Zifo Technologies, Inc.

Scope Design and optimize antibodies using AI-driven protein language models (PLMs). Accelerating the antibody design by efficiently navigating the search space through generation of novel antibodies using generative models. Implement a complete end-to-end workflow: Structure prediction Finetuning Generative models for protein design  Antibody sequence generation (contextual and non-contextual) Scale Data and computational requirements: Requires large-scale model training and massive search space exploration Support for Multi-modal inputs (sequence, structure, binding data) High-performance infrastructure (Snowflake, scalable compute) Model types: ProtGPT2: Sequence design and property prediction ESM2 (esm2_t30_150M_UR50D): Structure prediction and protein representation RoBERTa: Natural language-based metadata or annotation tasks Fine-tuning Modes: In-context learning (task-aware) Context-free learning (generalized generation) Value Scientific and commercial impact: Accelerates drug discovery by reducing time and cost in antibody development Improves hit rate for high-affinity therapeutic antibodies Enables rational antibody design at scale with high precision Adaptable platform for multiple life science applications Application Workflow The Antibody Engineering Application by Zifo begins with a simple start interface where users can either upload their own CSV dataset or choose from a default dataset.

After uploading, users select the relevant light and heavy chain columns from the data. Next, they choose a pre-trained model—ESM2, ProtGPT, or RoBERTa—to begin their analysis. Users can then configure training parameters such as batch size, learning rate, and the number of steps.

If needed, they can fine-tune the selected model to improve performance and securely save the trained version. The application then allows users to generate new antibody sequences by inputting the desired number of sequences, and even generate 3D structures for further analysis. All results can be viewed, downloaded, and analyzed with built-in tools for CDR (Complementarity Determining Region) analysis and visualization.

In case of issues, logs are available for troubleshooting, and users can reach out to support for assistance.