MOTIVATION: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. RESULTS: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of modality-specific variation. Across held-out retrieval, scaffold-split and UMAP-clustering virtual-screening benchmarks, DECODE improves functional retrieval and early active-compound prioritization over baselines. AVAILABILITY AND IMPLEMENTATION: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.

Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in accurately understanding molecular force fields (MFFs). ED describes the probability of finding electrons at specific locations around atoms or molecules, which uniquely determines all ground state properties (such as energy, molecular structure, etc.) of interactive multi-particle systems according to the Hohenberg-Kohn theorem. However, the calculation of ED relies on the time-consuming first-principles density functional theory (DFT), which leads to the lack of large-scale ED data and limits its application in MLFFs. In this paper, we introduce EDBench, a large-scale, high-quality dataset of ED designed to advance learning-based research at the electronic scale. Built upon the PCQM4Mv2, EDBench provides accurate ED data, covering 3.3 million molecules. To comprehensively evaluate the ability of models to understand and utilize electronic information, we design a suite of ED-centric benchmark tasks spanning prediction, retrieval, and generation. Our evaluation of several state-of-the-art methods demonstrates that learning from EDBench is not only feasible but also achieves high accuracy. Moreover, we show that learning-based methods can efficiently calculate ED with comparable precision while significantly reducing the computational cost relative to traditional DFT calculations. All data and benchmarks from EDBench will be freely available, laying a robust foundation for ED-driven drug discovery and materials science.

Accurate prediction of the drug binding between proteins and ligands can significantly advance the development of structure-based drug design. Recent advances have shown great potential in applying equivariant graph neural network (EGNN) -based methods to learn representations of protein-ligand (PL) complexes. However, most of them typically focus on atom-level graph representations and omit the residue-level information in PL complexes, which are considered essential for understanding the binding mechanism. In this article, we develop a SO(3)-equivariant hierarchical graph neural network (EHGNN) that effectively captures the intrinsic hierarchy of biomolecular structures to enhance the predictive performance of PL interactions. Based on the SO(3)-EHGNN, we further propose a molecular dynamics-powered and energy-guided deep learning framework, called Dynamics-PLI, to capture the spatial structures and energetic information inside molecular dynamic (MD) trajectories. Extensive experimental results show significant improvements over current state-of-the-art methods, with a decrease of 4.03% in RMSE for the binding affinity problem and an average increase of 3.95% in AUROC and AUPRC for the ligand efficacy problem, demonstrating the superiority of Dynamics-PLI for PL interaction prediction. Our findings indicate that the SO(3)-EHGNN exhibits enhanced performance without the necessity of pre-training, emphasizing the inherent analytical strength of SO(3)-EHGNN.

The audit process is a critical component of any company's financial management. It aims to guarantee that the financial statements of a company are accurate, reliable, and in compliance with relevant laws and regulations. Additionally, auditing provides an opportunity for companies to pinpoint areas for improvement, which can help increase efficiency and reduce instances of financial fraud and corruption. As lots of companies with small scales choose to perform the audits externally rather than relying on an internal team, the evaluation of the performance has become a significant problem during building an auditor team. Meanwhile, efficiency is another factor to pursue. This paper formalizes this problem with the Group Multirole Assignment with the Cooperation and Conflict Factors (GMRACCF) model. Specifically, we propose a new method to evaluate the efficiency factors and build a cooperation and conflict factors (CCF) matrix by turning it into time CCFs between auditors. Therefore, with several experiments being conducted, we can know that time factors can have different impacts on the performance of the External Auditor Assignment Problem (EAAP) depending on how much emphasis has been placed on its influence.