Group SELFIES: Fragment-Based Molecular Strings
Group SELFIES extends SELFIES with group tokens representing functional groups and substructures, maintaining chemical robustness while improving distribution learning and molecular generation quality.
Molecular Representations
MolBERT: Auxiliary Tasks for Molecular BERT Models
MolBERT pre-trains a BERT model on SMILES strings using masked language modeling, SMILES equivalence, and physicochemical property prediction as auxiliary tasks, achieving state-of-the-art results on virtual screening and QSAR benchmarks.
nach0: A Multimodal Chemical and NLP Foundation Model
nach0 unifies natural language and SMILES-based chemical tasks in a single encoder-decoder model, achieving competitive results across molecular property prediction, reaction prediction, molecular generation, and biomedical NLP benchmarks.
Neural Machine Translation of Chemical Nomenclature
This paper applies character-level CNN and LSTM encoder-decoder networks to translate chemical names between English and Chinese, comparing them against an existing rule-based tool.
Randomized SMILES Improve Molecular Generative Models
An extensive benchmark showing that training RNN generative models with randomized (non-canonical) SMILES strings yields more uniform, complete, and closed molecular output domains than canonical SMILES.
Seq2seq Fingerprint: Unsupervised Molecular Embedding
A GRU-based sequence-to-sequence model that learns fixed-length molecular fingerprints by translating SMILES strings to themselves, enabling unsupervised representation learning for drug discovery tasks.
SMI-TED: Encoder-Decoder Foundation Models for Chemistry
SMI-TED introduces encoder-decoder chemical foundation models (289M parameters) pre-trained on 91 million PubChem molecules, achieving strong results across property prediction, reaction yield, and molecule generation benchmarks.
SMI+AIS: Hybridizing SMILES with Environment Tokens
Proposes SMI+AIS, a hybrid molecular representation combining standard SMILES tokens with chemical-environment-aware Atom-In-SMILES tokens, demonstrating improved molecular generation for drug design targets.
SMILES Transformer: Low-Data Molecular Fingerprints
A Transformer-based encoder-decoder pre-trained on 861K SMILES from ChEMBL24 produces 1024-dimensional molecular fingerprints that outperform ECFP and graph convolutions on 5 of 10 MoleculeNet tasks in low-data settings.
SMILES vs SELFIES Tokenization for Chemical LMs
Introduces Atom Pair Encoding (APE), a chemistry-aware tokenizer for SMILES and SELFIES, and shows it consistently outperforms Byte Pair Encoding in RoBERTa-based molecular property classification on BBBP, HIV, and Tox21 benchmarks.
SMILES-BERT: BERT-Style Pre-Training for Molecules
SMILES-BERT pre-trains a Transformer encoder on 18M+ SMILES from ZINC using a masked recovery task, then fine-tunes for molecular property prediction, outperforming prior methods on three datasets.
Smirk: Complete Tokenization for Molecular Models
Introduces Smirk and Smirk-GPE tokenizers that fully cover the OpenSMILES specification, proposes n-gram language models as low-cost proxies for evaluating tokenizer quality, and benchmarks 34 tokenizers across intrinsic and extrinsic metrics.