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
The results indicate that this fully automated, open-source system holds potential value for improving the efficiency and sustainability of molecular synthesis, and the integration of organic and enzymatic synthesis enhances molecule construction efficiency.
LiTEN achieves state-of-the-art accuracy on standard benchmarks, consistently outperforming leading approaches in both precision and speed, and enables comprehensive modeling tasks, ranging from geometry optimization to free energy surface construction, with high computational efficiency for large biomolecules.
Qun Su, Kai Zhu, Qiaolin Gou et al.· Nature Communications· 2 citations
A deep learning framework that integrates knowledge graph embedding and pre-training strategies to overcome the intractable cold start issue, achieving superior performance and interpretability in drug repurposing is introduced.
Kun Li, Jiacai Yi, Qing Ye et al.· Communications Chemistry· 1 citation
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HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction.
Jialu Wu, Yiheng Zhu, Xiaorui Wang et al.· JACS Au· 0 citations
RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning, and introduces reinforcement learning to capture the relationships among products, reactants, and templates more accurately.
Yafeng Deng, Xinda Zhao, Hanyu Sun et al.· Nature Communications· 18 citations· ⚡2
ERAM aligns pre-trained molecular representations from Protein Language Model with the knowledge of enzyme catalysis by modeling enzymatic reactions as multi-relational data, and demonstrates its potential as a versatile and effective tool for enzyme catalysis research.
The Biotoxins Database (BioTD) is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species.
Gaoang Wang, Hang Wu, Yang Liao et al.· Journal of Chemical Informat...· 0 citations
An integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002 is introduced.
Kai Zhu, Huating Wang, Jin-Tu Zhang et al.· Nature Communications· 0 citations
Spin-torque diodes based on magnetic tunnel junctions have emerged as a promising paradigm for microwave detection and neuromorphic computing due to their intrinsic nonlinear electrical characteristics. Traditional artificial intelligence image processing technology based on Von Neumann architecture faces the bottlenec...
Jianling Hu, Zhenhao Liu, Bin Fang et al.· AIP Publishing· 0 citations
Multi-tenant Software-as-a-Service (SaaS) platforms rely on webhook change notifications from cloud APIs such as Microsoft Graph to synchronize calendar and resource-booking data in near-real time. Empirical analysis reveals a structural failure in this model, which we term webhook event amplification: a single state m...
Aayush Pandey· Zenodo (CERN European Organi...· 0 citations
Industrial Internet Technology Application and Practice is compiled by the team of experts from Wenzhou Vocational Secondary School and fully meets the vocational talent training requirements of modern industries, laying a solid foundation for cultivating versatile and innovative technical talents in the era of Industr...
Li jiang, Wu Jie, Bailang Wang· Figshare· 0 citations
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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