MOTIVATION: Predicting protein-ligand binding affinity accurately and quickly is a major challenge in drug discovery. Recent advancements suggest that deep learning-based computational methods can effectively quantify binding affinity, making them a promising alternative. Environmental factors significantly influence the interactions between protein pockets and ligands, affecting the binding strength. However, many existing deep learning approaches tend to overlook these environmental effects, focusing instead on extracting features from proteins and ligands based solely on their sequences or structures. RESULTS: We propose a deep learning method, EM-PLA, which is based on an environment-aware heterogeneous graph neural network and utilizes multimodal data. This method improves protein-ligand binding affinity prediction by incorporating environmental information derived from the biochemical properties of proteins and ligands. Specifically, EM-PLA employs a heterogeneous graph neural network (HGT) with environmental information to improve the calculation of non-covalent interactions, while also considering the interaction calculations between protein sequences and ligand sequences. We evaluate the performance of the proposed EM-PLA through comprehensive benchmark experiments for binding affinity prediction, demonstrating its superior performance and generalization capability compared to state-of-the-art baseline methods. Furthermore, by analyzing the results of the ablation experiments and integrating visual analyses and case studies, we validate the rationale of the proposed method. These results indicate that EM-PLA is an effective method for binding affinity prediction and may provide valuable insights for future applications. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/littlemou22/EM-PLA.

In recent years, researchers have developed novel Quantum-Inspired Neural Network (QINN) frameworks for the Natural Language Processing (NLP) tasks, inspired by the theoretical investigations of quantum cognition. However, we have found that the training efficiency of QINNs is significantly lower than that of classical networks. We analyze the unitary transformation modules of existing QINNs based on the time displacement symmetry of quantum mechanics and discover that they are resembling a mathematical form similar to the first-order Euler method. The high truncation error associated with Euler method affects the training efficiency of QINNs. In order to enhance the training efficiency of QINNs, we generalize QINNs' unitary transformation modules to the Quantum-like high-order Runge-Kutta methods (QRKs). Moreover, we present the results of experiments on conversation emotion recognition and text classification tasks to validate the effectiveness of the proposed approach.

Change detection (CD) in remote sensing (RS) images is a technique used to analyze and characterize surface changes from remotely sensed data at different time periods. However, current deep-learning-based methods sometimes struggle with the diversity of targets in complex RS scenarios, leading to issues, such as false detections and loss of detail. To address these challenges, we propose a method called difference learning-based convolution and network (DLB-CNet) for building CD (BCD). In DLB-CNet, we use difference learning module (DLM), accomplishing the extraction of building change features by enhancing the feature differences between the two images and enhancing model robustness. Additionally, an innovative attention module called integration attention (IA) is introduced to efficiently process semantic information by jointly focusing on global representation subspaces. Our model achieves impressive results on the LEVIR-CD dataset, WHU-CD dataset, and CDD dataset, with${F}1$-scores of 90.56%, 92.28%, and 94.98%, respectively, demonstrating its superiority over the state-of-the-art methods.