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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Shaolong Lin

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Open access Sep 2026

Task-adaptive multimodal molecular representations for structure-sensitive property prediction

CAMF (Chirality- and Activity-cliff-aware Multimodal Framework), a task-adaptive framework that models SSPs through selective integration of complementary molecular evidence, reveals that modality relevance is strongly task-dependent, with descriptors and 3D geometry becoming especially important in non-smooth property...

Shaolong Lin, Si-Long Zhai, Shi-Hang Wang et al. · 0 citations
Open access Jul 2026

MAERM: Predicting Enzyme-Reaction Matching Relationships with a Mixed-Attention Model

MAERM has the potential to reduce the experimental cost of measuring enzymes’ catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.

Tiantao Liu, Si-Long Zhai, Shaolong Lin et al. · 0 citations
#machine learning Aug 2025

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction

PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.

Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al. · 13 citations · ⚡1
Jul 2026

TPS-Flow: Physics-Guided Flow-Based Generative Modeling of Protein Transition Paths.

TPS-Flow is presented, a physics-guided flow-based generative framework for end point-conditioned conformational path sampling between predefined protein states (not equilibrium ensembles), thereby bridging atomistic simulation and deep generative modeling of protein transition paths.

Kai Xu, Likun Zhao, Yanan Tian et al. · 0 citations
Aug 2026

CoCoBind: Consistency-Contrastive Multitask Learning for RNA-Ligand Interaction and Binding Site Prediction.

CoBind is presented, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distr...

Shi-Hang Wang, Lin Wang, Wei Zhao et al. · 0 citations

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