Special Track on AI4Tech: AI Enabling Critical Technologies Papers (Guangzhou)

In modern wireless networks, user mobility modeling plays a pivotal role in learning-based network optimization, particularly in tasks such as user association and resource allocation. Traditional random mobility models, e.g., random waypoint and Gauss Markov model, often fail to accurately capture the distribution patterns of users within real-world areas. While trace-based mobility models and advanced learning-based trajectory generation methods offer improvements, they are frequently limited by the scarcity of real-world trajectory data in target areas, primarily due to privacy concerns. This paper introduces Map2Traj, a novel zero-shot trajectory generation method that leverages the diffusion model to capture the intrinsic relationship between street maps and user mobility. With solely the street map of an unobserved area, Map2Traj generates synthetic user trajectories that closely resemble the real-world ones in trajectory pattern and spatial distribution. This enables the creation of high-fidelity individual user channel states and an accurate representation of the overall network user distribution, facilitating effective wireless network optimization. Extensive experiments across multiple regions in Xi’an and Chengdu, China demonstrate the effectiveness of our proposed method for zero-shot trajectory generation. A case study applying Map2Traj to user association and load balancing in wireless networks is also presented to validate its efficacy in network optimization.

Continual ultra-fine-grained visual recognition (C-UFG) aims to continuously learn to categorize the increasing number of cultivates (VC-UFG) and consistently recognize crops across reproductive stages (HC-UFG), which is a fundamental goal of intelligent agriculture. Despite the progress made in general continual learning, C-UFG remains an underexplored issue. This work establishes the first comprehensive C-UFG benchmark using massive soy leaf data. By analyzing recent pre-trained model (PTM) based continual learning methods on the proposed benchmark, we propose two simple yet effective PTM-based methods to boost the performance of VC-UFG and HC-UFG, respectively. On top of those, we integrate the two methods into one unified framework and propose the first unified model, Unic, that is capable of tackling the C-UFG problem where VC-UFG and HC-UFG co-exist in a single continual learning sequence. To understand the effectiveness of the proposed methods, we first evaluate the models on VC-UFG and HC-UFG challenges and then test the proposed Unic on a unified C-UFG challenge. Experimental results demonstrate the proposed methods achieve superior performance for C-UFG. The code is available at https://github.com/PatrickZad/unicufg.

With feature size shrinking and design complexity increasing, detailed routing has become a crucial challenge in VLSI design. Although detailed routers have been proposed to judiciously handle hard-to-access pins and various design rules, their performances are sensitive to the order of nets to be routed, especially for those sequential routers with ripup-and-reroute scheme. In the published literature, net ordering strategies mainly rely on experts’ knowledge to design heuristics to guarantee their performances. In this paper, we propose a novel transformer-based reinforcement learning framework for net ordering in detailed routing, aiming at automatically gaining failure/success routing experiences and building net order policies to guide detailed routing. Our experimental results show that our framework can effectively reduce the number of design rule violations and routing cost with comparable wirelength and via count, with comparison to state-of-the-art approaches.

The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications. However, most molecular LLMs employ adapter-based architectures that fail to equally integrate molecule and text modalities and lack explicit supervision signals for the molecular modality. To address these issues, we introduce UniMoT, a Unified Molecule-Text LLM adopting a tokenizer-based architecture that expands the vocabulary of LLMs with molecule tokens. Specifically, we introduce a Vector Quantization-driven tokenizer that incorporates a Q-Former to bridge the modality gap between molecule and text. This tokenizer transforms molecular structures into sequences of tokens exhibiting causal dependency, thereby encapsulating both high-level molecular features and textual information. Equipped with this tokenizer, UniMoT unifies molecule and text modalities under a shared token representation and an autoregressive training paradigm. This enables the model to process molecular structures as a distinct linguistic system and generate them in textual form. Through a four-stage training scheme, UniMoT functions as a multi-modal generalist capable of performing both molecule-to-text and text-to-molecule tasks. Extensive experiments demonstrate that UniMoT achieves state-of-the-art performance across a wide range of molecule comprehension and generation tasks.

Learning causal relationships in directed acyclic graphs (DAGs) from multi-type event sequences is a challenging task, especially in large-scale telecommunication networks. Existing methods struggle with the exponentially growing search space and lack global exploration. Gradient-based approaches are limited by their reliance on local information and often fail to generalize. To address these issues, we propose TCCD, a framework that combines Monte Carlo Tree Search (MCTS) with continuous gradient optimization. TCCD balances global exploration and local optimization, overcoming the shortcomings of purely gradient-based methods and enhancing generalization. By unifying various causal structure learning approaches, TCCD offers a scalable and efficient solution for causal inference in complex networks. Extensive experiments validate its superior performance on both synthetic and real-world datasets. Code and Appendix are available at https://github.com/jzephyrl/TCCD.

Rib fracture patterns are key indicators of trauma severity. Detecting and locating these fractures is a critical yet time-consuming task, especially in 3D imaging, due to their minute size and irregular geometries. Existing voxel-based spatial methods fail to capture frequency-domain variations inherent in imaging and do not replicate the progressive refinement process used by clinicians during manual annotation, leading to suboptimal results. We propose a novel regression network, RF-DTR, incorporating a gated regressor mechanism and operating entirely in the frequency domain to address these challenges. Specifically, we present an innovative spatial-frequency transform applied to volumes and corresponding masks. Furthermore, we introduce a Mahalanobis regularization technique to enhance the model and learn high-frequency DCT components relevant to clinical tasks. Finally, a hierarchical penalty is proposed to improve the confidence of the prediction. Extensive experiments confirm our method’s superiority in handling complex, sparsely annotated medical imaging datasets.

Supervised time series prediction relies on directly measured target variables, but real-world use cases such as predicting remaining useful life (RUL) involve indirect supervision, where the target variable is labeled as a function of another dependent variable. Trending temporal regression techniques rely on sequential time series inputs to capture temporal patterns, requiring interpolation when dealing with sparsely and irregularly sampled covariates along the timeline. However, interpolation can introduce significant biases, particularly with highly scarce data. In this paper, we address the RUL prediction problem with data scarcity as time series regression under indirect supervision. We introduce a unified framework called parameterized static regression, which takes single data points as inputs for regression of target values, inherently handling data scarcity without requiring interpolation. The time dependency under indirect supervision is captured via a parametrical rectification (PR) process, approximating a parametric function during inference with historical posteriori estimates, following the same underlying distribution used for labeling during training. Additionally, we propose a novel batch training technique for tasks in indirect supervision to prevent overfitting and enhance efficiency. We evaluate our model on public benchmarks for RUL prediction with simulated data scarcity. Our method demonstrates competitive performance in prediction accuracy when dealing with highly scarce time series data.

The Dynamically Reconfigurable Battery (DRB) systems, which use high-speed power electronic switches to dynamically adjust battery interconnections in real-time, are critical to the performance of the battery pack. Traditional battery management strategies often fail to address multi-objective optimization, leading to imbalanced performance and inadequate energy utilization. To enhance decision-making across multiple objectives, an Evolutionary Ensemble Reinforcement Learning (EERL) framework is proposed in this paper. This framework incorporates evolutionary algorithms to associate ensemble learning, thus improving reinforcement learning (RL) performance. It decomposes a complex objective into multiple sub-objectives, each optimized independently, while incorporating diverse performance metrics into the correlation stage to derive the Pareto optimal solution. The EERL can efficiently mitigate potential adverse effects such as short circuits, disconnections, and reverse charging, thereby effectively reducing capacity differences among various batteries. Simulations and real-world testing demonstrate that the proposed approach overcomes the issue of local optima entrapment in multi-objective optimization scenarios. In a real-world system, an 11.08 % increase in energy efficiency is observed compared to existing approaches.

Large deep learning models have achieved significant success in various tasks. However, the performance of a model can significantly degrade if it is needed to train on datasets with noisy labels with misleading or ambiguous information. To date, there are limited investigations on how to restore performance when model degradation has been incurred by noisy label data. Inspired by the "forgetting mechanism" in neuroscience, which enables accelerating the relearning of correct knowledge by unlearning the wrong knowledge, we propose a robust model restoration and refinement (MRR) framework COLUR, namely Confidence-Oriented Learning, Unlearning and Relearning. Specifically, we implement COLUR with an efficient co-training architecture to unlearn the influence of label noise, and then refine model confidence on each label for relearning. Extensive experiments are conducted on four real datasets and all evaluation results show that COLUR consistently outperforms other SOTA methods after MRR.

Visualizing restoration treatments is a crucial task in dentistry. Traditionally, dentists drag the standard template tooth line onto the inner image from the front view to simulate the outcome of the restoration. This process lacks the precision needed for patient presentation. We find that calculating the camera pose and the relative positions of the upper and lower jaws can enhance visualization accuracy and efficiency while assisting dentists in treatment design. In this work, we leverage the optical flow model and a customized point renderer to help dentists show the treatment outcome to the patient. Specifically, we take the 3D scan model and the intraoral image pair as input. Our framework automatically outputs the camera pose and the relative position of the upper and lower jaws. With these parameters, dentists can directly design the restoration treatment on the 3D scan model without caring about the 2D visualization. Then the designed tooth line and other simulation modalities can be rendered on the intraoral image with our customized renderer. Our framework relieves the labor of dentists and shows the case precisely.

Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Experiments on the Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.

Early detection of Alzheimer’s disease (AD) through spontaneous speech analysis represents a promising, non-invasive diagnostic approach. Existing methods predominantly rely on fusion-based multimodal deep learning, effectively integrating linguistic and acoustic features. However, these methods inadequately model higher-order interactions between modalities, reducing diagnostic accuracy. To address this, we introduce SpeechHGT, a multimodal hypergraph transformer designed to capture and learn higher-order interactions in spontaneous speech features. SpeechHGT encodes multimodal features as hypergraphs, where nodes represent individual features and hyperedges represent grouped interactions. A novel hypergraph attention mechanism enables robust modeling of both pairwise and higher-order interactions. Experimental evaluations on the DementiaBank datasets reveal that SpeechHGT achieves state-of-the-art performance, surpassing baseline models in accuracy and F1 score. These results highlight the potential of hypergraph-based models to improve AI-driven diagnostic tools for early AD detection.

Space situational awareness (SSA) plays an imperative role in maintaining safe space operations, especially given the increasingly congested space traffic around the Earth. Space-based SSA offers a flexible and lightweight solution compared to traditional ground-based SSA. With advanced machine learning approaches, space-based SSA can extract features from high-resolution images in space to detect and track resident space objects (RSOs). However, existing spacecraft image datasets, such as SPARK, fall short of providing realistic camera observations, rendering the derived algorithms unsuitable for real SSA systems. In this work, we introduce SpaceDet, a large-scale realistic space-based image dataset for SSA. We consider accurate space orbit dynamics and a physical camera model with various noise distributions, generating images at the photon level. To extend the available observation window, four overlapping cameras are simulated with a fixed rotation angle. SpaceDet includes images of RSOs observed from 19 km to 63,000 km, captured by a tracker operating in LEO, MEO, and GEO orbits over a period of 5,000 seconds. Each image has a resolution of 4418 x 4418 pixels, providing detailed features for developing advanced SSA approaches. We split the dataset into three subsets: SpaceDet-100, SpaceDet-5000, and SpaceDet-full, catering to various image processing applications. The SpaceDet-full corpus includes a comprehensive dataloader with 781.5 GB of images and 25.9 MB of ground truth labels. Furthermore, we adapted detection and tracking algorithms on the collected dataset using a specified splitting method to accelerate the training process. The trained model can detect RSOs from real-world space observations with zero-shot capability.

Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However, existing imputation methods focus on discrete data points and are unable to effectively model sparse data, resulting in particularly poor performance for imputing substantial missing values. In this paper, we propose a novel approach, ImputeINR, for time series imputation by employing implicit neural representations (INR) to learn continuous functions for time series. ImputeINR leverages the merits of INR in that the continuous functions are not coupled to sampling frequency and have infinite sampling frequency, allowing ImputeINR to generate fine-grained imputations even on extremely sparse observed values. Extensive experiments conducted on eight datasets with five ratios of masked values show the superior imputation performance of ImputeINR, especially for high missing ratios in time series data. We also validate that applying ImputeINR to impute missing values in healthcare data enhances the performance of downstream disease diagnosis tasks.

Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approaches for optimizing complementarity-determining regions (CDRs) to improve developability properties operate in the raw data space, leading to excessively costly evaluations due to the inefficient search process. To address this, we propose LatEnt blAck-box Design (LEAD), a sequence-structure co-design framework that optimizes both sequence and structure within their shared latent space. Optimizing shared latent codes can not only break through the limitations of existing methods, but also ensure synchronization of different modality designs. Particularly, we design a black-box guidance strategy to accommodate real-world scenarios where many property evaluators are non-differentiable. Experimental results demonstrate that our LEAD achieves superior optimization performance for both single and multi-property objectives. Notably, LEAD reduces query consumption by a half while surpassing baseline methods in property optimization. The code is available at https://github.com/EvaFlower/LatEnt-blAck-box-Design.

The rapid advancement of large language models (LLMs) has led to the widespread adoption of video-language models (VLMs) across various domains. However, VLMs are often hindered by their limited semantic discrimination capability, exacerbated by the limited diversity and biased sample distribution of most video-language datasets. This limitation results in a biased understanding of the semantics between visual concepts, leading to hallucinations. To address this challenge, we propose a Multi-level Multimodal Alignment (MMA) framework that leverages a text encoder and semantic discriminative loss to achieve multi-level alignment. This enables the model to capture both low-level and high-level semantic relationships, thereby reducing hallucinations. By incorporating language-level alignment into the training process, our approach ensures stronger semantic consistency between video and textual modalities. Furthermore, we introduce a two-stage progressive training strategy that exploits larger and more diverse datasets to enhance semantic alignment and better capture general semantic relationships between visual and textual modalities. Our comprehensive experiments demonstrate that the proposed MMA method significantly mitigates hallucinations and achieves state-of-the-art performance across multiple video-language tasks, establishing a new benchmark in the field.

With the rapid advancements in quantum computing, cloud-based quantum services have gained increasing prominence. However, due to quantum noise, optimizing the deployment of quantum circuits remains an NP-hard problem with an expansive search space. Existing methods usually use heuristic algorithms to approximate the solution, such as the representative IBM Qiskit. On the one hand, they often find suboptimal deployment solutions. On the other hand, prior technologies do not consider user-specific requirements and can only provide a single deployment strategy. In this paper, we propose QCDeploy that can provide a ranked list of effective deployment strategies to optimize quantum serverless circuit deployment. Specifically, we model quantum circuits as Directed Acyclic Graph (DAG) representations and utilize graph contrastive learning for vector embedding. Then, a tailored list-aware learning-to-rank architecture is employed to generate a list of candidate strategies (prioritizing better strategies). We conduct extensive evaluations involving 45 prevalent quantum algorithm circuits across 3~5 qubits, utilizing 3 IBM quantum physical devices with three types of chip topologies. The results demonstrate that our proposed framework significantly outperforms IBMQ’s default deployment scheme, e.g., achieving 17.95% overhead reduction and increasing the execution success rate by 20%~40%.

Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.

Molecular odor prediction involves using a molecule’s structure to estimate its odor. While accurate prediction remains challenging, AI models can suggest potential odors. Existing methods, however, often rely on basic descriptors or handcrafted fingerprints, which lack expressive power and hinder effective learning. Furthermore, these methods suffer from severe class imbalance, limiting the training effectiveness of AI models. To address these challenges, we propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet). Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level, capturing detailed features crucial for odor prediction. To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM). This module dynamically learns feature importance and frequency modulation, improving the model’s capability to capture relevant patterns. Additionally, a Global Multi-Hierarchy Feature Extraction module (GMFE) is designed to learn global features from the molecular graph topology, enabling the model to fully leverage global information and enhance its discriminative power for odor prediction. To further mitigate the issue of class imbalance, we propose a Chemically-Informed Loss (CIL). Experimental results demonstrate that our approach significantly improves performance across various deep learning models, highlighting its potential to advance molecular structure representation and accelerate the development of AI-driven technologies.

Portfolio optimization encounters persistent challenges in adapting to dynamic markets due to static assumptions and high-dimensional decision spaces. Although reinforcement learning (RL) has emerged as a potential solution, conventional reward engineering often fails to capture complex market dynamics. Recent advances in deep RL and graph neural networks have attempted to enhance market microstructure modeling. However, these methods still struggle with the systematic integration of financial knowledge. To address the above issues, we propose a novel heuristic-guided inverse reinforcement learning framework for portfolio optimization. Specifically, our framework provides an interpretable expert strategy generation mechanism that takes into account sector diversification and correlation constraints. Then, a multi-objective reward optimization method is adopted to adaptively strike a balance between returns and risks. Furthermore, it also utilizes heterogeneous graph policy learning with hierarchical attention mechanisms to explicitly model inter-stock relationships. Finally, we conduct extensive experiments on real-world financial market data to demonstrate that our framework outperforms several state-of-the-art deep learning and RL baselines in terms of risk-adjusted returns. We provide case studies to showcase the ability of our framework to balance return maximization and risk containment. Our code is publicly available at https://github.com/ChloeWenyiZhang/SmartFolio/.

E-bikes (EBs) are a key transportation mode in urban area, especially for couriers of delivery platforms, but underdeveloped EB systems can hinder courier’s productivity due to limited battery capacity. Battery-swapping stations address this issue by enabling riders to exchange depleted batteries for fully charged ones. However, managing supply and demand (SnD) imbalances at these stations has become increasingly complex. To address this, we introduce a new approach that formulates the Battery-Swapping Problem (BSP) as a discrete-time Markov Decision Process (MDP) to capture the dynamics of SnD imbalances. Building on it, we propose a Wasserstein-enhanced Proximal Policy Optimization (W-PPO) algorithm, which integrates Wasserstein distance with reinforcement learning to improve the robustness against uncertainty in forecasting SnD. W-PPO provides a BSP-specific, accurate loss function that reflects reward variations between two policies under real-world simulation. The algorithm’s effectiveness is assessed using key metrics: Shared Battery Utilization Ratio (SBUR) and Battery Supply Ratio (BSR). Simulations on real-world datasets show that W-PPO achieves a 30.59% improvement in SBUR and a 16.09% increase in BSR ensures practical applicability. By optimizing battery utilization and improving EB delivery systems, this work highlights the potential of AI for creating efficient and sustainable urban transportation solutions.

Maps are crucial for various smart city applications as a core component of city geographic information systems (GIS). Developing effective Map Entity Representation Learning methods can extract semantic information for downstream tasks like crime rate prediction and land use classification, with significant application potential. A map comprises three entity types: land parcels, road segments, and points of interest. Most existing methods focus on a single entity type, losing inter-entity relationships and weakening representation effectiveness for real-world applications. Thus, jointly modelling and representing multiple map entity types is essential. However, designing a unified framework is challenging due to map data’s unstructured, complex, and heterogeneous nature. We propose a novel method, HygMap, to represent all map entity types. We model the map as a heterogeneous hypergraph, design an encoder for map entities, and introduce a hybrid self-supervised training scheme. This architecture comprehensively captures the heterogeneous relationships among map entities at different levels. Experiments on nine downstream tasks with two real-world datasets show that our framework outperforms all baselines, with good computational efficiency and scalability.

To enhance the reliability and credibility of graph neural networks (GNNs) and improve the transparency of their decision logic, a new field of explainability of GNNs (XGNN) has emerged. However, two major limitations severely degrade the performance and hinder the generalizability of existing XGNN methods: they (a) fail to capture the complete decision logic of GNNs across diverse distributions in the entire dataset’s sample space, and (b) impose strict prerequisites on edge properties and GNN internal accessibility. To address these limitations, we propose OPEN, a novel cOmprehensive and Prerequisite-free Explainer for GNNs. OPEN, as the first work in the literature, can infer and partition the entire dataset’s sample space into multiple environments, each containing graphs that follow a distinct distribution. OPEN further learns the decision logic of GNNs across different distributions by sampling subgraphs from each environment and analyzing their predictions, thus eliminating the need for strict prerequisites. Experimental results demonstrate that OPEN captures nearly complete decision logic of GNNs, outperforms state-of-the-art methods in fidelity while maintaining similar efficiency, and enhances robustness in real-world scenarios.

The outbreak of pandemic has a huge impact on production and consumption in the business world, especially for the retail sector. As a crucial component of decision-support technology in the retail industry, sales forecasting is significant for production planning and optimizing the supply of essential goods during the pandemic. However, due to the irregular fluctuation pattern caused by uncertainty and the complex temporal correlation between multiple covariates and sales, there is still no effective approach for sales forecasting in this extreme event. To fill this gap, we propose a Pandemic-Compatible Attentive Network (PCAN) for retail sales forecasting. Specifically, to capture the irregular fluctuation patterns from the sales series, we design a fluctuation attention mechanism based on association discrepancy in the time series. Then, a parallel attention module is developed to learn the complex relationship between target sales and various dynamic influence factors in a decoupled manner. Finally, we introduce a novel rectification decoding strategy to indicate fluctuation points in prediction. By evaluating PCAN on four real-world retail food datasets from the SF Express international supply chain system, the results show that our method achieves superior performance over the existing state-of-the-art baselines. The model has been deployed in the supply chain system as a fundamental component to serve a world-leading food retailer.

The deep operator networks (DON), a class of neural operators that learn mappings between function spaces, have recently emerged as surrogate models for parametric partial differential equations (PDEs). However, their full potential for accurately approximating general black-box PDEs remains underexplored due to challenges in training stability and performance, primarily arising from difficulties in learning mappings between low-dimensional inputs and high-dimensional outputs. Furthermore, inadequate encoding of input functions and query positions limits the generalization ability of DONs. To address these challenges, we propose the Dynamical Coupled Operator (DCO), which incorporates temporal dynamics to learn coupled functions, reducing information loss and improving training robustness. Additionally, we introduce an adaptive spectral input function encoder based on empirical mode decomposition to enhance input function representation, as well as a hybrid location encoder to improve query location encoding. We provide theoretical guarantees on the universal expressiveness of DCO, ensuring its applicability to a wide range of PDE problems. Extensive experiments on real-world, high-dimensional PDE datasets demonstrate that DCO significantly outperforms DONs.

Automated medication recommendation is a crucial task within the domain of artificial intelligence in healthcare, where recommender systems are supposed to deliver precise, personalized drug combinations tailored to the evolving health states of patients. Existing approaches often treat clinical records (e.g., diagnoses, procedures) as isolated or unified entities, neglecting the inherent set-structured nature of medical data and the need to model interdependencies among clinical elements. To address the gap, we propose SSPNet, a novel end-to-end framework designed to process complete clinical record sets and directly generate optimal medication sets. SSPNet employs a set-based encoder to effectively capture and represent a patient’s health condition from the electronic health records (EHRs), while a permutation-consistent decoder predicts the entire medication combination as a set. In addition, we introduce a novel personalized representation mechanism to capture the drugs previously used by individual patients. Extensive experiments on MIMIC-Ⅲ and MIMIC-Ⅳ data sets reveal that SSPNet surpasses existing state-of-the-art methods in the accuracy of medication recommendations.

Retrosynthesis, which predicts the reactants of a given target molecule, is an essential task for drug discovery. Retrosynthesis prediction based on molecular graph editing has garnered widespread attention due to excellent interpretability. Existing methods fail to effectively incorporate the chemical knowledge when learning molecular representations. To address this issue, we propose a Knowledge-enhanced Graph Contrastive Learning model (KGCL), which retrieve functional group embeddings from a chemical knowledge graph and integrate them into the atomic embeddings of the product molecule using an attention mechanism. Furthermore, we introduce a graph contrastive learning strategy that generates augmented samples using graph edits to improve the molecular graph encoder. Our proposed method outperforms the strong baseline method Graph2Edits by 1.6% and 3.2% in terms of the top-1 accuracy and top-1 round-trip accuracy on the USPTO-50K dataset, respectively, and also achieves a new state-of-the-art performance among semi-template-based methods on the USPTO-FULL dataset.