Biology42: Generative Biologics

Biology42: Generative Biologics is an advanced, AI-powered platform developed by Insilico Medicine for designing and optimizing a broad range of biologics, including peptides, nanobodies, and antibodies tailored to specific targets. The platform is built for drug discovery teams seeking to accelerate the engineering of novel biologics, particularly against challenging targets where traditional approaches fall short.

Powered by more than 10 generative and predictive models supported by physics-based tools, Biology42: Generative Biologics enables the creation of novel, diverse, and high-quality binder candidates in less than 24 hours. The platform is specifically designed to target hard-to-drug proteins and specific epitopes, delivering speed and scale to biologics discovery where it matters most.

Core Capabilities

• De Novo Generation: Design binders for hard targets and desired epitopes from scratch, without relying on existing templates.
• Template-Based Generation: Generate novel, improved variants of an existing binder in three dimensions, building on known structural information.
• Optimization Workflow: Simultaneously optimize multiple properties of biologic candidates, balancing both affinity and developability using a combination of built-in and user-trained models for tailored results.
• Model Training: Leverage proprietary experimental data to train property- and project-specific predictive models, achieving higher precision compared to traditional large language models.
• Prediction Workflow: Assess affinity and developability early in the discovery process to identify and select the best candidates for further development.
• Built-In Predictive Models: Use pre-trained models to predict and optimize developability properties without the need for additional data or configuration.

Key Platform Features

• Generates binder candidates in less than 24 hours, significantly accelerating the early discovery timeline.
• Targets hard-to-drug proteins and specific epitopes through advanced generative AI approaches.
• Supports simultaneous multi-property optimization, enabling researchers to balance competing biologic attributes in a single workflow.
• Proprietary models are designed for specific biologic types, delivering higher accuracy than general-purpose language models.
• AI-guided experimental design reduces both time and cost associated with iterative laboratory cycles.

Workflow Overview

1. Define the target protein and desired epitope for binder design.
2. Use de novo or template-based generation to produce a diverse set of novel biologic candidates.
3. Apply built-in or custom-trained predictive models to assess affinity and developability properties of generated candidates.
4. Run the optimization workflow to simultaneously refine multiple properties and identify the most promising leads.
5. Train project-specific models using proprietary experimental data to continuously improve prediction accuracy and guide future experiments.

Biology42: Generative Biologics is available as a standalone technology access solution or as part of a broader Pharma.ai collaboration, offering a data-driven AI partnership model for organizations seeking deeper integration with Insilico Medicine's drug discovery ecosystem.