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.
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.
Jiang et al. survey 12 families of transformer architectures in molecular science, covering GPT, BERT, BART, graph transformers, Transformer-XL, T5, ViT, DETR, Conformer, CLIP, sparse transformers, and mobile/efficient variants, with detailed algorithmic descriptions and molecular applications.
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.
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.
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.