Hypothesis overload
Discovery teams often generate more target, biomarker, and indication hypotheses than they can rigorously evaluate. Promising opportunities may therefore be overlooked because of limited time and analytical capacity.
Two ways to move discovery forward
Choose the kind of support your team needs today. The two offerings can work independently or together as your scientific and organizational needs evolve.
IN DETAIL
Explore each offering
See how each offering works, the problems it addresses, and what an engagement can deliver.
01
Discovery Partners
Make better scientific decisions
We work as an extension of biotech discovery teams to answer high-value scientific questions across target discovery and preclinical development.
Ranked candidate targets · illustrative
01
Problems we help solve
Where discovery teams lose time
Fragmented scientific evidence
Public datasets, scientific literature, multi-omics profiles, and internal experiments each provide part of the answer. Integrating these sources into a coherent, decision-ready view is often one of the most time-intensive parts of discovery.
Late identification of translational risk
A target may appear promising in early studies but weaken when evaluated against disease biology, mechanism of action, patient heterogeneity, or independent evidence. Identifying these uncertainties earlier helps teams make better-informed preclinical decisions.
02
Questions we help answer
Typical engagements start here
- Q1Which therapeutic targets should we prioritize?
- Q2Does the data support the proposed mechanism of action?
- Q3Which biomarkers are associated with treatment response?
- Q4How do findings align across multi-omics datasets?
- Q5What experiment or analysis should we run next?
03
Core capabilities
What we actually deliver
01
Target Discovery and Prioritization
Identify and rank candidate targets by integrating public datasets, literature, multi-omics data, and internal experimental evidence.
- Novel target discovery
- Target prioritization
- AI-assisted target identification
- Multi-omics integration
02
Target and Biomarker Validation
Assess the strength and consistency of the evidence supporting a target or biomarker before it advances further in the preclinical pipeline.
- Biomarker discovery
- Responder analysis
- Mechanism-of-action assessment
- Public-data validation
03
Disease and Translational Insights
Place targets, biomarkers, and treatment responses within the broader context of disease biology and patient heterogeneity.
- Disease characterization
- Patient stratification
- Pathway and network analysis
- Indication expansion
What makes our approach different
We frame the scientific question with your team, select the right methods for the decision, and synthesize findings across sources rather than analyzing each in isolation.
Every recommendation includes the evidence that supports it, the evidence that challenges it, our level of confidence, and the next experiment or analysis needed to resolve the remaining uncertainty.
Example engagement
Prioritize 30 candidate targets for an oncology indication by integrating internal screening results, public functional genomics, expression evidence, literature, and disease biology. Deliver a defensible shortlist, evidence gaps, and recommended validation experiments.
Have a target, biomarker, or program decision to make?
Talk to our discovery team02
Scientific AI Enablement
Build a more intelligent discovery organization
We help biotech organizations embed AI into scientific knowledge, workflows, and research operations. Instead of building isolated tools, we create reusable systems around how your teams already work.
Connected context graph · illustrative
01
Problems we help solve
Where discovery teams lose time
Fragmented scientific and organizational knowledge
Important findings, analyses, experimental context, and decisions are scattered across documents, databases, cloud storage, literature, reports, and individual researchers.
Disconnected and repetitive workflows
Scientific analyses are repeatedly rebuilt, with limited standardization, reuse, or visibility across projects.
Individual AI adoption
AI tools are introduced without clearly defined scientific decisions, validation frameworks, or integration into day-to-day research.
02
Questions we help organizations answer
Typical engagements start here
- Q1How can researchers reliably access previous analyses, findings, and decisions?
- Q2Which scientific workflows should we standardize or automate first?
- Q3How can AI work across our existing data, tools, and infrastructure?
- Q4How do we validate AI outputs before they influence research decisions?
- Q5How can knowledge and workflows improve with every project?
03
Core capabilities
What we actually deliver
01
Scientific Knowledge and Context Systems
Connect internal evidence, literature, experimental history, previous analyses, and decisions into reusable organizational memory.
- Organizational knowledge graph
- Scientific knowledge assistant
- Literature assistant
02
Reusable Scientific Workflows
Capture repeatable research and analysis processes as controlled workflows with clear inputs, validation, review, and outputs. Specialized scientific capabilities are drawn from the SoulBio capability stack rather than rebuilt per project.
- RNA-seq analysis workflows
- Public dataset ingestion
- Internal research pipelines
- Workflow orchestration
03
AI Integration and Orchestration
Connect existing data, tools, pipelines, and specialized scientific capabilities through an intelligent research layer, giving researchers a single interface across the systems they already use.
- Natural language research interface
- Unified scientific search
- AI research copilots
- Workflow execution
04
Evaluation, Governance, and Reproducibility
Benchmark outputs, preserve provenance, introduce human checkpoints, and ensure AI-assisted work can be reviewed and reproduced.
- AI evaluation frameworks
- Scientific benchmarking
- Workflow validation
- Human approval checkpoints
- Audit trails
- Reproducibility monitoring
What makes our approach different
We begin with the scientific work, not with a generic AI use case. Every system is designed around a defined research decision, workflow, or source of organizational friction.
We build around your existing infrastructure rather than replacing it. Knowledge systems, workflows, and AI capabilities are connected to the tools and data your teams already use.
We prioritize controlled adoption over full autonomy. Outputs remain traceable, reviewable, and subject to human approval at meaningful scientific decision points.
Example engagement
Connect program documents, prior analyses, internal datasets, and research decisions into a program-specific knowledge system, then standardize a recurring public-data analysis workflow with review checkpoints and auditable outputs.
Where is scientific work getting stuck in your organization?
Talk to our team