Biologics modeling for wet lab scientists: Detecting and deprioritizing dead ends before they reach the bench

Protein developability challenges are a primary cause of biotherapeutic drug failure, often leading to costly late-stage setbacks. For bench researchers, identifying and addressing liabilities early is essential for mitigating risk and accelerating promising candidates toward clinical approval. Schrödinger’s computational platform integrates physics-based protein descriptors with sequence and structural analysis to pinpoint liability hotspots and rationally design targeted mutations to address them.

Join us to learn how to detect and deprioritize high-risk candidates, effectively discarding developability dead-ends before they ever reach the bench.

Key highlights:

• Precision liability mapping: Utilize physics-based structural analysis to identify specific residues responsible for degradation, aggregation, and instability
• Rational mutagenesis design: Efficiently design and rank targeted mutations to “engineer out” liabilities while maintaining or enhancing target affinity
• Physics-driven triage: Move beyond simple sequence-based rules to assess intricate molecular 3D surface properties
• Workflow integration: Seamlessly connect early-stage discovery with late-stage developability to eliminate candidates likely to fail in manufacturing

Who should attend:

• Bench scientists seeking to reduce time-to-clinic by identifying developability risks earlier in the pipeline
• Beginner computational chemists interested in applying advanced physics-based modeling and molecular dynamics to protein design
• Discovery project leads tasked with selecting and prioritizing lead candidates for scale-up and clinical development