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-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.
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.
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.
ChemBench introduces an automated benchmark of 2,700+ chemistry questions to evaluate LLMs against human expert chemists, revealing that frontier models outperform domain experts on average while struggling with basic tasks and confidence calibration.
ChemEval is a four-level, 62-task benchmark for evaluating LLMs across chemical knowledge, literature understanding, molecular reasoning, and scientific deduction, revealing that general LLMs excel at comprehension while chemistry-specific models perform better on domain tasks.
Identifies failure modes in molecular generative models, showing that trivial edits fool distribution-learning benchmarks and that ML-based scoring functions introduce exploitable model-specific and data-specific biases during goal-directed optimization.
MoleculeNet introduces a large-scale benchmark suite for molecular machine learning, curating over 700,000 compounds across 17 datasets with standardized metrics, data splits, and featurization methods integrated into the DeepChem open-source library.
MolGenBench introduces a comprehensive benchmark for evaluating molecular generative models in realistic drug discovery settings, spanning de novo design and hit-to-lead optimization across 120 protein targets with 220,005 experimentally validated actives.