ArticleNature communications2024
Chemical language modeling with structured state space sequence models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- An electron-density point-cloud framework for robust protein-ligand interaction prediction.Nature communications · 2026Article
- VeGA-RX and VeGA-SCX: Controllable SMARTS-Guided Generative Transformers for Precision-DrivenJournal of chemical information and modeling · 2026Article
- MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.Briefings in bioinformatics · 2026Article
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- Decoding chemical composition of urinary crystals from ultrasonic echo signals via deep learning.Mikrochimica acta · 2026Article
- Article
- Sample-efficient generative molecular design using memory manipulation.Nature machine intelligence · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.Journal of chemical information and modeling · 2025Article
- Enhancing deep chemical reaction prediction with advanced chirality and fragment representation.Chemical communications (Cambridge, England) · 2025Article
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- VeGA: A Versatile Generative Architecture for Bioactive Molecules across Multiple Therapeutic Targets.Journal of chemical information and modeling · 2025Article
- Going beyond SMILES enumeration for data augmentation in generative drug discovery.Digital discovery · 2025Article
- Article
- TCM-navigator, a deep learning-based workflow for generation and evaluation of traditional Chinese medicine-like compounds for drug development.Briefings in bioinformatics · 2025Article
- A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles.Nature communications · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- An Mcformer encoder integrating Mamba and Cgmlp for improved acoustic feature extraction.Scientific reports · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Generative deep learning is reshaping drug design. Chemical language models (CLMs) - which generate molecules in the form of molecular strings - bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can 'emerge' from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling - uncovering its untapped potential in the molecular sciences.
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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.