1 citations · 1 across the 3 of their papers we have counts for
5 papers
FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
Xuefeng Liu, Songhao Jiang, Qinan Huang +5
Fragment-Based Drug Discovery (FBDD) is a popular approach in early drug development, but designing effective linkers to combine disconnected molecular fragments into chemically an…
Bidirectional Hierarchical Protein Multi-Modal Representation Learning
Xuefeng Liu, Songhao Jiang, Chih-chan Tien +2
Protein representation learning is critical for numerous biological tasks. Recently, large transformer-based protein language models (pLMs) pretrained on large scale protein sequen…
ControllableGPT: A Ground-Up Designed Controllable GPT for Molecule Optimization
Xuefeng Liu, Songhao Jiang, Bo Li +1
Large Language Models (LLMs) employ three popular training approaches: Masked Language Models (MLM), Causal Language Models (CLM), and Sequence-to-Sequence Models (seq2seq). Howeve…
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization
Xuefeng Liu, Songhao Jiang, Siyu Chen +4
Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research delves into the realm of drug optimization and introduce a nove…
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization
Xuefeng Liu, Songhao Jiang, Ian Foster +2
Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates r…