ReviewPatterns (New York, N.Y.)2022
SELFIES and the future of molecular string representations.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
91 citing papers in PubMed, 1 synthesis or guideline pooled it, 250 citations in OpenAlex.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Article
- Article
- AI-enhanced adaptive virtual screening of large libraries for ligand discovery.Nature biotechnology · 2026Article
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- MOZAIC: compound growth via in silico reactions and global optimization using Conformational Space Annealing.Bioinformatics (Oxford, England) · 2026Article
- Mol-CADiff: text-conditional molecule generation via causality-aware autoregressive diffusion.Nature communications · 2026Article
- CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN.Interdisciplinary sciences, computational life sciences · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Efficient and valid large molecule generation via self-supervised generative models.npj drug discovery · 2026Article
- Scaling SMILES-based chemical language models for therapeutic peptide engineering.bioRxiv : the preprint server for biology · 2026Article
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions.bioRxiv : the preprint server for biology · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.Journal of cheminformatics · 2026Article
- Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.PLoS computational biology · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A diffusion-based framework for designing molecules in flexible protein pockets.Science advances · 2026Article
- AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.bioRxiv : the preprint server for biology · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
31 authors at 19 institutions in 9 countries.
Funding
Abstract
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Identifiers
What OpenQuestion holds
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.