ArticleJournal of cheminformatics2023
Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning.CPT: pharmacometrics & systems pharmacology · 2026Article
- Data-driven design and screening of novelRSC advances · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- Entropy-based byte patching transformer for self-supervised pretraining of SMILES data.iScience · 2026Article
- AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.Journal of computer-aided molecular design · 2026Article
- InfoMedex: drug-drug interaction prediction via a multimodal CNN-transformer model.Frontiers in drug safety and regulation · 2026Article
- Article
- Refining EI-MS library search results through atomic-level insights.Communications chemistry · 2025Article
- ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.Briefings in bioinformatics · 2025Article
- Hybridization of SMILES and chemical-environment-aware tokens to improve performance of molecular structure generation.Scientific reports · 2025Article
- Comparative Analysis of Recurrent Neural Networks with Conjoint Fingerprints for Skin Corrosion Prediction.Journal of chemical information and modeling · 2025Article
- Positional embeddings and zero-shot learning using BERT for molecular-property prediction.Journal of cheminformatics · 2025Article
- Transformer-based models for chemical SMILES representation: A comprehensive literature review.Heliyon · 2024Review
- Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling.Scientific reports · 2024Article
- ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.Journal of chemical information and modeling · 2024Article
- Representations of lipid nanoparticles using large language models for transfection efficiency prediction.Bioinformatics (Oxford, England) · 2024Article
- Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.International journal of molecular sciences · 2024Article
- Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition.Journal of cheminformatics · 2024Article
- Article
- Synergistic activity of nootropic herbs as potent therapeutics for Alzheimer's disease: A cheminformatics, pharmacokinetics, and system pharmacology approach.Journal of Alzheimer's disease reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. To address this issue, we developed the atom-in-SMILES tokenization scheme that eliminates ambiguities in the generic nature of SMILES tokens. Our results in multiple chemical translation and molecular property prediction tasks demonstrate that proper tokenization has a significant impact on prediction quality. In terms of prediction accuracy and token degeneration, atom-in-SMILES is more effective method in generating higher-quality SMILES sequences from AI-based chemical models compared to other tokenization and representation schemes. We investigated the degrees of token degeneration of various schemes and analyzed their adverse effects on prediction quality. Additionally, token-level repetitions were quantified, and generated examples were incorporated for qualitative examination. We believe that the atom-in-SMILES tokenization has a great potential to be adopted by broad related scientific communities, as it provides chemically accurate, tailor-made tokens for molecular property prediction, chemical translation, and molecular generative models.
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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.