Molecular representation learning has become a fundamental component of modern AI-driven drug discovery. Although 2D graph-based models have advanced this field, they often fail to capture the 3D geometric structures essential for many molecular properties, including ADMET. To address this limitation, we propose TheProperty, a self-supervised framework that integrates 2D topological information with 3D geometry. We introduce a distance denoising pre-training objective, which trains the model to recover the original euclidean distance matrix from a noised 3D conformer. For the fine-tuning, we designed a StructureInformed Attention mechanism that effectively fuses pre-trained 2D atom embeddings with the learned 3D distance embeddings, where the distance embeddings are injected as a structural pair bias into the attention mechanism. TheProperty demonstrated significant performance improvements across 22 tasks in the TDC benchmark. Furthermore, various analytical experiments verified that our proposed model effectively learns 3D structural consistency and pharmacological properties. TheProperty provides a robust, 3D-aware, and interpretable molecular representation that effectively integrates 2D topology with 3D geometry for enhanced ADMET prediction.

Generative Flow Networks (GFlowNets) are designed to generate diverse, high-quality samples by sampling proportionally to rewards using flow conservation constraints. However, they suffer from mode collapse, the very problem they were designed to address. We identify that the root cause is forced terminal states arising from artificial trajectory truncation in vast state spaces. Unlike natural terminal states, forced terminals violate flow conservation boundary constraints, causing flow leakage that biases generation toward maximum-length trajectories and triggers mode collapse. We propose Pruning Loss, a novel training objective that enforces flow conservation at forced terminals by requiring both sink flow and total outflow to equal the reward. This dual constraint implicitly drives unnecessary action flows to zero while maintaining non-vanishing gradients for stable convergence. Our theoretical analysis demonstrates that Pruning Loss recovers proper flow conservation in truncated spaces while guaranteeing gradient persistence. Empirical evaluation on molecular generation tasks validates our theoretical predictions. On sparse-reward kinase protein targets, our method achieves substantial improvements over standard objectives. On dense-reward drug-likeness tasks, all methods perform comparably well, validating that flow leakage specifically limits performance in sparse reward landscapes where diverse exploration is critical. Our results establish that correcting boundary constraints at forced terminals is more fundamental than refining balance equations. This principle provides a new foundation for addressing mode collapse in GFlowNets.

Protein representation learning is a research field that converts proteins into numerical representations that computational models can process, playing a crucial role in areas such as structure-based drug development and protein function prediction. However, existing methods struggle to integrate global information from protein language models with local structure and surface data, particularly on protein surfaces which are key to interactions. We propose a novel surface feature initialization method that maps ESM embeddings onto the protein surface and TheProtein, a novel multimodal architecture that integrates global evolutionary context with local structure and surface information. This is achieved through a new surface feature initialization method using ESM embeddings and dedicated TheProtein blocks. TheProtein achieved new state-of-the-art performance on the Atom3D benchmark, with ablation studies and visualizations confirming its effective integration of global ESM information with local data. Our work highlights the importance of evolutionary context in multimodal protein representation and offers a significant methodological advance.

Recent advances in Structure-based Drug Design (SBDD) have leveraged generative models for 3D molecular generation, predominantly evaluating model performance by binding affinity to target proteins. However, practical drug discovery necessitates high binding affinity along with synthetic feasibility and selectivity, critical properties that were largely neglected in previous evaluations. To address this gap, we identify fundamental limitations of conventional diffusion-based generative models in effectively guiding molecule generation toward these diverse pharmacological properties. We propose $\texttt{CByG}$, a novel framework extending Bayesian Flow Network into a gradient-based conditional generative model that robustly integrates property-specific guidance. Additionally, we introduce a comprehensive evaluation scheme incorporating practical benchmarks for binding affinity, synthetic feasibility, and selectivity, overcoming the limitations of conventional evaluation methods. Extensive experiments demonstrate that our proposed $\texttt{CByG}$, framework significantly outperforms baseline models across multiple essential evaluation criteria, highlighting its effectiveness and practicality for real-world drug discovery applications.