Retrosynthesis, the process of predicting reactants from products, remains a critical challenge in computational chemistry and drug discovery. While recent deep learning methods have shown strong performance, they remain overly reliant on reaction datasets, which are limited in availability and quality. Large-scale unlabeled molecular data encode rich structural patterns that can be leveraged to learn transferable chemical knowledge, but remain largely unexplored. In this work, we propose KnowRetro (Knowledge-Guided Retrosynthesis Prediction), a chemically-aware framework that learns chemical knowledge from large-scale unlabeled molecules to enhance the accuracy and diversity of retrosynthesis prediction. Specifically, KnowRetro first builds a hierarchical knowledge graph from millions of unlabeled molecules, which captures transformation-relevant relationships among molecules, substructures, and functional groups. It then employs chemically guided pre-training based on substructure decomposition to encourage the model to capture fundamental reaction patterns, followed by fine-tuning with an adapter designed to inject task-relevant knowledge into reactant generation. Extensive experiments demonstrate that KnowRetro achieves high accuracy with improved robustness and diversity in reactant generation. Our code is available at https://github.com/chenyujie1127/KnowRetro.
Yujie Chen, Tengfei Ma, Zhou Yu et al.· Proceedings of the 32nd ACM...· 0 citations
Molecular representation learning (MRL) has shown promise in accelerating drug development by predicting chemical properties. However, imperfectly annotation among datasets pose challenges in model design and explainability. In this work, we formulate molecules and corresponding properties as a hypergraph, extracting three key relationships: among properties, molecule-to-property, and among molecules, and developed a unified and explainable multi-task MRL framework, OmniMol. It integrates a task-related meta-information encoder and a task-routed mixture of experts (t-MoE) backbone to capture correlations among properties and produce task-adaptive outputs. To capture underlying physical principles among molecules, we implement an innovative SE(3)-encoder for physical symmetry, applying equilibrium conformation supervision, recursive geometry updates, and scale-invariant message passing to facilitate learning-based conformational relaxation. OmniMol achieves state-of-the-art performance in properties prediction, reaches top performance in chirality-aware tasks, demonstrates explainability for all three relations, and shows effective performance in practical applications. Our code is available in our https://github.com/bowenwang77/OmniMol public repository. AI models for drug discovery often struggle with real-world, incomplete data. Here, the authors present OmniMol, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability.
Drug repositioning holds promise for discovering new therapeutic applications for existing drugs, accelerating drug development and reducing associated costs. However, current methodologies encounter difficulties in managing diverse network representations, tackling cold start issues, and handling intrinsic attribute representations. Here we introduce a Unified Knowledge-Enhanced deep learning framework for Drug Repositioning (UKEDR), which integrates knowledge graph embedding, pre-training strategies, and recommendation systems to address these challenges. To overcome the cold start issue, UKEDR utilizes a semantic similarity-driven embedding approach. Our evaluations show that UKEDR performs better than various baselines, including classical machine learning, network-based, and deep learning approaches. In cold start scenarios, it demonstrates an improved capability in handling unseen nodes and generalizing to new compounds. The model also demonstrates strong robustness on imbalanced datasets and shows excellent generalization capabilities in specific drug-centric and disease-centric cold-start scenarios, validating its potential for real-world applications. Drug repositioning offers a promising avenue for accelerating drug development, yet existing methods struggle with network diversity, cold start issues, and intrinsic attribute representation. Here, the authors introduce UKEDR, a deep learning framework that integrates knowledge graph embedding and pre-training strategies to overcome the intractable cold start issue, achieving superior performance and interpretability in drug repurposing.
Kun Li, Jiacai Yi, Qing Ye et al.· Communications Chemistry· 1 citation