NumMolFormer: an explicit functional group number-guided framework for structure-based drug design

<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>The source code and datasets are available at http://www.github.com/zengzhicun/nummolformer.