ArticleNature communications2025
Token-Mol 1.0: tokenized drug design with large language models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN.Interdisciplinary sciences, computational life sciences · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Adaptable Covalent Organic Framework-Hydrogel Microneedles for Glucose-Responsive Isoliquiritigenin Delivery in Diabetic Wound Therapy.Advanced healthcare materials · 2026Article
- Transforming Molecular Science With Large Language Models: From Molecule Understanding to Autonomous Scientific Discovery.Chemistry, an Asian journal · 2026Review
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- The identification of potent nonopioid analgesics and their potential for perioperative use.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- LLM-driven collaborative framework for knowledge-enhanced cancer pain assessment and management.NPJ digital medicine · 2026Article
- The Five-Decade Journey of Small Cell Lung Cancer.Cancer communications (London, England) · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Novel molecule design with POWGAN, a policy-optimized Wasserstein generative adversarial network.Journal of cheminformatics · 2025Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Structure-Based Generation of 3D Small-Molecule Drugs: Are We There Yet?Journal of medicinal chemistry · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Food-derived linear vs. rationally designed cyclic peptides as potent TNF-alpha inhibitors: an integrative computational study.Frontiers in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of large language models (LLMs) into drug design is gaining momentum; however, existing approaches often struggle to effectively incorporate three-dimensional molecular structures. Here, we present Token-Mol, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens. Built on a transformer decoder and trained with causal masking, Token-Mol introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream applications. The model surpasses existing methods, improving molecular conformation generation by over 10% and 20% across two datasets, while outperforming token-only models by 30% in property prediction. In pocket-based molecular generation, it enhances drug-likeness and synthetic accessibility by approximately 11% and 14%, respectively. Notably, Token-Mol operates 35 times faster than expert diffusion models. In real-world validation, it improves success rates and, when combined with reinforcement learning, further optimizes affinity and drug-likeness, advancing AI-driven drug discovery.
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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.