ArticleMolecules (Basel, Switzerland)2023
cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- Smart molecular design of NIR-II organic fluorophores through self-driven iterative evolution, deep learning, and fragment-based assembly.Smart molecules : open access · 2026Article
- Data-driven design and screening of novelRSC advances · 2026Article
- Novel molecular design via a scaffold-aware transformer with multi-scale attention mechanisms.Journal of cheminformatics · 2026Article
- Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- 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
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ACS central science · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- Exploring the macrocyclic chemical space for heuristic drug design with deep learning models.Communications chemistry · 2025Article
- A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.Nature communications · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
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Authors and funding
4 authors.
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Abstract
Deep generative models applied to the generation of novel compounds in small-molecule drug design have attracted a lot of attention in recent years. To design compounds that interact with specific target proteins, we propose a Generative Pre-Trained Transformer (GPT)-inspired model for de novo target-specific molecular design. By implementing different keys and values for the multi-head attention conditional on a specified target, the proposed method can generate drug-like compounds both with and without a specific target. The results show that our approach (cMolGPT) is capable of generating SMILES strings that correspond to both drug-like and active compounds. Moreover, the compounds generated from the conditional model closely match the chemical space of real target-specific molecules and cover a significant portion of novel compounds. Thus, the proposed Conditional Generative Pre-Trained Transformer (cMolGPT) is a valuable tool for de novo molecule design and has the potential to accelerate the molecular optimization cycle time.
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