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Add retrained PolyEdit checkpoint and audit artifacts
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---
library_name: pytorch
license: apache-2.0
tags:
- polymers
- molecular-optimization
- transformer
- polyedit
---
# PolyEdit Molecular Optimization Transformer (polymer-retrained)
This checkpoint adapts MolecularAI's released Molecular Optimization Transformer to
two-anchor PSMILES and the eight PolyEdit DFT properties. It is not directly comparable
to the authors' original LogD/Solubility/Clint model without this adaptation.
The original transformer body is retained: 6 encoder/decoder layers, hidden size 256,
8 heads, and feed-forward size 2,048. All 258 shape-compatible upstream tensors were
loaded. Four vocabulary-dependent embedding/generator tensors were initialized for a
new vocabulary in which padding and the `*` polymer anchor have distinct IDs. Training
used 152,036 component-held-out examples; the best validation checkpoint was epoch 7
of 10.
On the balanced 8,176-request test set, the checkpoint obtains 99.217% RDKit+TDC
validity, 99.168% two-anchor polymer validity, 99.083% changed outputs, 8.892% strict
MIPS-retrained full-edit hits, and 10.127% strict DFT full-edit hits at 95.034% DFT
coverage. Only 383 unique raw outputs were generated, so the strong validity comes with
substantial mode collapse and must not be reported without the diversity result.
Upstream code: <https://github.com/MolecularAI/deep-molecular-optimization>
Upstream checkpoint: <https://doi.org/10.5281/zenodo.5571965>
PolyEdit implementation and record-level evaluation: <https://github.com/promotion-kim/POLYEDIT/tree/tsyou/balanced-polymer-baseline-eval>