Molecular design is central to advances in drug discovery, materials science, and chemical engineering, where identifying optimal molecules can lead to breakthroughs in performance, sustainability, and cost. However, the scarcity of high-quality data in these domains often limits the effectiveness and generalizability of machine learning models. To address this challenge, we present a software package that generates, modifies, and analyzes molecular graphs composed of functional chemical groups. Rather than relying on large datasets, our approach uses graph-theoretic and combinatorial methods to systematically explore the space of chemically meaningful structures. By representing molecules as graphs of functional groups and incorporating principles from combinatorics, graph theory, and group theory, our software enables the exhaustive enumeration of structurally unique molecules. We implement symmetry-aware algorithms that efficiently eliminate automorphic redundancies, ensuring completeness without compromising performance. Key features include symmetry identification, both stochastic and exhaustive molecular graph generation, and molecular fragmentation into functional groups using heuristic and exhaustive strategies. Furthermore, our tool is interoperable with existing platforms such as the Molecular Simulation Design Framework (MoSDeF) and RDKit. To demonstrate the utility of the software, we include pedagogical case studies that illustrate its application to real-world problems in molecular design and simulation.
