AI-Driven Biologics Design at PozeSCAF

Next-generation biologics integrate AI-driven design, precision engineering, and immunological insights to create highly effective therapeutics. From optimizing protein expression and stability to refining antibody specificity and half-life, each step enhances efficacy and safety. Innovations in ADCs and immunotherapy—like smart epitope mapping and rational linker design—ensure targeted, durable responses. These advances are exemplified by successful clinical translation, including cytokine therapies.

Structure-to-Function Engineering

AI-driven modeling of protein structure, motion, and hotspots to design optimized biologics with enhanced efficacy, specificity, and stability. Deep learning models used for T-cell and B-cell epitope prediction, enabling reduction of immunogenicity and improved half-life.

Antibody design

1. AI-Guided CDR Engineering

Structure-aware CDR loop modeling, paratope mapping, and affinity maturation using deep generative and reinforcement learning tools.

2. Epitope-Specific Antibody Design

Integration of antigen surface modeling and epitope prediction for de novo antibody generation and optimization.

3. In Silico Humanization & Immunogenicity Profiling

• Machine learning–based frameworks for species-specific sequence tuning and T-cell epitope de-risking.
• Antibody–antigen docking, MD-based interface stabilization, and residue scanning for affinity, specificity, and developability.

ADC Design – Precision Payloads for Targeted Therapy

1. Target Selection & Payload Matching

AI-guided identification of tumor-selective antigens and optimization of payload-linker combinations for maximal therapeutic index.

2. Linker & Conjugation Site Optimization

In silico screening of cleavable/non-cleavable linkers, conjugation chemistries, and site-specific conjugation strategies.

3. Payload Potency & Bystander Modeling

Simulation-driven modeling of payload release dynamics, membrane permeability, and bystander effect potential.

4. AI-Powered Developability Filters

Screening for aggregation risk, solubility, and toxicity profiles pre-conjugation to ensure optimal ADC formulation.

5. Integrated Workflow for ADC Design

AxDrug-enabled end-to-end ADC discovery, from antigen prediction to conjugate engineering and in vitro testing.

Precision in Biologics Design – Tailored, Targeted, Transformative

1. Epitope-Centric Antibody & Protein Engineering

AI models identify and optimize clinically relevant epitopes, enabling design of antibodies and cytokines with high specificity.

2. AI-Guided CDR & Paratope Optimization

Deep learning–driven design of high-affinity binding regions with minimized immunogenicity and enhanced developability.

3. Tunable Effector Functions & Fc Engineering

Rational design of Fc variants for controlled immune activation, half-life extension, or reduced effector function.

4. Multi-Specific & Modular Biologics

In silico design of bi- and tri-specific antibodies, fusion proteins, and scaffolded cytokines tailored to complex disease biology.

5. Predictive In Silico Developability Assessment

Screening for aggregation, solubility, viscosity, and post-translational modification liabilities early in the design process.

Advantage with PozeSCAF

• World’s best predictive pharmacology and toxicology platform (Ax-Drug) driving data-driven drug development decisions
• Deep learning based cutting-edge protein modelling technologies for addressing the challenges of structurally disordered (undruggable) proteins.
• Empowering projects through an interdisciplinary team that blends knowledge for optimal direction and impact
• Driven by innovation, PozeSCAF offers precise, highly effective solutions with rapid turnaround time and outstanding success metrics proven by different case studies