nach0 unifies natural language and SMILES-based chemical tasks in a single encoder-decoder model, achieving competitive results across molecular property prediction, reaction prediction, molecular generation, and biomedical NLP benchmarks.
An extensive benchmark showing that training RNN generative models with randomized (non-canonical) SMILES strings yields more uniform, complete, and closed molecular output domains than canonical SMILES.
A critical reassessment of the PMO benchmark for de novo molecule generation, showing that adding molecular weight, LogP, and diversity filters substantially re-ranks generative models, with Augmented Hill-Climb emerging as the top method.
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.
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.
This survey systematically reviews scientific LLMs (Sci-LLMs) across five modalities: textual, molecular, protein, genomic, and multimodal, analyzing architectures, datasets, evaluation methods, and open challenges for AI-driven scientific discovery.
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.
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.