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.· Journal of Chemical Informat...· 13 citations· ⚡1
CarsiDock-Cov is presented, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.
Chao Shen, Hongyan Du, Xujun Zhang et al.· Acta Pharmaceutica Sinica B· 12 citations
EpiMII is presented, a Graph Neural Network model for MHC-II epitope design, which learns from the structural features of epitopes to predict their sequences, potentially contributing to vaccine development.
Jiayi Yuan, Xiaowei Xu, Ze-Yu Sun et al.· bioRxiv· 1 citation
The Q‐GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well‐designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination.
Zhijiang Yang, Liangliang Wang, Tengxin Huang et al.· Advancement of science· 6 citations
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This work proposes a novel strategy that incorporates two models: a fragment-based model (Frag-MD) and a character-based model (MD), both integrating a conditional transformer, recurrent neural networks, and reinforcement learning that holds the potential to establish a new paradigm for discovering novel PSs applicable...
Kepeng Chen, Xiaoting Zhang, Ji-Ke Wang et al.· Chemical Science· 4 citations
A unified benchmarking framework is established that enables systematic evaluation of docking methods across diverse tasks and provides critical insights into the strengths and limitations of current docking strategies, thereby informing future developments in protein-protein docking research.
Linlong Jiang, Ke Zhang, Kai Zhu et al.· Journal of Chemical Informat...· 3 citations
Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDoc...
Hui Zhang, Xujun Zhang, Qun Su et al.· Journal of the American Chem...· 6 citations
This Account describes a computational and AI-driven ecosystem for structure-based covalent drug discovery and dives into a suite of cutting-edge, AI-driven computational methods, exploring the potential of deep learning in tasks such as molecular docking, covalent binding site prediction, and lead optimization.
Shi Li, Hongyan Du, Xujun Zhang et al.· Accounts of Chemical Researc...· 4 citations
The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.
Bowei Zhao, Dongxu Li, Yue Yang et al.· Journal of Chemical Informat...· 10 citations· ⚡1
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.· bioRxiv· 15 citations
A pioneering multi-modal framework with TCR-peptide-HLA sequence and structure features incorporating an attention mechanism designed to accurately identify tumor antigens with immunogenic properties, which presents a brand-new and promising approach for cancer immunotherapies that target tumor antigens.
The authors develop mRNABERT, a foundational AI model that designs entire mRNA sequences and demonstrates superior performance across comprehensive benchmarks, which signifies a substantial leap forward in mRNA research and therapeutic development.
Ying Xiong, Aowen Wang, Yu Kang et al.· Nature Communications· 23 citations· ⚡1
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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