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The Problem You Are Trying to Solve
“I have an initial set of hits from a virtual screen, but I want a refined set with fewer false positives and false negatives, validated by more robust, physics-based simulations.”
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Early hit lists are often noisy. Both experimental high throughput screening and virtual screening is designed for speed and scale, which means:
  • Some compounds score well for the wrong reasons (false positives)
  • Some true binders are missed due to imperfect sampling or rigid assumptions (false negatives)
  • Binding stability and solvent effects are not fully captured
This workflow helps you upgrade your given confidence in your hits by progressively applying more realistic models of binding. Solution
This workflow refines your hit list using a primary refinement chain: Docking → Protein–Ligand MD → ABFE/RBFE. Each step increases realism and confidence for your compounds of interest:
  • Docking provides a fast structural sanity check and rescoring method that can be applied to larger library sizes
  • Protein-Ligand MD tests whether poses are stable over time in solvent, and with dynamic time conditions as well
  • ABFE/RBFE quantifies binding energetics with much higher fidelity than docking
Revilico also supports integration with other engines (QSAR, Pharmacophore Analysis, Protein Water MD, etc.), but the chain above is the core refinement backbone for gaining more confidence in your hit sets, and validating assumptions and hypotheses at the molecular level. What Data Do I Need to Provide?
Required
  • Hit molecules as SMILES (CSV preferred)
  • A protein structure (experimental or predicted) with a defined binding site
  • A clear binding pocket definition (from known ligand, site annotation, or docking grid)
Optional (but useful)
  • Known reference ligand(s) or co-crystal pose (for benchmarking)
  • Experimental activity data (if any)
  • Notes on liabilities to avoid (reactive groups, solubility risk, etc.)
Workflow
  1. Re-score and Sanity-Check Hits
Start by re-evaluating the hit set with the Docking engine to quickly filter obvious false positives. The typical approach is as follows:
  • If your hit set is large: run Static Docking first for throughput, then downselect based on set binding criteria for your project
  • If your hit set is smaller: go straight to Flexible Docking for better pose realism, or to Ensemble Docking for more advanced workflows and protein systems
What you’re looking for:
  • Clear, plausible binding poses (no severe clashes)
  • Consistent strong scores across top poses (low variation between best and average)
  • A pose that makes chemical sense (key interactions, reasonable geometry)
The Docking engine will output a narrowed hit set with selected docked complexes (protein + ligand poses) ready for MD. Integration note: If the binding pocket is known to be flexible or uncertain, you can optionally generate a receptor ensemble via Protein Water MD and use those snapshots for an “ensemble-style” docking pass before MD.
  1. Validate Binding Pose Stability
Docking is still a snapshot. Now test whether the hit remains stably bound in a more realistic environment using Protein-Ligand MD. In Protein-Ligand MD, your docked complex is solvated, ionized, minimized, equilibrated, then simulated over time, producing a trajectory that shows whether binding is stable or fragile. What you’re looking for
  • RMSD vs time: a quick rise then plateau (no long-term drift)
  • Radius of gyration / SASA: stable protein behavior (no unfolding or destabilization)
  • RMSF near pocket residues: no extreme instability; stable binding-site behavior
  • Visual check: ligand stays in the pocket and does not flip, drift, or dissociate from the pocket
This step will result in a smaller set of hits where binding is stable over time, accompanied by trajectories and stability metrics that help explain “why this hit is real”. Integration note: Protein-Ligand MD can optionally include MMPBSA-style analysis as a faster energetic check, but the main goal here is stability and realism before FEP-level investment.
  1. Quantify Binding Energetics
Once you have MD-validated hits, move to ABFE/RBFE Calculation for a stronger energetic signal. This step helps you answer:
  • ABFE: “Is binding thermodynamically favorable, and how strong is it?”
  • RBFE: “Between similar ligands, which binds better and by how much?”
Our Free Energy Perturbation Suite uses alchemical free energy methods (lambda windows + MD sampling), accounting for:
  • Solvent reorganization
  • Protein–ligand dynamics
  • Entropy and restraint corrections
  • More realistic thermodynamics than docking scores
What you’re looking for
  • More favorable (more negative) binding free energies for stronger candidates
  • Reasonable, stable energetic components (no obvious artifacts)
  • Consistency across methods (TI vs MBAR) when available
  • Clear ranking that separates top candidates from the rest
This step will leave you with a high-confidence ranked subset of hits with binding free energy estimates, and a defensible shortlist that is much less sensitive to docking noise.
  1. Final Refinement and Selection
At this point, you should have a refined hit set that is:
  • structurally plausible (Docking)
  • dynamically stable (Protein–Ligand MD)
  • thermodynamically supported (ABFE/RBFE)
From here, users typically:
  • Select a small number of compounds for synthesis / purchase
  • Prepare follow-on hit-to-lead expansion campaigns
Integration note: You can optionally add QSAR Modeling and/or Pharmacophore Analysis as additional triage layers (especially if you have assay data), but they are not required for the core physics-based refinement chain. Results
  • A refined set of hits with significantly fewer false positives
  • Better protection against false negatives (through better pose realism and dynamics)
  • Ranked candidates supported by higher-fidelity binding energetics
  • Compounds ready for experimental validation or lead optimization workflows
Now what?
  • You can move these compounds more confidently into experimentation, knowing that these hypotheses have been rigorously tested through a multi-modal procedure to assess for different binding dynamics.
  • After being tested initially, you can utilize generative chemistry workflows to optimize your lead sets using similar methods
Why Revilico?
Revilico makes hit refinement a single connected workflow: you can start with virtual screening outputs, validate stability with MD, then quantify binding with FEP-grade calculations, all without rebuilding inputs across tools. This produces a refined shortlist that is both computationally grounded and scientifically interpretable, so teams can make better downstream decisions with less wasted synthesis and assay effort.