SLOGEN: A Structure-based Lead Optimization Model Unifying Fragment Generation and Screening

Lead optimization plays an important role in preclinical drug discovery. While deep learning has accelerated this process, structure–based approaches that leverage 3D protein–ligand information remain underexplored. Existing models could improve predicted affinity but often yield synthetically inaccessible compounds, whereas screening–based methods limit chemical novelty by relying on fixed fragment libraries. To bridge the gap, we introduce Slogen, a Structure-based Lead Optimization algorithm unifying fragment Generation and screENing. To achieve this, Slogen integrates a transformer–based variational autoencoder, pretrained on the BindingNet v2 dataset, with an E(3)–equivariant graph neural network that models 3D protein–fragment interactions. This unified framework enables both fragment generation and similarity–based screening, simultaneously addressing synthetic tractability and structural diversity. The benchmarking study shows that Slogen matches or surpasses state–of–the–art methods while exploring broader chemical space. Case studies on the Smoothened and D1 dopamine receptors demonstrate its capacity to design high–affinity, drug–like molecules, providing a practical method for structure–guided lead optimization.