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BioTarget Predictions BENCHMARK

Binding affinity, target identification, and mechanism of action — validated against CASF-2016 and ChEMBL benchmarks. Validated against published benchmarks.

91%
MoA Accuracy
ChEMBL validation
0.772
Pearson r
CASF-2016 (270 complexes)
10,065
Targets
5 kingdoms
1.28
MAE (pKi)
CASF-2016 binding affinity

Why these numbers matter more than they look

FluxTarget solves a fundamentally harder problem than every method it is compared against.

What every other method receives
✓ A resolved bound-complex structure with far more target-specific information than a raw molecular query
✓ A known interaction geometry rather than an inferred one
✓ For many methods, large structure-supervised training sets as well
They score a known answer. The hard part — finding the pose — is already done by the crystallographer.
What FluxTarget receives
1. A SMILES string — a flat, 2D text representation of the molecule (e.g. CC(=O)Oc1ccccc1C(=O)O)
2. A target name (e.g. “Thrombin”)
No resolved bound structure. No supplied pose. FluxTarget must infer the missing context computationally.

How FluxTarget closes the information gap

⚖
Builds 3D ligand
Constructs a usable structural hypothesis from the molecular input
⚙
Loads 3D protein
Contextualizes the target environment and plausible interaction region
⚡
Docks on GPU
Evaluates candidate interaction hypotheses with deterministic physics
★
Predicts affinity
Returns an affinity estimate with interpretable contributing factors
FluxTarget reconstructs missing structural context before scoring affinity. The methods it is compared against typically begin from a much more informative experimental starting point.

Validated Capabilities

Binding Affinity Prediction — CASF-2016

  • Pearson r = 0.772 on 270 CASF-2016 complexes (Su et al. 2019)
  • MAE = 1.28 pKi
  • Every prediction deterministic and fully auditable
  • Deterministic, fully auditable affinity inference
  • 5,000+ predictions/sec (~300,000x faster than conventional docking)

Literature Context

These are not apples-to-apples comparisons. Every other method starts from a resolved structure with substantially more binding information already exposed. FluxTarget does not; it must infer the missing context before it can score affinity. Many deep-learning baselines also rely on large structure-supervised training sets.
Method Pearson r MAE (pKi) Required Input Training Data
DEEP LEARNING + 3D CO-CRYSTAL STRUCTURE + LARGE TRAINING SETS
graphDelta (GNN) 0.87 — Resolved bound complex Large structure-supervised set
Kdeep (CNN) 0.85 — Resolved bound complex Large structure-supervised set
GNINA (CNN) 0.82 ~1.0 Resolved bound complex Large structure-supervised set
CLASSICAL SCORING + 3D CO-CRYSTAL STRUCTURE
RF-Score v3 0.72 ~1.4 Resolved bound complex Moderate structure-based set
Glide SP 0.65 — Resolved bound complex Empirical (fitted to structures)
X-Score 0.61 — Resolved bound complex Empirical (fitted to structures)
AutoDock Vina 0.60–0.70 ~1.5–1.7 Resolved bound complex Empirical (fitted to structures)
NO 3D STRUCTURE · NO TRAINING DATA
FluxMateria FluxTarget 0.772 1.28 SMILES + target name None (physics-only)

All methods are benchmarked on CASF-2016 scoring power. Many comparison methods rely on resolved complex structures and structure-supervised training sets. FluxTarget remains competitive despite starting from materially less input information.

Download per-complex results
270 CASF-2016 complexes · PDB ID, target, experimental pKd, predicted pKi, error
Download CSV

Target Identification

  • AUC = 0.980 on target identification benchmark
  • 10,065 targets across 5 biological kingdoms
  • Predicts likely protein targets for a given small molecule
  • Physics-only scoring — no ML

Mechanism of Action (MoA) Prediction

  • 91% accuracy on ChEMBL validation set
  • Predicts agonist/antagonist/inhibitor classification
  • Physics-only approach — no ML, unique to FluxMateria
  • Integrated with FluxTarget module

Planned

Selectivity Profiling

Off-target binding predictions and selectivity scoring. Status: Planned.

Try FluxTarget on Your Data

Predict binding affinities across 10,000+ targets with full interpretability. CASF-2016 validated.

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