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FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
Created by
Haebom
Author
Xuefeng Liu, Songhao Jiang, Qinan Huang, Tinson Xu, Ian Foster, Mengdi Wang, Hening Lin, Rick Stevens
Outline
To address the challenges of linker design for connecting isolated molecular fragments in Fragment-Based Drug Discovery (FBDD), we present the FragmentGPT model, which integrates an energy-based bond-cleavage dictionary learning strategy based on chemical knowledge and the Reward Ranked Alignment with Expert Exploration (RAE) algorithm. FragmentGPT generates linkers that connect diverse molecular segments and simultaneously optimize multiple pharmacological targets, intelligently merging structural redundancies such as overlapping fragments to synthesize optimized molecules. Experimental results on real-world cancer datasets demonstrate that FragmentGPT generates chemically plausible, high-quality molecules, making it suitable for subsequent drug discovery tasks.
Takeaways, Limitations
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Takeaways:
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A novel approach to addressing the challenges of linker design in FBDD.
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Development of an efficient model that integrates chemical knowledge and multi-objective optimization.
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The ability to resolve structural redundancy enables optimized molecular synthesis.
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Proving practicality through performance validation on real cancer datasets.
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Limitations:
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Further validation of the model's generalization performance is needed.
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Applicability evaluation for various disease targets is needed.
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Additional validation through actual synthesis and efficacy evaluation is needed.
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The complexity and computational cost of the RAE algorithm must be taken into consideration.
