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.
Method Papers: New Algorithms, Architectures, and Mechanisms
Papers whose primary contribution is a new algorithm, architecture, or approximation for AI and physical sciences research.
SMILES2Vec: Interpretable Chemical Property Prediction
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.
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.
SPE: Data-Driven SMILES Substructure Tokenization
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.
SPMM: A Bidirectional Molecular Foundation Model
SPMM pre-trains a dual-stream transformer on SMILES and 53 molecular property vectors using contrastive learning and cross-attention, enabling bidirectional structure-property generation, property prediction, and reaction prediction through a single model.
t-SMILES: Tree-Based Fragment Molecular Encoding
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.
Transformer Name-to-SMILES with Atom Count Losses
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: SMILES Embeddings for QSAR Modeling
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.
VAE for Automatic Chemical Design (2018 Seminal)
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: Pre-training on 1.1B Molecules for SMILES
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.
Back Translation for Semi-Supervised Molecule Generation
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.
Frechet ChemNet Distance for Molecular Generation
Introduces the Frechet ChemNet Distance (FCD), a single metric that captures chemical validity, biological relevance, and diversity of generated molecules by comparing distributions of learned ChemNet representations.