
Anima Biotech
Visual computing runtime enabling AI models to experimentally reason in biological pathways and resolve causal biology in drug discovery.
Overview
AnimaBiotech is the company behind the Biology GPU — the first experimental runtime layer for AI in drug discovery. The platform enables AI models to visually compute inside living cells and reason experimentally across biological pathways, closing the loop between computational hypothesis generation and real cellular observation. AnimaBiotech serves pharmaceutical and biotech organizations seeking to resolve causal disease and target biology at scale, and has established strategic collaborations with AbbVie, Takeda, and Lilly across more than 20 active programs.
Powered by a proprietary technology stack, the Biology GPU directly visualizes biological processes and pathways at AI scale. Its foundational Visual Biology Model has been trained on over 2 billion proprietary cellular pathway images spanning approximately 140 cell types and 25 diseases, generated through real projects with pharma partners. This infrastructure allows AI models to generate hypotheses about biological processes and then execute AI-scale visual experiments, enabling iterative reasoning on biological mechanisms, pathway behavior, compound activity, and process dynamics.
Core Platform: Biology GPU
- Large-scale experimental infrastructure: Executes AI- and omics-derived hypotheses directly in cells across multiple disease-relevant cellular systems, covering different cell types, disease conditions, and perturbations.
- Visual computing as a new biological modality: Observes pathway activity and target effects across millions of cellular images, interpreted by a model trained on 2B+ pathway images across ~140 cell types and 25 diseases.
- Parallel experimental execution: Resolves disease and target biology at scale simultaneously, rather than one target at a time.
- BioGPU Agent: Enables scientists or large language models (LLMs) to design experiments, run visual compute, and iteratively test hypotheses against observed pathway activity in an autonomous experimental reasoning loop.
Biology Resolution Capabilities
- Pathway Biology Resolution: Experimentally resolves the truly active pathways under perturbation, making the full biological consequences visible early enough to influence decisions.
- Target Biology Resolution: Separates causal driver targets from associated passenger targets, ranking targets by cellular activity.
- Molecule Biology Resolution: Identifies how active molecules work and where they act off-target, supporting compound optimization based on pathway activity.
- Disease Biology Resolution Models: Experimentally generated foundational models that serve as the core layer of the BioGPU biology resolution framework.
Visual Biology Trained Models
- Large Visual Computing Model: Generated from 25 disease models trained in real pharma projects, encompassing over 2 billion process visualizations in total.
- Visual mRNA Biology Model: A domain-specific model that visualizes mRNA biology regulatory mechanisms, reflecting the team's deep expertise in mRNA biology.
Discovery Workflow
- Hundreds of disease hypotheses are generated computationally as a starting point.
- The Biology GPU then enables scientists and AI models to see and compute on biological processes and pathways inside cells.
- Causal targets are identified and ranked by cellular activity, with drivers clearly separated from passengers.
- The BioGPU Agent demonstrated the ability to turn ChatGPT into an autonomous drug discovery engine, resolving causal biology in ALS through iterative experimental loops.
Applications Across the R&D Value Chain
- Discovering pathway signatures and identifying novel targets.
- Optimizing compounds based on observed pathway activity.
- Supporting preclinical decisions by seeing biology inside cells.
- Integrating data modalities including genomics, transcriptomics, proteomics, pathology, and cellular morphology.
- Powering use cases across the discovery process in over 20 programs, with strategic collaborations with AbbVie, Takeda, and Lilly.
Team and Expertise
- AnimaBiotech was co-founded by Yochi Slonim (M.Sc., CEO) and Iris Alroy (PhD., CSO), with deep roots in mRNA biology and imaging.
- The leadership team spans expertise in mRNA biology, software engineering, artificial intelligence, computational biology, chemistry, imaging, and operations.
- Key scientific and technical leaders include Vice Presidents and Principal Scientists covering software development, data analysis, chemistry, therapeutic areas, discovery, assay development, MOA and target identification, and drug discovery.
- The board includes members with backgrounds in software, operations, and life sciences, including Pascal Brandys, Barak Ben-Eliezer, Fred Voccola, and Gilead Sher.
AnimaBiotech's Biology GPU represents a foundational shift in how AI interacts with disease biology — moving from static data analysis to dynamic, experimental reasoning inside living cells, and providing the missing experimental modality that AI needs to resolve causal biology across pathways and processes in parallel.