ReviewHeliyon2024
Transformer-based models for chemical SMILES representation: A comprehensive literature review.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Artificial intelligence in impurity prediction: current landscape, challenges, and future directions.Journal of computer-aided molecular design · 2026Review
- Overrepresentation Bias Leads to Performance Overestimation in Blood-Brain Barrier Permeability Prediction Models: Characterization and Mitigation.Journal of chemical information and modeling · 2026Article
- Transformer-accelerated discovery of inhibitors targeting the RpsAJournal of cheminformatics · 2026Article
- Target-aware molecule SMILES generation using a large language model with retrieval-augmented generation, multi-turn memory, and a predictive model.Journal of computer-aided molecular design · 2026Article
- Synthesis, in-vitro and in-silico bioactivity and computational studies of pyrene and p-methoxycinnamaldehyde linked 4-hydroxycoumarins.Scientific reports · 2026Article
- A Multi-Step Computational Workflow for Screening and Prioritizing SHP2-Binding Molecules.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Artificial intelligence-based screening of phytochemicals for targeted cancer therapy.Natural products and bioprospecting · 2026Review
- Accelerate Flash Removal of PFAS from Soil by Human-Guided Bayesian Optimization and Interpretable Machine Learning.ACS nano · 2026Article
- Adaptive AI framework for pharmacokinetics using GATs, transformers, and AutoML.Molecular diversity · 2026Article
- Entropy-based byte patching transformer for self-supervised pretraining of SMILES data.iScience · 2026Article
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Integrating traditional and modern approaches for comprehensive pharmacophore map validation in drug discovery.Journal of computer-aided molecular design · 2026Review
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Machine learning driven LDFrontiers in oncology · 2026Article
- Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets.Nucleic acids research · 2025Article
- Enhancing deep chemical reaction prediction with advanced chirality and fragment representation.Chemical communications (Cambridge, England) · 2025Article
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
- Auxiliary Discrminator Sequence Generative Adversarial Networks for Few Sample Molecule Generation.Journal of chemical information and modeling · 2025Article
- Transformer-based deep learning enables improved B-cell epitope prediction in parasitic pathogens: A proof-of-concept study on Fasciola hepatica.PLoS neglected tropical diseases · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pre-trained chemical language models (CLMs) have attracted increasing attention within the domains of cheminformatics and bioinformatics, inspired by their remarkable success in the natural language processing (NLP) domain such as speech recognition, text analysis, translation, and other objectives associated with language. Furthermore, the vast amount of unlabeled data associated with chemical compounds or molecules has emerged as a crucial research focus, prompting the need for CLMs with reasoning capabilities over such data. Molecular graphs and molecular descriptors are the predominant approaches to representing molecules for property prediction in machine learning (ML). However, Transformer-based LMs have recently emerged as de-facto powerful tools in deep learning (DL), showcasing outstanding performance across various NLP downstream tasks, particularly in text analysis. Within the realm of pre-trained transformer-based LMs such as, BERT (and its variants) and GPT (and its variants) have been extensively explored in the chemical informatics domain. Various learning tasks in cheminformatics such as the text analysis that necessitate handling of chemical SMILES data which contains intricate relations among elements or atoms, have become increasingly prevalent. Whether the objective is predicting molecular reactions or molecular property prediction, there is a growing demand for LMs capable of learning molecular contextual information within SMILES sequences or strings from text inputs (i.e., SMILES). This review provides an overview of the current state-of-the-art of chemical language Transformer-based LMs in chemical informatics for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided in this evolving field.
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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.