AI-powered mechanistic models that deliver causal drug insight years before traditional approaches - when a pivot costs thousands, not millions.
90% of drugs fail - not because the science is wrong, but because critical decisions are made with gut instinct when they should be made with causal reasoning.
AI alone works early where there's a corpora of data to align to targets and candidate selection. Traditional QSP takes 9-18 months. Bioleap delivers mechanistic insight in weeks - when decisions still matter.
Bioleap builds constrained, validated, auditable mechanistic models - available when decisions matter most.
Hypothesis plus public data equals first predictions. No need to wait for proprietary data - get causal reasoning when you need it most.
The Bioleap layer provides domain constraints and biological semantics. We provide the semantic layer that ensures outputs are scientifically meaningful.
Models persist and accumulate across development. Add assay data, animal data, human data - each refinement reduces uncertainty while preserving institutional knowledge.
Interactive dashboards for biologists, chemists, and decision-makers. Trace predictions to assumptions. Regulatory-ready, auditable outputs with human in the loop.
When optimising antibody candidates, understanding avidity effects across different designs is critical - but traditional QSP approaches take 9-18 months. Bioleap delivers answers in weeks.
Mechanistic models of multivalent antibody-receptor interactions capturing binding dynamics, spatial effects, and reaction kinetics. Models that serve as institutional memory - persisting across team turnover and evolving with each data stage.
Planning a Phase II trial with uncertainty about patient stratification? Variable response rates without mechanistic understanding of why some patients respond and others don't?
Patient stratification models incorporating receptor expression levels, genetic variants, and pathway activity. Models that identify mechanistic biomarkers predicting response - enabling smarter trial enrolment and adaptive dosing strategies.
Evaluating how CAR construct modifications affect T cell exhaustion? With dozens of variables across costimulatory domains, genetic knockouts, and cytokine secretion strategies, empirical testing alone takes years.
Mechanistic models of CAR-T exhaustion dynamics, simulating how design choices like 4-1BB vs CD28 costimulation, ITAM configurations, and genetic modifications (PD-1 knockout, c-Jun overexpression) affect persistence and anti-tumour activity over time.
Mechanistic models in weeks, not years. Decision support for your entire team.
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