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Shu-Ting Jin

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Book Open access Aug 2026

DiffPro: Decoupled Generative Prior with Diffusion Models for Efficient Few-Shot Drug Synergy Prediction

DiffPro, a novel framework that integrates a Latent Diffusion Model (LDM) as a structural prior, confirms that the diffusion prior successfully regularizes the latent space, mitigating task representation collapse and enabling robust, high-precision predictions for combination therapy design.

Shu-Ting Jin, Xu Guo, An-Qi Huang et al. · 0 citations
Book Open access Aug 2026

The Forgetting-Learning Trade-off: Making Reinforcement Learning Work for Protein Language Models

Diagnostics reveal that RL on PLMs is governed by two reward properties: verifiability, whether the reward is a fixed environment or a learned surrogate vulnerable to distribution shift, and coverage, the fraction of sequence space giving an informative gradient.

Hanqun Cao, Hongrui Zhang, Junde Xu et al. · 0 citations
Book Open access Aug 2026

The Forgetting-Learning Trade-off: Making Reinforcement Learning Work for Protein Language Models

Reinforcement learning (RL) is increasingly applied to Protein Language Models (PLMs), yet its effectiveness varies across tasks, and standard metrics such as pass@k can rise even when the model's solvable problem set is shrinking. We introduce two capability-level diagnostics. The Expansion-Shrinkage Ratio (ESR) measu...

Hanqun Cao, Hongrui Zhang, Junde Xu et al. · 0 citations
Conference Sep 2026

MultiGeo: Predicting Drug-Target Affinity via Adaptive Multi-Conformation Ensemble Learning

MultiGeo is a DTA prediction framework that explicitly leverages multiple protein conformations rather than a single snapshot, and introduces a disagreement-aware gating mechanism that adaptively fuses this ensemble representation with the dominant structure only when the additional conformers provide complementary inf...

Rui-Da Zeng, Cheng Guo, Ya-Jie Meng et al. · 0 citations
Jul 2026

A Scalable Structure-Aware Multimodal Architecture for Accurate Drug-Target Affinity Prediction.

Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which re...

Junlin Xu, Ye Yuan, Menglong Hu et al. · 0 citations

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