From target to optimised portfolio — weeks to hours
Transparent validation data across 14 DUD-E targets, 7 protein families, and 42 real campaigns. Every number is reproducible from the same natural-language input.
A campaign isn't one step.
It's a cycle.
Here's what changes when the entire design-make-test-analyse loop runs autonomously.
~40× time compression
Target research, PDB selection, protein prep, compound library assembly
Docking, scoring, manual hit triage, ADMET profiling
Medicinal chemistry reviews SAR, proposes modifications
Second-round library, re-dock, re-score, re-triage
MM-GBSA or FEP on selected compounds. Waiting for compute.
Final candidate selection, report, handoff to synthesis
Target research → protein prep → library → docking → ADMET → validation → SAR → tiered portfolio
Autonomous evaluation. Generates new compounds if needed. Repeats until convergence.
The hard part, automated
The first screen is easy. The real work starts when you ask: “How do I make these hits better?”
Traditional MPO
Each iteration: minutes, not weeks
Evaluate portfolio
Qualified hits, scaffold diversity, ADMET pass rate
Diagnose bottleneck
Binding? ADMET? Diversity? Auto-detected.
Generate compounds
Scaffold hops, bioisosteric swaps, R-group enumeration
Full pipeline screen
Dock → score → qualify → ADMET → validate
Merge & compare
SAR accumulates. Tracks what improved.
Stop or continue
Converged → stop. Improving → another round.
What gets optimised
The platform balances 8 objectives simultaneously — the same trade-offs a medicinal chemist navigates manually, resolved in every iteration.
Improving binding often breaks ADMET. Fixing ADMET drops binding. This whack-a-mole takes weeks manually. The platform's multi-parameter scoring resolves these trade-offs in every iteration, tracking what improved and what regressed.
DUD-E benchmark results
Complete automated workflow per target. AUC-ROC and Precision@20 on initial screening pass.
Initial screening pass. In real campaigns, iterative optimisation generates additional compounds targeting weaknesses — improving coverage in subsequent rounds.
What a campaign produces
After the final iteration, a complete actionable package.
Tiered compound portfolio
Typical distributionStrong binding, ADMET-clean, physics-validated, diverse scaffolds
Good binding, specific liabilities identified
Moderate binding, valuable for structure-activity understanding
Iteration history
- What was tried and results
- Per-round metrics & trends
- Generation strategy rationale
SAR analysis
- Matched molecular pairs
- Scaffold-activity relationships
- What helps, what hurts
Per-compound profiles
- Binding mode & residue contacts
- ADMET predictions
- Synthetic accessibility
Where we're honest
The pipeline underperforms on 3 of 14 targets. We show these results because transparency is how trust gets built.
Virtual screening is not solved. No tool gets every target right. What matters is automating the 90% that doesn't require expert judgment — and being transparent about the 10% that still does.
Non-canonical DFG-out binding conformation. CNN-based pose scoring was trained on canonical modes and misjudges binding quality here.
Fragment-like actives (avg MW ~295) fall below the molecular weight range where CNN scoring is most reliable.
Deep transmembrane GPCR pocket. Structure-based virtual screening remains challenging for this target class generally.
The platform detects scoring unreliability from score distributions and flags results rather than presenting false-confident rankings.
DUD-E (Database of Useful Decoys — Enhanced), 14 targets, ~500 compounds per target. Full automated pipeline: literature review, PDB retrieval, protein preparation, compound library construction, 3D conformer generation, CNN-based docking, multi-signal scoring, adaptive qualification, scaffold-diverse selection, ADMET screening, physics-based binding validation. AUC-ROC and Precision@20 on initial screening pass. Campaign times across 42 real campaigns. All benchmarks reproducible from same natural-language input. March 2026.
See these results on your target
AiDDA runs the full virtual screening pipeline — from target to ranked portfolio — in hours, not months. Run a campaign on your protein target and compare against your current workflow.
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