# FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design

arXiv: https://arxiv.org/abs/2509.11044 · HTML: https://mlsanity.com/paper/2509.11044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design](https://mlsanity.com/paper/2210.05274/) (2022)
- [3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design](https://mlsanity.com/paper/2205.07309/) (2022)
- [Blending Imitation and Reinforcement Learning for Robust Policy Improvement](https://mlsanity.com/paper/2310.01737/) (2023)
- [Multi-Objective Latent Space Optimization of Generative Molecular Design Models](https://mlsanity.com/paper/2203.00526/) (2022)
- [DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization](https://mlsanity.com/paper/2502.07237/) (2025)
- [Active Advantage-Aligned Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2502.07937/) (2025)
- [ControllableGPT: A Ground-Up Designed Controllable GPT for Molecule Optimization](https://mlsanity.com/paper/2502.10631/) (2025)
- [ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization](https://mlsanity.com/paper/2502.06891/) (2025)

## Built on by (most cited first, 1 of 1)

- [Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design](https://mlsanity.com/paper/2608.17381/) (2026)

## Abstract (arXiv)

Fragment-Based Drug Discovery (FBDD) is a popular approach in early drug development, but designing effective linkers to combine disconnected molecular fragments into chemically and pharmacologically viable candidates remains challenging. Further complexity arises when fragments contain structural redundancies, like duplicate rings, which cannot be addressed by simply adding or removing atoms or bonds. To address these challenges in a unified framework, we introduce FragmentGPT, which integrates two core components: (1) a novel chemically-aware, energy-based bond cleavage pre-training strategy that equips the GPT-based model with fragment growing, linking, and merging capabilities, and (2) a novel Reward Ranked Alignment with Expert Exploration (RAE) algorithm that combines expert imitation learning for diversity enhancement, data selection and augmentation for Pareto and composite score optimality, and Supervised Fine-Tuning (SFT) to align the learner policy with multi-objective goals. Conditioned on fragment pairs, FragmentGPT generates linkers that connect diverse molecular subunits while simultaneously optimizing for multiple pharmaceutical goals. It also learns to resolve structural redundancies-such as duplicated fragments-through intelligent merging, enabling the synthesis of optimized molecules. FragmentGPT facilitates controlled, goal-driven molecular assembly. Experiments and ablation studies on real-world cancer datasets demonstrate its ability to generate chemically valid, high-quality molecules tailored for downstream drug discovery tasks.