Anima Biotech is a biotechnology company developing the “Biology Runtime Layer for AI,” an experimental and computational infrastructure that enables the visual computation of biological processes in living cells. The company’s technology allows artificial intelligence systems to observe, interpret, and reason experimentally within cellular environments—bringing visual explainability and biological context to AI-driven drug discovery and target validation.

Technology and Platforms

Biology GPU

At the core of Anima Biotech’s innovation is the Biology GPU, a visual computation system designed to observe and quantify cellular biology in real time. It consists of three interconnected layers:

1. Visualization Layer – Captures dynamic cellular pathways and processes through real-time imaging.
2. Biology Layer – Enables AI models to conduct experimental reasoning on biological systems.
3. Computation Layer – Hosts the Visual Biology Model, a deep learning system trained on more than 2 billion cellular pathway images, capable of identifying active biological mechanisms in healthy and diseased cells.

Lightning.AI is Anima Biotech’s integrated drug discovery and target identification platform. It applies AI to uncover disease mechanisms, discover new therapeutic targets, and design small-molecule modulators of mRNA biology. The platform functions across the full discovery spectrum—from hypothesis generation to candidate optimization—enabling the visualization of biological mechanisms as they respond to chemical modulation.

Anima Biotech has developed a specialized visual neural network for mRNA biology, capable of tracking translation dynamics within live cells. This technology allows identification of small molecules that modulate translation of specific proteins—facilitating drug discovery for previously “undruggable” protein targets.

Applications

The Biology GPU and associated AI tools are applied across multiple stages of drug research:

• Identification of new disease targets
• Validation of biological mechanisms
• Comparative disease signature analysis
• Testing compound selectivity and mechanism of action
• Optimization of chemical series based on pathway activity
• Toxicity profiling and preclinical safety evaluation

This approach delivers visual, quantitative, and interpretable biological evidence to guide drug discovery.

Strategic Partnerships

Anima Biotech has established multi-year strategic collaborations with leading pharmaceutical companies:

• AbbVie: Discovery and development of mRNA biology modulators for oncology and immunology targets.
• Upfront: $42 million
• Total potential: up to $540 million (R&D milestones, commercial payments, tiered royalties)
• Option to expand collaboration to three additional targets.
• Takeda Pharmaceutical Company: Discovery of mRNA translation modulators for neurological diseases, including Huntington’s disease.
• Upfront and early milestones: $120 million
• Total potential: up to $1.1 billion, with option to expand to three additional targets (total $1.2 billion).
• Two milestone achievements publicly disclosed, confirming platform validation.
• Eli Lilly: Discovery and development of translation inhibitors for specific protein targets.
• Upfront: $30 million
• Research funding: $14 million
• Total potential: up to $1.05 billion, plus tiered royalties on product sales.

Leadership and Organization

Founders:

• Yochi Slonim – Chief Executive Officer (CEO)
• Iris Alroy, Ph.D. – Chief Scientific Officer (CSO)

Executive Team:

• Avi Eliassaf – Chief Operating Officer
• Yossi Oulu – Vice President of Software Development
• Generoso Ianniciello – Chief Business Officer
• Yoni Sheinberger – Vice President of Discovery

Scientific Advisory Board:

• Ada Yonath (Nobel Laureate in Chemistry)
• Michel Goldberg
• Barry Cooperman

The company’s multidisciplinary team integrates expertise in mRNA biology, software engineering, AI, computational biology, chemistry, and high-resolution cellular imaging.

Scientific Framework

Anima Biotech’s closed-loop discovery system allows AI models to experimentally reason about biology by observing live cellular pathways. This dynamic feedback mechanism supports the interpretation of disease mechanisms, detection of dysregulated activity, and time-resolved analysis of compound effects—producing visual, quantifiable, and explainable biological evidence for target selection and candidate optimization.