Modular Framework for 3D Molecular Generation in Computational Chemistry Applications

Three-dimensional molecular generative models construct molecules by assigning atoms explicit Cartesian coordinates. This formulation allows the molecular generation process to be guided by geometric (e.g., steric effects, molecular shape) and physicochemical constraints toward desired objectives in a given chemical problem. The practical adoption of these models, however, remains limited by two key challenges: the high computational cost associated with model training as well as the lack of standardized platforms that allow models to be developed, evaluated, and compared. The landscape of 3D generative models for molecular design is fragmented, with most methods developed for specific molecular generation objectives. The implementations of these models are scattered across different repositories, software environments, and evaluation protocols, which impedes reproducibility and controlled comparison of different approaches and, generally, limits broader adoption. Here, we introduce MolCraftDiffusion, a modular and extensible platform for building, deploying, and evaluating 3D molecular diffusion models in computational chemistry. Its layered architecture decouples core training logic from model definitions and task implementations, enabling new generative architectures, guidance strategies, and evaluation metrics to be integrated. As part of the package, we implement curriculum learning, a progressive chemical complexity training approach used to construct a pretrained diffusion model on 3D molecular data sets compiled from multiple sources, circumventing the cost of full training in downstream applications. The framework provides a modular set of guidance mechanisms for directing generation toward chemically relevant objectives: structure control via molecular inpainting (systematic exploration of structural variants around a reference) and outpainting (extending molecules with new chemical groups), and property-conditioned generation via gradient-based and classifier-free approaches. We illustrate these capabilities across computational chemistry tasks, including virtual library construction and inverse molecular design. The extensibility of the platform is further demonstrated by porting three architecturally distinct models from the literature: TABASCO, ADiT, and ShEPhERD, each integrated without modifications to the core codebase. The codebase, pretrained models, and examples are available at: https://github.com/lcmd-epfl/MolCraftDiffusion and https://huggingface.co/pregH/MolecularDiffusion.