LatticeZero
Physics-First Scoring at Screening Speed

Physics-First Molecular Docking & Screening.

Hundreds of ligands per second without sacrificing accuracy. IsoScreen universal scorer, zero per-target parameters. No fitted parameters. No training data. No black box. Every weight traces to a physics term.

0.902
Mean AUC · 178 targets
100s
Lig/sec · GPU docking
R = 0.77
Abs. affinity · CASF-2016
178
Targets validated

LatticeZero computes physics-first binding interactions at hundreds of ligands per second on a single GPU, with zero trained parameters, and beats every classical scoring function on both discrimination and absolute affinity prediction.

Everything you need to dock, score, and predict.

A complete molecular docking platform built on physics first principles. Works on any target without calibration. No external dependencies.

Target Prep Automated

Automated receptor preparation. PDB cleanup, protonation, binding site detection, grid generation. Ready in minutes.

Optimizer Supervised

Bring your own labeled data to build custom scoring profiles for your target. Maximizes per-target AUC with cross-validated physics weights. Optional when you have active/decoy labels.

Discrimination Benchmarks

Adaptive geometric filtering matches each pocket's binding regime from population statistics alone. No per-target calibration.

0.886
DUD-E (87 targets)
Industry standard
0.933
DEKOIS2 (76 targets)
Property-matched decoys
0.842
LIT-PCBA (15 targets)
Held-out, real HTS
0.902
Combined (178 targets)
Overall mean

Predict Binding Free Energy from First Principles

LatticeZero predicts binding free energy from physics-first energy terms computed on the GPU. CASF-2016: 81 complexes, the gold standard benchmark.

R = 0.77
LatticeZero (production)
MAE = 1.56 kcal/mol
R = 0.84
LatticeZero (ceiling)
MAE = 1.36 kcal/mol
Method CASF-2016 R Speed
LatticeZero 0.77 100s lig/sec
RF-Score v3 0.75 ~1,000 lig/sec
FEP+ (AB-FEP) 0.69 hours per molecule
AutoDock Vina 0.60 ~50 lig/sec
Glide SP 0.57 ~100 lig/sec

Methodology

From first-principles physics to GPU-accelerated scoring in four steps.

Step 01
Physics Foundation
First Principles

Scoring grounded in calibrated sterics and electrostatics. No parameters trained on binding data. Every weight traces to a physical interaction term.

Step 02
LP+ Physics Engine
18 Energy Terms

Coulomb, dispersion, repulsion, H-bonds, pi-stacking, burial penalties, strain, and more. All computed from our geometric physics engine.

Step 03
WebGPU Compute
100s lig/sec

GPU-accelerated scoring via WebGPU compute shaders. Score hundreds of molecules per second on RTX-series GPUs, directly in the browser.

Step 04
IsoScreen Universal Scoring
Zero Calibration

Zero per-target parameters. Docking strain and physics terms determine the scoring pathway automatically. Works on any target without calibration. 0.902 mean AUC across 178 targets.

How We Compare

Tool Discrimination (DUD-E AUC) Affinity (CASF R) Speed (lig/sec) Parameters trained
Glide SP ~0.78 0.57 ~100 hundreds
AutoDock Vina ~0.70 0.60 ~50 dozens
FEP+ (absolute) n/a 0.69 ~0.001 simulation
RF-Score v3 ~0.80 0.75 ~1,000 100s
GNINA (DL) ~0.85 n/a ~100 millions
LatticeZero 0.886 0.77 100s zero

What Makes This Different

Physics-first scoring on GPU

Coulomb, dispersion, repulsion, strain, and hydrogen bond geometry computed per atom pair at hundreds of ligands per second on a single GPU. Not parameterized force fields. Not learned potentials. Computed from atomic coordinates and partial charges.

Adaptive pocket geometry filtering

Population statistics of the docked ensemble reveal the pocket's binding regime. Scoring weights adjust automatically. Rigid pockets, flexible grooves, metal sites, allosteric cavities: each gets the physics it needs.

Absolute affinity from first principles

The same physics engine that discriminates actives from decoys also predicts binding free energy. R = 0.77 on CASF-2016. Screening and affinity in one platform.

Works on any target without calibration

No training set required. No active/decoy labels needed. Upload a receptor PDB and a ligand library. Score in minutes.

Problems We Solve

Empirical overfitting

Physics-derived scoring grounded in calibrated sterics and electrostatics. No parameters trained on binding data. No decoy bias.

Steric clashes

Docking-calibrated softening derived from potential matching, not AUC optimization. Reduces clash artifacts while preserving pose discrimination.

Requires per-target training

Most ML scoring functions need active/decoy labels for every new target. IsoScreen scores any target with zero calibration - docking strain and physics terms determine the scoring pathway automatically.

Black-box predictions

Every score traces to interpretable physics terms. Coulomb, dispersion, repulsion, H-bonds, burial - all inspectable. No neural network hidden layers.

Get Early Access

Full platform access during open beta. No credit card required.

Enterprise
By Inquiry
  • Custom scoring profiles with labeled data
  • Priority support
  • Dedicated infrastructure
  • SLA & integration
Contact Us

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