BRAIDBERTa

BRAIDBERTa is the first pretrained language model for BRAID, a molecular notation in which every valid string decodes to a valid molecule.

Compared against a matched SMILES baseline on BBBP, BRAID achieves statistically indistinguishable ROC-AUC while guaranteeing valid decoding.

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The BRAID notation

BRAID (Branch-counted, Relative-ring, Attachment-native, Invalid-free, Dense) is built from four mechanisms, all of which are recombined prior art:

Mechanism	Syntax	Borrowed from
Organic-subset lexing, [...] only when needed	C, Cl, [N+]	SMILES
Length-counted branches (no parentheses)	>k then k atoms	SELFIES branch counts
Relative ring closure (no paired digits)	^d = bond d atoms back	DeepSMILES relative rings
Fragment separator, global index continues across it	.	SAFE

Only atom tokens advance the global index; >k, ^d, bond symbols and . do not.

aspirin   SMILES  CC(=O)Oc1ccccc1C(=O)O
          BRAID   CC>1=OOcccccc^5C>1=OO        (aromatic mode)
          BRAID   CC>1=OOC=CC=CC=C^5C>1=OO     (kekulé mode, default)

Novelty: none claimed

BRAID is not novel at the mechanism level. Relative ring closure is DeepSMILES (O'Boyle & Dalke, 2018). Length-counted branches are the SELFIES branch-count idea (Krenn et al., 2020) with the brackets stripped. Guaranteed-valid decoding via a valence state machine is the entire point of SELFIES. The fragment separator is SAFE (Noutahi et al., 2023).

Please treat BRAID as a recombination.

The property BRAID does have

Every string decodes. On 10,000 random and mutated strings, the decoder produced a sanitizable molecule with 0 crashes. This is achieved by making the decoder total: unparseable tokens and over-valent bonds become silent no-ops. Validity is guaranteed.

Under an order-k Markov generator on a 79-molecule corpus:

representation	valid%	unique%	novel%	vocab	tok/mol
SMILES	40.0	38.4	34.0	24	13.6
DeepSMILES	35.4	46.9	42.4	27	13.3
SELFIES	100.0	59.6	57.9	29	12.9
BRAID	100.0	60.9	59.2	30	13.2

A Markov model is far weaker than a real CLM, so treat validity% as the meaningful (model-independent) signal and uniqueness/novelty as illustrative.

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Model details

• Architecture: RoBERTa (masked language modelling)
• Pretraining corpus: ZINC 100k, encoded in BRAID
• Tokenizer: BPE trained on the BRAID corpus
• Objective: MLM

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Downstream evaluation: BBBP

Finetuned for binary classification (blood–brain barrier penetration) with a fresh 2-logit head and cross-entropy loss. Hyperparameters (learning rate, epochs) were selected by Optuna on the validation split, then the best configuration was retrained across 5 seeds. Metric is test ROC-AUC.

Model	Notation	BBBP ROC-AUC	Seed 0	Seed 1	Seed 2	Seed 3	Seed 4
BRAIDBERTa-v9	BRAID	0.725 ± 0.017	0.727	0.710	0.751	0.710	0.727
SMILES control	SMILES	0.720 ± 0.010	0.719	0.738	0.715	0.714	0.712

(± is the sample standard deviation across seeds.)

Interpretation

The two are statistically indistinguishable. Welch's t-test on the five seeds per model gives t ≈ 0.61, p ≈ 0.56. The 0.005 gap in favour of BRAID sits well inside seed noise, and the per-seed ranges overlap almost completely (BRAID 0.710–0.751, control 0.712–0.738).

The correct reading is

Under matched pretraining and finetuning, BRAID's guaranteed-validity property comes at no measurable cost to downstream predictive performance on BBBP.

Note: The seed-to-seed spread (±0.017) exceeds the between-model difference. Any comparison at this scale reporting a single seed is measuring noise.

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Usage

The model consumes BRAID strings, not SMILES, so you need the codec:

pip install rdkit transformers
pip install git+https://github.com/AayushK-othari/braid.git

from braids import smiles_to_braid
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("aakothari/BRAIDBERTa-v9")
model = AutoModelForSequenceClassification.from_pretrained(
    "aakothari/BRAIDBERTa-v9", num_labels=2
)

braid = smiles_to_braid("CC(=O)Oc1ccccc1C(=O)O")   # -> CC>1=OOC=CC=CC=C^5C>1=OO
inputs = tok(braid, return_tensors="pt", truncation=True, max_length=128)
logits = model(**inputs).logits

Do not feed raw SMILES to this tokenizer. It will not error — BPE always backs off to characters — it will simply produce a meaningless tokenization and a plausible-looking, wrong prediction. Always encode with smiles_to_braid first.

Encode in the same mode the model was pretrained in. The codec has Kekulé (default) and aromatic modes, and they produce different strings for the same molecule (C=CC=CC=C^5 vs cccccc^5). Mixing modes between pretraining and inference silently degrades performance.

The reference implementation — encoder, decoder, valence state machine, stereo handling, tokenizer/Vocab builder, and the test suites — lives at github.com/aakothari/braids.

Verified codec behaviour

• Round-trip: 42/42 molecules recover their constitution in both Kekulé and aromatic modes — including fused/bridged systems (naphthalene, indole, adamantane, caffeine, purine, quinoline, nicotine) and ions (nitromethane, acetate, ammonium, Na⁺·Cl⁻).
• Stereo: 26/26 chiral / E-Z molecules round-trip in both modes — L-alanine, (S)-ibuprofen, meso vs. L-tartaric acid, menthol, L-DOPA, adrenaline, carvone, fumaric/maleic acid, conjugated dienes, mixed chiral + E/Z.
• Validity: 10,000/10,000 random and mutated strings decode to a sanitizable molecule with 0 crashes.

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Limitations

1. Stereochemistry is partial. Tetrahedral chirality and E/Z double bonds round-trip on 26/26 test cases. Allene/axial/planar chirality, atropisomers, and non-tetrahedral stereo centres fall back to unspecified.

2. The valence state machine is approximate. A hand-rolled charge→valence rule, correct for common ions but liable to mis-clamp hypervalent, organometallic, or unusual-charge atoms. It is not a substitute for RDKit's model.

3. Insertion/deletion is non-local. Inserting or deleting an atom shifts every ^d that spans the edit point. Inherent to relative back-references; applies equally to DeepSMILES.

4. Not a canonical hash. One molecule has many valid BRAID strings. The encoder uses RDKit canonical-rank rooting for determinism, but uniqueness is not proven.

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Citation

BRAID is a recombination of published mechanisms. Please cite the underlying work:

• O'Boyle, N. & Dalke, A. DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures. ChemRxiv, 2018.
• Krenn, M. et al. Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation. Machine Learning: Science and Technology, 2020.
• Noutahi, E. et al. Gotta be SAFE: A New Framework for Molecular Design. 2023.
• Weininger, D. SMILES, a chemical language and information system. J. Chem. Inf. Comput. Sci., 1988.