ArticleScientific reports2024
Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning.CPT: pharmacometrics & systems pharmacology · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Efficient and valid large molecule generation via self-supervised generative models.npj drug discovery · 2026Article
- Multimodal feature fusion for molecular property classification.Journal of cheminformatics · 2026Article
- Data-driven design and screening of novelRSC advances · 2026Article
- Framework for evaluating explainable AI in antimicrobial drug discovery.Journal of cheminformatics · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
- MolOrgGPT:Journal of chemical information and modeling · 2026Article
- Optimizing SMILES token sequences via trie-based refinement and transition graph filtering.Journal of cheminformatics · 2026Article
- RL-MSVP: Reinforcement Learning and Multi-scale Validation Pipeline Investigating Azole-Fused Pyrimidines as PotentialACS omega · 2025Article
- Beyond performance: how design choices shape chemical language models.Journal of cheminformatics · 2025Article
- Representation of Molecules by Sequences of Instructions.Journal of chemical information and modeling · 2025Article
- RGBChem: Image-Like Representation of Chemical Compounds for Property Prediction.Journal of chemical theory and computation · 2025Article
- Hybridization of SMILES and chemical-environment-aware tokens to improve performance of molecular structure generation.Scientific reports · 2025Article
- Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.Journal of chemical information and modeling · 2025Article
- AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities?Journal of cheminformatics · 2025Article
- fragSMILES as a chemical string notation for advanced fragment and chirality representation.Communications chemistry · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Life sciences research and experimentation are resource-intensive, requiring extensive trials and considerable time. Often, experiments do not achieve their intended objectives, but progress is made through trial and error, eventually leading to breakthroughs. Machine learning is transforming this traditional approach, providing methods to expedite processes and accelerate discoveries. Deep Learning is becoming increasingly prominent in chemistry, with Convolutional Graph Networks (CGN) being a key focus, though other approaches also show significant potential. This research explores the application of Natural Language Processing (NLP) to evaluate the effectiveness of chemical language representations, specifically SMILES and SELFIES, using tokenization methods such as Byte Pair Encoding (BPE) and a novel approach developed in this study, Atom Pair Encoding (APE), in BERT-based models. The primary objective is to assess how these tokenization techniques influence the performance of chemical language models in biophysics and physiology classification tasks. The findings reveal that APE, particularly when used with SMILES representations, significantly outperforms BPE by preserving the integrity and contextual relationships among chemical elements, thereby enhancing classification accuracy. Performance was evaluated in downstream classification tasks using three distinct datasets for HIV, toxicology, and blood-brain barrier penetration, with ROC-AUC serving as the evaluation metric. This study highlights the critical role of tokenization in processing chemical language and suggests that refining these techniques could lead to significant advancements in drug discovery and material science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.