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2,354 papers
A committor-based method that promotes frequent transitions between the metastable states of the system and allows extensive sampling of the process transition state ensemble and highlights the advantages of a graph-based approach in describing the role of solvent molecules in systems, such as ion pair dissociation or...
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.
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.
AntiBMPNN consistently outperforms AbMPNN, AntiFold, and ProteinMPNN in designing complementarity determining regions (CDR) 1‐3, yielding stronger binding affinities and confirming its potential to improve therapeutic antibody design.
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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.
BBBP-Atlas is proposed, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP, which offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug d...
Graph neural networks (GNNs) have achieved significant success in various graph-related tasks, including node classification, link prediction, and graph classification. However, a critical limitation of many GNN approaches is their dependence on large amounts of labeled data for training. Obtaining such labeled data ca...
This paper proposes a novel approach to cybersecurity threat detection leveraging Graph Neural Networks (GNNs) for personalized modeling. Traditional cybersecurity solutions often rely on generic, rule-based systems that struggle to adapt to the diverse and evolving nature of network environments. We introduce a framew...
This paper introduces Temporal Topology, a novel system for predicting future topology based on event streams. We propose a Temporal Graph Network, a neural network trained to learn geometric patterns from sequences of events, allowing for dynamic analysis and predictive modeling. The system's core claim centers around...
This paper proposes a novel approach to system security analysis leveraging Graph Neural Networks (GNNs). Traditional security assessments often rely on static analysis and may fail to capture the dynamic and interconnected nature of complex systems. We argue that system vulnerabilities can be effectively modeled as gr...
Graph embedding techniques have become increasingly prevalent in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, traditional graph embedding methods often treat nodes and edges independently, neglecting the crucial relational context that governs the s...
This paper proposes a novel approach to cognitive computing centered around the symbolic simulation of human reasoning. The core idea is to build cognitive systems by explicitly modeling the underlying processes of human thought, leveraging constraints, contextual knowledge, and probabilistic inference. We introduce a...
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
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.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
Climate & Sustainability
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