this sector.
HelixForge
AI-powered drug discovery — target to lead in 2–4 weeks
Replaces costly wet-lab HTS with an in-silico AI pipeline: graph neural networks, molecular docking, MD simulation, and closed-loop active learning. 80–90% in vitro confirmation rate vs 30–40% for standard virtual docking. Four modalities: target discovery, small molecule, gene therapy, and antibody engineering.
View projectTargetIQ
Ranked disease targets before chemistry
Multi-omics AI engine integrating GWAS, transcriptomic, proteomic, and single-cell data with knowledge-graph GNNs. Delivers 8–15 druggable targets with portfolio-ready evidence dossiers in 2–4 weeks — upstream of chemistry platforms.
View projectGeneForge
CRISPR guide design and genomic optimisation
Genome-wide guide screening, off-target prediction, base editor optimisation, and AAV codon optimisation. Top 10–20 validation-ready sequences in 10–14 days with 85–95% wet-lab confirmation for top-ranked guides.
View projectProteinForge
AI antibody and protein design
Protein language models, AlphaFold-Multimer, inverse folding, and developability scoring deliver ranked biologics in 2–4 weeks. 70–85% cost reduction vs phage display with 65–80% expression success for top candidates.
View projectClinicalSim
Predict trial outcomes before Phase II
Population PK/PD, PBPK, mechanistic toxicity, and Monte Carlo virtual trials predict Phase II/III outcomes before enrollment. 73–93% concordance with observed clinical outcomes; $2.4M–$16M capital preserved per program.
View projectMolecular Dynamics Research
Quantum-accelerated conformational sampling
Applying hybrid quantum-classical methods to molecular dynamics at scales classical MD cannot reach efficiently. Early-stage research in partnership with computational biology collaborators. Methods are being prepared for publication.
View projectAI-Guided Drug Discovery
Meta-learning applied to target identification
Meta-learning and architecture search applied to drug-target interaction prediction and ADMET profiling. Fewer wet-lab iterations per validated candidate. Active research with select pharmaceutical partners.
View projectComputational Structural Biology
Protein folding and binding affinity prediction
Protein folding ensemble modelling, structure-based virtual screening, and binding affinity prediction using HyperFabric-class infrastructure for throughput. Building toward production-grade computational screening pipelines.
View projectWorking on a problem in precision biotech?
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