Accurately identifying and classifying protein active sites is crucial for understanding protein mechanisms, drug design, and synthetic biology. Current methods often rely on binary classification and single-modal data, limiting their scope. To address these limitations, we propose M$^{3}$Site, a multimodal framework that integrates protein sequence embeddings, structural graph representations, and functional text annotations for residue-level, multiclass active site prediction. Built upon a curated dataset of 25 883 proteins sourced from UniProt and AlphaFold2, M$^{3}$Site leverages pretrained protein language models, equivariant graph neural networks, and biomedical language models for feature extraction. The function informed cross-attention module enables cross-modal feature fusion, while the adaptive weighted fusion mechanism balances modality contributions. A compound loss function tackles class imbalance, ensuring robust performance. Experimental results show M$^{3}$Site significantly outperforms existing models, and an interactive application has been developed to enhance its practical utility for predictions and visualizations. The dataset, source code for experiments, and interactive application are publicly available at https://github.com/Gift-OYS/M3Site.

The accurate identification of active sites in proteins is essential for the advancement of life sciences and pharmaceutical development, as these sites are of critical importance for enzyme activity and drug design. Recent advancements in protein language models (PLMs), trained on extensive datasets of amino acid sequences, have significantly improved our understanding of proteins. However, compared to the abundant protein sequence data, functional annotations, especially precise per-residue annotations, are scarce, which limits the performance of PLMs. On the other hand, textual descriptions of proteins, which could be annotated by human experts or a pretrained protein sequence-to-text model, provide meaningful context that could assist in the functional annotations, such as the localization of active sites. This motivates us to construct a $\textbf{ProT}$ein-$\textbf{A}$ttribute text $\textbf{D}$ataset ($\textbf{ProTAD}$), comprising over 570,000 pairs of protein sequences and multi-attribute textual descriptions. Based on this dataset, we propose $\textbf{MMSite}$, a multi-modal framework that improves the performance of PLMs to identify active sites by leveraging biomedical language models (BLMs). In particular, we incorporate manual prompting and design a MACross module to deal with the multi-attribute characteristics of textual descriptions. MMSite is a two-stage ("First Align, Then Fuse") framework: first aligns the textual modality with the sequential modality through soft-label alignment, and then identifies active sites via multi-modal fusion. Experimental results demonstrate that MMSite achieves state-of-the-art performance compared to existing protein representation learning methods. The dataset and code implementation are available at https://github.com/Gift-OYS/MMSite.

Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity. In this paper, we study multi-task learning for molecular property prediction in a novel setting, where a relation graph between tasks is available. We first construct a dataset including around 400 tasks as well as a task relation graph. Then to better utilize such relation graph, we propose a method called SGNN-EBM to systematically investigate the structured task modeling from two perspectives. (1) In the latent space, we model the task representations by applying a state graph neural network (SGNN) on the relation graph. (2) In the output space, we employ structured prediction with the energy-based model (EBM), which can be efficiently trained through noise-contrastive estimation (NCE) approach. Empirical results justify the effectiveness of SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM.

Sarcasm is a subtle form of language in which people express the opposite of what is implied.Previous works of sarcasm detection focused on texts.However, more and more social media platforms like Twitter allow users to create multi-modal messages, including texts, images, and videos.It is insufficient to detect sarcasm from multi-model messages based only on texts.In this paper, we focus on multimodal sarcasm detection for tweets consisting of texts and images in Twitter.We treat text features, image features and image attributes as three modalities and propose a multi-modal hierarchical fusion model to address this task.Our model first extracts image features and attribute features, and then leverages attribute features and bidirectional LSTM network to extract text features.Features of three modalities are then reconstructed and fused into one feature vector for prediction.We create a multi-modal sarcasm detection dataset based on Twitter.Evaluation results on the dataset demonstrate the efficacy of our proposed model and the usefulness of the three modalities.