If you missed the live webinar, don’t worry — the full recording is available below.

 

 

In this session, we introduce SILCS v2026, including several new capabilities and workflow enhancements designed to improve interpretability, efficiency, and decision‑making in computational drug discovery.

This update may be particularly relevant for those working on lead optimization, ligand binding analysis, or differentiating compounds beyond affinity alone.

 

Key Takeaways

Introduction of the SILCS‑Kinetics Suite
  • SILCS‑Pathway identifies ligand unbinding pathways using FragMaps and A* search
  • SILCS‑Kinetics estimates ligand unbinding rates (koff) using pathway‑based energetics and machine learning
  • Extends SILCS workflows from binding affinity to kinetic insight

 

Improved Interpretability of SILCS Simulations
  • New probe–residue frame extraction enables analysis of how specific probes interact with target residues
  • Provides deeper insight into interaction patterns, orientation, and local behavior
  • Bridges global FragMaps with residue‑level understanding

 

Updates to Workflow Efficiency and Usability
  • GUI enhancements:
    • Multi‑pose ligand visualization
    • Improved restart mechanics with accessible log information
    • Clearer job and directory organization
  • Reduces friction in HPC usage and workflow management

 

Enhancements Across SILCS Modules
  • CGenFF v5.0 with batch processing support
  • SILCS‑Biologics: automated post‑processing and experimental data integration
  • SILCS‑Covalent: improved ligand input handling and traceability
  • Continued focus on automation and reducing manual scripting

 

SILCS in Practice: Case Study
  • Demonstrates an end‑to‑end workflow from FragMaps to experimental prioritization
  • Shows how SILCS supports decision‑making across:
    • Binding site identification
    • Virtual screening
    • Lead optimization
  • Highlights the role of SILCS in early‑stage compound selection

 

Additional Resources