AI-Powered
Drug Discovery.
Built Different.
We bring AI, machine learning, and data management together in a proprietary platform engineered to compress the drug discovery timeline—from target identification to lead candidate, faster than any conventional approach.
Where We Operate in Your Pipeline
Hyperionsoft brings focused AI expertise to the three highest-leverage points in drug discovery— each pillar is a distinct capability, not a one-size-fits-all platform.
Target Identification & Validation
We deploy graph neural networks and multi-omics integration to identify and prioritize novel disease targets from genomic, proteomic, and clinical data—cutting the target-hunting phase from years to months.
- Multi-omics data fusion (genomics, proteomics, metabolomics)
- Disease pathway network analysis
- CRISPR screen data interpretation
- Biomarker discovery and stratification
- Target druggability scoring
ADMET Prediction
Accurately predicting absorption, distribution, metabolism, excretion, and toxicity early eliminates costly late-stage failures. Our multi-task deep learning models forecast ADMET profiles at the hit identification stage—before a single compound enters the lab.
- Oral bioavailability and membrane permeability prediction
- CYP450 inhibition and metabolic stability modeling
- hERG cardiotoxicity and off-target safety screening
- Blood-brain barrier penetration prediction
- Multi-task models trained on proprietary + public assay data
Molecular Design & De Novo Drug Design
From scaffold hopping to designing entirely novel candidates from scratch, our generative platform covers the full design spectrum. Constrained by target geometry, ADMET profiles, and synthesizability, it explores chemical space faster and broader than any conventional approach.
- De novo molecular generation (diffusion / VAE / transformer)
- Structure-based generative design from target binding site
- Scaffold hopping and fragment-based lead optimization
- Retrosynthetic route planning and synthesizability scoring
- Multi-parameter optimization (potency + selectivity + safety)
We Are Not a Platform.
We Are Your Science Team's
AI Partner.
Generic AI platforms provide tools. We embed with your scientists to build models trained on your proprietary data, tuned to your specific targets, and validated against your assay readouts. The difference is measurable in cycle time.
Proprietary-Data First
Your internal assay data, hit libraries, and genomic datasets drive model training—not generic public corpora.
Wet-Lab Closed Loop
Computational predictions feed directly into synthesis and screening queues. Experimental feedback continuously retrains models.
Regulatory Fluency
We understand IND requirements and FDA's AI/ML guidance. Models come with uncertainty quantification and audit trails.
Every atom
matters.
Every bond
accelerates
the cure.
How an Engagement Works
From first conversation to production models—a structured six-stage process designed around your existing workflows.
Technology Partners
Google Cloud
Amazon Web Services
Microsoft Azure
Nvidia
CoreWeave
Databricks
Hugging FaceLive Molecular
Network Simulation
Move your cursor over the canvas to see how our models explore chemical space— atoms repel, bonds form and break, new configurations emerge. This is a simplified metaphor for the generative chemistry models we deploy in production.
- Oxygen / Polar
- Nitrogen
- Carbon
- Sulfur / Phosphorus
- Other
Download Our
Drug Discovery AI
Capabilities Brief
A detailed 12-page overview of our technical approach, benchmark results, case studies, and partnership models. Intended for research leads, CSOs, and business development teams evaluating AI partnerships.
- Technical architecture and model benchmarks
- Three pharma case studies with quantified outcomes
- Data requirements and integration checklist
- Engagement models and timeline expectations
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Affiliated with USC Alfred E. Mann School of Pharmacy
Cho-Nan Tsai, Founder and CEO of Hyperionsoft, serves as Adjunct Professor at the USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences. His appointment reflects both active engagement with pharmaceutical science research and a commitment to translating academic rigor into production-ready AI systems for drug discovery.
Ready to Accelerate Your Pipeline?
Book a 30-minute discovery call with our computational biology team.
Schedule a Discovery Call