PyLabs

Molecular Photophysics AI

Seven optical properties from SMILES in seconds.

TD-DFT takes 24–72 hours per molecule, so millions of virtual candidates get narrowed to a few hundred that anyone actually evaluates. Our graph isomorphism network predicts absorption, emission, bandwidths, extinction, quantum yield, and lifetime straight from a SMILES string.

R² 0.946Absorption max, n=1,771
1000×Faster than TD-DFT
<1 secPer molecule, per property
Absorption max486 nm
Quantum yield0.71

The problem

Every molecule's optical behaviour is a question quantum chemistry answers slowly.

Before anyone synthesises a candidate, someone needs to know how it absorbs, emits, and decays. Today that means TD-DFT - accurate, and slow enough that only a sliver of any real virtual library ever gets checked. The rest is guesswork, or it never gets screened at all.

Time to compute one molecule with TD-DFT24–72 hrs
Typical virtual library size for screeningMillions
Of those, the number evaluated before synthesisA few hundred

Try the model

Pick a molecule. Watch the properties land.

This is a scripted preview of the live API using four reference fluorophores - the production model accepts any valid SMILES string.

Reference molecules
CCN(CC)c1ccc2cc3c(cc2c1)oc1cc(=O)ccc1c3-c1ccccc1
Abs max (λabs)
Em max (λem)
Abs FWHM
Em FWHM
log ε
Quantum yield
Lifetime (τ)
Status
Inference time: -
GIN · trained on 20,796 chromophore–solvent points

How it fits your workflow

We don't replace TD-DFT. We decide what's worth running it on.

A two-stage pipeline: cheap, fast pre-screening across the full virtual library, then TD-DFT validation only on the candidates that clear the bar.

01 - MILLIONS

GIN pre-screen

SMILES in, seven properties out, in under a second per molecule. Rank a virtual library that would take decades to touch with TD-DFT.

~0.4s / molecule, CPU
02 - HUNDREDS

TD-DFT validation

Spend your quantum-chemistry budget on the survivors. Complementary to the tools you already run, not a replacement for them.

24–72 hrs / molecule
03 - TOP 5-10

Synthesise

At $5–20K and weeks of lab time per candidate, the cost of the pipeline is decided long before anyone touches a flask.

Weeks / candidate

Test-set performance

Accuracy, property by property.

Held-out R² across all seven predicted properties. We publish all seven, not just the best ones.

Abs max
0.946
n = 1,771
Em max
0.896
n = 1,850
Abs FWHM
0.824
n = 697
Em FWHM
0.775
n = 1,139
log ε
0.811
extinction coefficient
log Φ
0.705
quantum yield
log τ
0.766
excited-state lifetime
Inference
<1 sec
per molecule, per property

Where we point it first

Major industries, one bottleneck.

Different molecules, same problem: optical properties gate every downstream decision, and today they're found out too late.

27Validated use cases
15+Industries identified
4Where we're focused first

The model predicts universal photophysical properties - any industry that needs to know how a molecule absorbs, emits, or decays is a structural fit. The list of validated use cases is growing every quarter.

Bring us your hardest scaffold.

Send a library and a target window. We return the ranked shortlist and the seven predicted properties behind it, so you can check the model against molecules you already measured.

Request accessSee the proof