PRECISION BIOTECH
Engineering at the Molecular Scale
Computational biology, molecular simulation, and AI-driven drug discovery
Our precision biotech programme centres on HelixForge, our flagship computational drug discovery platform. HelixForge replaces costly wet-lab high-throughput screening with an in-silico AI pipeline — graph neural networks, molecular docking via AutoDock Vina and DiffDock, MD simulation via GROMACS and OpenMM, and closed-loop active learning. It delivers ranked candidates in 2–4 weeks with 80–90% in vitro confirmation rates. Beyond HelixForge, we are in active research on quantum-accelerated molecular dynamics and protein folding ensembles.
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. We engage selectively with partners where the scientific case is strong and the data infrastructure is ready.
Research pillars
How we work in precision biotech.
HelixForge drug discovery
AI pipeline delivering ranked small molecule, gene therapy, and antibody candidates in 2–4 weeks. GNNs, molecular docking, MD simulation, and closed-loop active learning. 80–90% in vitro confirmation rate.
Molecular simulation
Applying quantum-classical hybrid methods to conformational sampling and molecular dynamics at scales that classical MD cannot reach efficiently.
Computational structural biology
Protein folding ensembles, binding affinity prediction, and structure-based virtual screening using HyperFabric-class infrastructure for throughput.
Capabilities
What we deliver.
- AI-powered drug discovery (HelixForge)
- Small molecule screening via GNNs and molecular docking
- Gene therapy & sequence design (codon optimisation, off-target prediction)
- Antibody & protein engineering (million-variant library ranking)
- Target discovery & disease pathway mapping
- Quantum-accelerated molecular dynamics
- Protein structure prediction & ensemble modelling
- Closed-loop active learning for discovery pipelines
- High-throughput virtual screening
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. $100K–$600K 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?
We engage selectively. Tell us what you're solving and we'll respond with a clear assessment — not a generic nurture sequence.