large language models

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.

Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding of Indoor Building Spaces

Society is steadily embracing the idea of coinhabitant service robots in the built environment for assistance with household tasks and chores. In particular, the growing number of older adults, individuals with disabilities, and people requiring daily assistance has further created an urgent need for intelligent coinhabitant robots capable of supporting mobility, independence, and safety within physical living spaces. The growing complexity of modern built environments and the increasing prevalence of coinhabitant robots have underscored the need for advanced computational methods to support analysis, monitoring, and management. To provide users with meaningful assistance and clear scene understanding in such settings, robots must be able to perceive, interpret, and reason about their surroundings in a way that aligns with human spatial understanding. Place segmentation, which delineates spatial regions such as rooms or functional areas, together with semantic recognition, which assigns meaningful labels to these regions, forms the basis for accurate scene understanding in building-scale environments. Existing deep learning approaches have largely relied on closed-vocabulary detection systems, which are limited to a predefined set of categories of rooms and therefore struggle to interpret diverse or naturalistic descriptions of spaces. Furthermore, most current methods neglect the uncertainty inherent in scene recognition—for example, when rooms have overlapping functions (e.g., kitchen–dining areas) or when people use linguistically similar descriptions to refer to distinct spaces—thereby reducing their effectiveness in ambiguous or complex environments. To address these challenges, we propose an open-vocabulary semantic segmentation and classification pipeline that leverages vision–language models (VLMs) and large language models. Our framework follows a “segment–detect–select” paradigm for open-vocabulary scene understanding, consisting of three modules: (1) segmentation of distinct rooms and regions in the scene; (2) VLM-based similarity scoring between descriptive queries and segmented regions; and (3) adaptive conformal prediction to select among candidate regions while explicitly accounting for uncertainty. Evaluation results on the large-scale Matterport3D dataset, which comprises diverse photorealistic indoor environments, show that the proposed framework outperforms state-of-the-art open-vocabulary methods in scene segmentation [by 30% in AP50 and 5% in mean intersection over union (mIOU)], classification (by 200% in F1 score and 263% in mean average precision), and selection (by 50% in room mIoU). These advances are significant for built environments, as improved scene understanding directly supports safer, more adaptive, and more intuitive coinhabitant assistive agents for humans in complex indoor spaces.

CodeAssay: A Multi-Metric Benchmark with Audited Ground Truth for LLM Code Generation

These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios.

Navigating committor landscape of biomolecules with a general pairwise interaction model

A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScore, an accurate scoring model that learns residue-atom distance distributions for affinity prediction.

Empowering Large Language Models for Relational Data Cleaning and Integration

Do people apply different norms to humans and large language models acting on their behalf? Evidence from norm elicitations in two canonical economic games

Decoding hate: The rise of transformer and large language model architectures in automated hate speech detection

A comparative review of modern large language model paradigms: GPT-4, BERT, Gemini, and DeepSeek

Comparison of GPT-4, BERT (bidirectional encoder representations from transformers), Gemini, and DeepSeek large language models (LLM), focusing on architectures, training methodologies, and real-world applications reveals GPT-4 excels in natural language generation and complex reasoning, supporting up to 128K tokens with moderate latency and higher costs making it effective for conversational artificial intelligence (AI).

An Explicit Interaction-Prompted Diffusion Framework for High-Fidelity 3D Molecular Generation.

Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.

ClauseMiner: Prompt-engineered large language model for accurate, scalable legal clause extraction