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China Pharmaceutical University

Academic institutionasia · cn
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Research library9linked papers
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Selected work

Representative Papers

RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction

Aug 09, 2026

Accurate prediction of protein–ligand binding affinity is hindered by data scarcity, experimental heterogeneity, and conformational dependence. This work proposes a novel paradigm that leverages a multi-head frozen stochastic atom graph encoder to generate diverse structural representations, integrates explicit physicochemical interaction fingerprints, and employs a heterogeneous regressor combining neural networks and tree-based models. To enhance generalization and robustness, the approach avoids end-to-end training and instead adopts a validation-based non-negative fusion strategy. Evaluated on the GEMS-reconstructed PDBbind 2020R1 similarity-isolated split and the CASF-2016 benchmark, the method demonstrates consistently strong and stable predictive performance.

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MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

Jul 27, 2026

This work addresses the low accuracy in extrapolating in vitro to in vivo ADMET properties and poor generalization of existing models for small-molecule drugs by proposing MEGA-CL, a novel molecular foundation model. MEGA-CL innovatively integrates self-supervised contrastive learning with a multi-head external attention mechanism within an enhanced message-passing architecture, enabling simultaneous modeling of local substructures and global graph relationships while effectively mitigating the oversmoothing issue common in graph neural networks. Evaluated across 13 benchmark datasets and 21 ADMET tasks, MEGA-CL substantially outperforms state-of-the-art methods: it achieves prediction errors within three-fold for over 75% of tasks, predicts human liver microsomal (HLM) clearance within two-fold error for more than half of 18 novel compounds, and demonstrates prospective validation with all HLM clearance errors below 2.5-fold and 73.3% accuracy in CYP450 inhibition classification.

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EVAF: A Test-Retest Protocol for Selective Parametric Consolidation

Jun 29, 2026

This work addresses the limitation of existing long-running language agents in selectively internalizing experiences, as they struggle to distinguish high-value, persistently useful knowledge from retrievable factual memories. To overcome this, the authors propose the EVAF mechanism, which integrates an Echo-Valence Attractor Field with gated LoRA to enable parameter-level experience consolidation guided by value and surprise signals. A test–retest protocol is introduced to evaluate behavioral persistence under interference. By decoupling memory access from memory depth, the system simultaneously retains retrievable facts and internalizes critical experiential knowledge. Experiments on GPT-2 and TinyLlama demonstrate that EVAF significantly enhances behavioral stability compared to baselines—including frozen models, pure retrieval, and ungated continual updating—while maintaining low parameter drift and minimal cross-character contamination.

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Recent publications

Latest Papers

RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction

Aug 09, 2026

Accurate prediction of protein–ligand binding affinity is hindered by data scarcity, experimental heterogeneity, and conformational dependence. This work proposes a novel paradigm that leverages a multi-head frozen stochastic atom graph encoder to generate diverse structural representations, integrates explicit physicochemical interaction fingerprints, and employs a heterogeneous regressor combining neural networks and tree-based models. To enhance generalization and robustness, the approach avoids end-to-end training and instead adopts a validation-based non-negative fusion strategy. Evaluated on the GEMS-reconstructed PDBbind 2020R1 similarity-isolated split and the CASF-2016 benchmark, the method demonstrates consistently strong and stable predictive performance.

0 citationsRead paper

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

Jul 27, 2026

This work addresses the low accuracy in extrapolating in vitro to in vivo ADMET properties and poor generalization of existing models for small-molecule drugs by proposing MEGA-CL, a novel molecular foundation model. MEGA-CL innovatively integrates self-supervised contrastive learning with a multi-head external attention mechanism within an enhanced message-passing architecture, enabling simultaneous modeling of local substructures and global graph relationships while effectively mitigating the oversmoothing issue common in graph neural networks. Evaluated across 13 benchmark datasets and 21 ADMET tasks, MEGA-CL substantially outperforms state-of-the-art methods: it achieves prediction errors within three-fold for over 75% of tasks, predicts human liver microsomal (HLM) clearance within two-fold error for more than half of 18 novel compounds, and demonstrates prospective validation with all HLM clearance errors below 2.5-fold and 73.3% accuracy in CYP450 inhibition classification.

0 citationsRead paper

EVAF: A Test-Retest Protocol for Selective Parametric Consolidation

Jun 29, 2026

This work addresses the limitation of existing long-running language agents in selectively internalizing experiences, as they struggle to distinguish high-value, persistently useful knowledge from retrievable factual memories. To overcome this, the authors propose the EVAF mechanism, which integrates an Echo-Valence Attractor Field with gated LoRA to enable parameter-level experience consolidation guided by value and surprise signals. A test–retest protocol is introduced to evaluate behavioral persistence under interference. By decoupling memory access from memory depth, the system simultaneously retains retrievable facts and internalizes critical experiential knowledge. Experiments on GPT-2 and TinyLlama demonstrate that EVAF significantly enhances behavioral stability compared to baselines—including frozen models, pure retrieval, and ungated continual updating—while maintaining low parameter drift and minimal cross-character contamination.

0 citationsRead paper