t-SMILES represents molecules by fragmenting them into substructures, building full binary trees, and traversing them breadth-first to produce SMILES-type strings that reduce nesting depth and outperform SMILES, DeepSMILES, and SELFIES on generation benchmarks.
A comprehensive review of transformer-based chemical language models operating on SMILES, categorizing encoder-only (BERT variants), decoder-only (GPT variants), and encoder-decoder models with analysis of tokenization strategies, pre-training approaches, and future directions.
This paper applies a Transformer sequence-to-sequence model to predict SMILES strings from chemical compound names (Synonyms). Two enhancements, an atom-count constraint loss and SMILES/InChI multi-task learning, improve F-measure over rule-based and vanilla Transformer baselines.
Transformer-CNN extracts dynamic SMILES embeddings from a Transformer trained on SMILES canonicalization and feeds them to a TextCNN for QSAR modeling, achieving strong results across 18 benchmarks with built-in LRP interpretability.
Sultan et al. review 16 sequence-based transformer models for molecular property prediction, systematically analyzing seven design decisions (database selection, chemical language, tokenization, positional encoding, model size, pre-training objectives, and fine-tuning strategy) and identifying a critical need for standardized evaluation practices.
This foundational paper introduces a variational autoencoder (VAE) that encodes SMILES strings into a continuous latent space, allowing gradient-based optimization of molecular properties. Joint training with a property predictor organizes the latent space by chemical properties, and Bayesian optimization over the latent surface discovers drug-like molecules with improved QED and synthetic accessibility.
X-MOL applies large-scale Transformer pre-training on 1.1 billion molecules with a generative SMILES-to-SMILES strategy, then fine-tunes for five molecular analysis tasks including property prediction, reaction analysis, and de novo generation.
Introduces AMORE, an embedding-based retrieval framework that evaluates whether chemical language models can recognize the same molecule across different SMILES representations. Results show current models are not robust to identity-preserving augmentations.
Adapts back translation from NLP to molecular generation, using unlabeled molecules from ZINC to create synthetic training pairs that improve property optimization and retrosynthesis prediction across Transformer and graph-based architectures.
A benchmark of 84 chemistry coding tasks evaluating code-generating LLMs like Codex, showing 72% accuracy with prompt engineering strategies that improve performance by 30 percentage points.
Benchmarks large language models on six molecular property prediction datasets, finding that LLMs lag behind GNNs but can augment ML models when used collaboratively.
This study trains over 62,000 models to systematically evaluate molecular representations and models for property prediction, finding that traditional ML on fixed descriptors often outperforms deep learning approaches.