Finally, in the third case we created a simple automated protein-ligand design pipeline using BuildAMol. To that end, we prepared a small library of some 200 fragments of small organic molecules. We then developed a class to assemble fragments based on instructions encoded numerically in Numpy arrays. The assembled molecules were then passed to the software library dockstring to generate a docking score. For our example case, we chose the dopamine D2 receptor (Uniprot ID F8VUV1), which is bound by the drug Risperidone. Our "pipeline's" main function would generate a molecule from a Numpy array, and return a docking score for use with an optimization algorithm. For the example, we used Scipy's Nelder-Mead algorithm which we ran for five iterations only. We repeated the test three times. Interestingly, our simple pipeline was already able to generate molecules that bind in the same pocket as Risperidone (figure , lower left panel). Moreover, our best prediction (the blue ligand in figure , lower left panel) showed some features that were structurally similar to Resperidone and produced a docking score of -11.6, which is in a similar range to Risperidone's own docking score of -11.9. As a proof of concept to demonstrate BuildAMol in a workflow involving deep learning applications, we performed a follow-up experiment where we created multiple derivatives from the generated ligand by adding various functional groups to one position. We then successfully docked the derivatives using the deep learning tool DiffDock , presenting a practical illustration of BuildAMol's compatibility with state-of-the-art deep learning tools (figure , lower right panel). In this work, we presented BuildAMol, a versatile and user-friendly Python library designed to empower researchers in fragment-based molecular modeling. BuildAMol caters to a broad range of applications, from de novo assembly of complex molecules to the modification and optimization of existing structures. Its focus on user control and extensibility supports both manual and semi-automated workflows, making it suitable for expert-driven modeling tasks.