ArticleChemical science2021
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Censoring chemical data to mitigate dual use risk.Digital discovery · 2026Article
- Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.ACS omega · 2026Review
- A geometry-aware generative framework integrating GPS-VAE and Transformer-SELFIES for structure-based de novo drug design.Journal of molecular modeling · 2026Article
- ChemBang: Expanding the Chemical Space Around Small Molecules.Molecular informatics · 2026Article
- Q-CaDD: accelerating in silico methodologies with quantum computation and machine learning for Epidermal growth factor receptor.Scientific reports · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- AI-guided competitive docking for virtual screening and compound efficacy prediction.npj drug discovery · 2026Article
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Selector: A General Python Library for Diverse Subset Selection.Journal of chemical information and modeling · 2026Article
- Explainable artificial intelligence for molecular design in pharmaceutical research.Chemical science · 2026Review
- Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design.Industrial & engineering chemistry research · 2026Article
- Bidirectional reinforcement learning neural network for constrained molecular design.Scientific reports · 2025Article
- From Bits to Bonds: High-Throughput Virtual Screening of Ribonucleic Acid Nanocarriers Using a Combinatorial Approach of Machine Learning and Molecular Dynamics.Journal of the American Chemical Society · 2025Article
- Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025Article
- Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.Nature biotechnology · 2025Article
- Selector: A General Python Library for Diverse Subset Selection.bioRxiv : the preprint server for biology · 2025Article
- Stereochemistry-aware string-based molecular generation.PNAS nexus · 2025Article
- A Review of Global Optimization Methods for Molecular Structures: Algorithms, Applications and Perspectives.Journal of computational chemistry · 2025Review
- Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions.Nanomaterials (Basel, Switzerland) · 2025Review
1 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED - a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.
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