Drug Target Interaction (DTI) prediction has witnessed promising performance boosts accompanied by advanced multimodal feature extraction. However, existing approaches suffer from two main difficulties. First, the complex protein structures cannot be well represented by current protein-sequence-based feature extractors. Second, the gap between protein and drug features increases the vulnerability of the obtained classifier thus degrading the prediction robustness. To address these issues, we propose a novel R-DTI method by exploring the second-order relevance in both protein structural feature extraction and DTI prediction phases. Specifically, we construct a pre-trained structural feature extractor that mines the atomic relevance of each amino acid. Then, an inter-feature structure-preserved Riemannian network is designed to expand the existing protein extraction patterns. To improve the prediction robustness, we also develop a Riemannian classifier that uses the second-order protein-drug relevance with a unified feature space. Extensive experimental results demonstrate the merits and superiority of our R-DTI against the state-of-the-art, achieving 1.4% and 1.9% higher AUC-ROC on the BindingDB and DrugBank datasets, respectively.

Model-based methods, which define pose parameters as 3D rotations of 24 joints relative to their parent joints, have proven highly effective in recovering 3D human meshes. Existing methods typically estimate these parameters by coupling features extracted from 2D images. However, the performance of these methods is always limited by spatial ambiguities due to dimensional inconsistencies and kinematic chain ambiguities caused by direct regression of all parameters. To overcome these limitations, we present a novel method that uses 3D joint-aware features within a GRU-based kinematic chain (AFGK) framework to obtain spatially accurate features. Specifically, we first use a self-attention mechanism to enable mutual interactions between features around keypoints, generating 3D joint-aware features, improving spatial accuracy of keypoints, and robustness to extreme poses. Then, a GRU-structured Kinematic chain update module (GRK) is introduced to decouple and update the 24 pose parameters. This module captures the kinematic relationships between keypoints and uses the features of parent joints to dynamically update the pose parameters of child joints, further reducing relative rotation errors. Extensive experimental results show that our method outperforms state-of-the-art competitors on standard benchmarks, significantly improving human reconstruction. The codes will be available on the project homepage: https://github.com/jingchenzhang/AFGK.

Recently, depth ambiguity reduction has made significant progress in monocular human mesh reconstruction. However, regression-based methods struggle to perceive minor depth deviations due to insufficiently exploited monocular cues, such as linear perspective, shadows, and pose priors of the given image. To tackle this limitation, we propose a Depth-aware Updated Feature Feedback (DAUFF) loop to iteratively exploit spatial information from rendered images. Specifically, the normalized depth and dense correspondence images rendered from the ground truth human meshes are adopted as auxiliary supervision for more reliable and adequate cues. Moreover, we also use dense correspondences for feature update to improve the adaptability to new predictions in the loop. Remarkably, dense correspondences can facilitate the rectification of parameters by providing a fine-grained perception of the current prediction. Extensive quantitative comparison on standard benchmarks shows that DAUFF achieves better-aligned reconstruction results than existing approaches. The dataset and codes will be available on the project homepage: https://github.com/github1838/DAUFF.

Recently, mask-fill-based 3D Molecular Generation (MG) methods have become very popular in virtual drug design. However, the existing MG methods ignore the chemical properties of atoms and contain inappropriate atomic position training data, which limits their generation capability. To mitigate the above issues, this paper presents a novel mask-fill-based 3D molecule generation model driven by atomic chemical properties (APMG). Specifically, we construct a new attention-MPNN-based encoder and introduce the electronic information into atom representations to enrich chemical properties. Also, a multi-functional classifier is designed to predict the electronic information of each generated atom, guiding the type prediction of elements and bonds. By design, the proposed method uses the chemical properties of atoms and their correlations for high-quality molecule generation. Second, to optimize the atomic position training data, we propose a novel atomic training position generation approach using the Chi-Square distribution. We evaluate our APMG method on the CrossDocked dataset and visualize the docking states of the pockets and generated molecules. The obtained results demonstrate the superiority and merits of APMG over the state-of-the-art approaches.

MOTIVATION: Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug-target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. RESULTS: We propose a novel Multi-Functional and Robust Drug-Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug-target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: https://github.com/JU-HuaY/MFR.

Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.