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Chemical structure segmentation constitutes a pivotal task in cheminformatics, involving the extraction and abstraction of structural information of chemical compounds from text-based sources, including patents and scientific articles. This study introduces a deep learning approach to chemical structure segmentation, employing a Vision Transformer (ViT) to discern the structural patterns of chemical compounds from their graphical representations. The Chemistry-Segment Anything Model (ChemSAM) achieves state-of-the-art results on publicly available benchmark datasets and real-world tasks, underscoring its effectiveness in accurately segmenting chemical structures from text-based sources. Moreover, this deep learning-based approach obviates the need for handcrafted features and demonstrates robustness against variations in image quality and style. During the detection phase, a ViT-based encoder-decoder model is used to identify and locate chemical structure depictions on the input page. This model generates masks to ascertain whether each pixel belongs to a chemical structure, thereby offering a pixel-level classification and indicating the presence or absence of chemical structures at each position. Subsequently, the generated masks are clustered based on their connectivity, and each mask cluster is updated to encapsulate a single structure in the post-processing workflow. This two-step process facilitates the effective automatic extraction of chemical structure depictions from documents. By utilizing the deep learning approach described herein, it is demonstrated that effective performance on low-resolution and densely arranged molecular structural layouts in journal articles and patents is achievable. [ABSTRACT FROM AUTHOR]

The latest biological findings observe that the motionless "lock‐and‐key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory‐related studies, thus hindering the possibility of supervised learning. A spatial‐temporal pre‐training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self‐supervised learning tasks: atom‐level prompt‐based denoising generative task and conformation‐level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time‐dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task‐specific fine‐tuning. A huge improvement from current state‐of‐the‐art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision‐recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor. [ABSTRACT FROM AUTHOR]

In the multimedia era, exploding image and video data highlight the importance of video compression for storage and transmission. The All-Intra structure is a coding mode in HEVC and VVC, in which each frame is encoded using intra coding, and in this paper learned All-Intra coding is explored on the basis of the research of the learned image compression. A channel-wise contextual module based on channel segmentation is introduced to fully exploit non-local information. Then, two distinct attention mechanisms are designed for different feature layers to enhance the effectiveness of the transform network. Additionally, a post-processing module is employed to enhance the quality of decoded frames. Experimental results on the Kodak and Tecnick datasets demonstrate that the proposed method performs better than the majority of the recent learning-based methods and traditional image codecs (BPG, JPEG2000 and JPEG), and also perform better than traditional video codecs in terms of PSNR. • A novel video compression framework specially designed for I frame is introduced. • A channel-wise contextual module enhances context-based entropy modeling. • A post-processing module aims to enhance the quality of the reconstructed frames. • Two distinct attention mechanisms are designed for different feature layers. [ABSTRACT FROM AUTHOR]