MOTIVATION: Rational molecule generation that balances binding affinity with favorable physicochemical properties remains a formidable challenge in structure-based drug design. The number of functional groups is a key determinant, as over-functionalization compromises physicochemical properties, whereas under-functionalization reduces binding affinity. However, current methods are limited in their capacity to incorporate the constraint. RESULTS: To address this, we present NumMolFormer, a Transformer-based framework designed to explicitly model functional group numbers. NumMolFormer adopts a dual-sequence input strategy, integrated with a numerical embedding module and a dual-stream differential attention mechanism, allowing molecular structures and functional group numbers to be encoded separately. This formulation alleviates the inherent limitations of standard Transformer in handling numerical information. In addition, we construct a large-scale dataset of 18 million molecules with functional group annotations for molecular pre-training, and further fine-tune the model using a combination of self-supervised learning and reinforcement learning under protein pocket constraints. The results demonstrate that NumMolFormer effectively leverages functional group information to generate molecules with improved binding affinity, synthetic accessibility, and drug-likeness compared to baseline methods. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at http://www.github.com/zengzhicun/nummolformer.

Ion channels are critical targets in drug development and play an important role in the treatment of various diseases. However, existing prediction methods either generalize across all drug targets or focus narrowly on specific ion channel proteins, leading to limitations in accuracy and versatility. In this study, we propose DeepDICI, a novel deep learning framework that integrates topology-aware protein embeddings with spatial-channel interaction modeling to overcome these challenges. Our approach leverages a LLaMA3-style language model to capture longrange structural dependencies between transmembrane segments and cytoplasmic domains, effectively simulating the grammar of channel folding. In addition, a Cross-Attention network dynamically aligns drug substructures with channel functional domains, enabling the precise identification of state-dependent binding interfaces. Comprehensive evaluations in multiple scenarios demonstrate that DeepDICI achieves the highest precision of prediction$(P R C>0.98)$and maintains exceptional robustness under cold start conditions, showing a 28.9% improvement in terms of recall for novel chemical scaffolds and a 14.3% increase in terms of accuracy for unseen channel targets. All the results indicate that DeepDICI represents a unified and interpretable paradigm for ion channel-targeted drug discovery, bridging computational predictions with biophysical insights into structuredynamic protein systems. The code and datasets for DeepDICI are freely available at https://github.com/CSUBioGroup/DeepDICI.