An early and influential review cataloging 45 papers on deep generative modeling for molecules, comparing RNN, VAE, GAN, and reinforcement learning architectures across SMILES and graph-based representations.
Compares RNN-based and Transformer-based chemical language models across three molecular generation tasks of increasing complexity, finding that RNNs excel at local features while Transformers handle large molecules better.
This paper introduces structured state space sequence (S4) models to chemical language modeling, showing they combine the strengths of LSTMs (efficient recurrent generation) and GPTs (holistic sequence learning) for de novo molecular design.
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.
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.
SMILES2Vec is a deep RNN that learns chemical features directly from SMILES strings using a Bayesian-optimized CNN-GRU architecture. It matches graph convolution baselines on toxicity and activity prediction, and its explanation mask identifies chemically meaningful functional groups with 88% accuracy.
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.
Introduces SMILES Pair Encoding (SPE), a data-driven tokenization algorithm that learns high-frequency SMILES substrings from ChEMBL to produce shorter, chemically interpretable token sequences for deep learning.