Winter et al. propose CDDD, a translation-based encoder-decoder that learns continuous molecular descriptors by translating between equivalent chemical representations like SMILES and InChI, pretrained on 72 million compounds.
DeepSMILES replaces paired parentheses and ring closure symbols in SMILES with a postfix notation and single ring-size digits, making it easier for generative models to produce syntactically valid 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.
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 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.
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.
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.
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 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.
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.
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.
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.