FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
This work addresses a critical bottleneck in early drug development for pharmaceutical researchers by providing a unified framework for fragment growing, linking, and merging, though it appears incremental as it builds upon existing GPT-based methods with specific enhancements.
The paper tackled the challenge of designing effective linkers and resolving structural redundancies in Fragment-Based Drug Discovery by introducing FragmentGPT, a unified GPT model that integrates a chemically-aware pre-training strategy and a novel Reward Ranked Alignment with Expert Exploration algorithm, resulting in the generation of chemically valid, high-quality molecules tailored for cancer drug discovery tasks.
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.