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| license: cc-by-4.0 |
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| # BRAIDBERTa |
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| BRAIDBERTa is the first pretrained language model for BRAID, a molecular notation in which every valid string decodes to a valid molecule. |
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| Compared against a matched SMILES baseline on BBBP, BRAID achieves statistically indistinguishable ROC-AUC while guaranteeing valid decoding. |
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| Check it out: https://huggingface.co/spaces/aakothari/Conformer |
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| ## The BRAID notation |
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| BRAID (**B**ranch-counted, **R**elative-ring, **A**ttachment-native, **I**nvalid-free, |
| **D**ense) is built from four mechanisms, all of which are recombined prior art: |
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| | 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 | |
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| Only **atom tokens** advance the global index; `>k`, `^d`, bond symbols and `.` do not. |
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| ``` |
| 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) |
| ``` |
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| ### Novelty: none claimed |
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| 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). |
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| Please treat BRAID as a recombination. |
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| ### The property BRAID does have |
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| 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. |
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| Under an order-*k* Markov generator on a 79-molecule corpus: |
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| | 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 | |
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| 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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| --- |
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| ## Model details |
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| - **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 |
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| 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. |
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| | 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 | |
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| (± is the sample standard deviation across seeds.) |
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| ### Interpretation |
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| **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). |
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| The correct reading is |
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| > Under matched pretraining and finetuning, BRAID's guaranteed-validity property comes |
| > **at no measurable cost to downstream predictive performance** on BBBP. |
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| 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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| --- |
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| ## Usage |
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| The model consumes **BRAID strings, not SMILES**, so you need the codec: |
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| ```bash |
| pip install rdkit transformers |
| pip install git+https://github.com/AayushK-othari/braid.git |
| ``` |
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| ```python |
| 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 |
| ``` |
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| > **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. |
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| **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. |
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| <!-- TODO: state which mode the ZINC 100k pretraining corpus used --> |
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| The reference implementation — encoder, decoder, valence state machine, stereo handling, |
| tokenizer/`Vocab` builder, and the test suites — lives at |
| **[github.com/aakothari/braids](https://github.com/aakothari/braids)**. |
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| ### Verified codec behaviour |
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| - **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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| --- |
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| ## Limitations |
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| 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. |
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| 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. |
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| 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. |
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| 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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| --- |
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| ## Citation |
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| BRAID is a recombination of published mechanisms. Please cite the underlying work: |
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| - 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. |
