A Diffusion Framework for Geometrically Valid and Practically Viable 3D Molecular Generation

By operating on full Cartesian geometries, three-dimensional (3D) molecular generative models offer direct control over the spatial arrangements dictating molecular properties. Yet generating structures that are both geometrically valid (reasonable bond lengths and angles, valid valencies, and minimal steric clashes) and practically useful, including charge neutrality and synthetic accessibility, remains challenging. Most existing models primarily target geometric correctness. Here, we present a diffusion-based framework for organic molecule generation that jointly addresses these criteria. The framework combines three complementary components: (i) a dataset construction strategy that includes positive examples, defined as geometrically valid, charge-neutral, and synthetically accessible molecules, together with negative examples containing geometric distortions, disconnected fragments, or non-zero net charges, annotated by scalar scores for distortion, fragment count, net charge, and synthetic accessibility; (ii) conditional training schemes that enable the model to learn both molecular structures and these associated attributes; and (iii) guidance mechanisms that use contrasting attributes during sampling to promote favorable features while suppressing undesirable ones. Across multiple 3D molecular datasets, the framework consistently improves geometric validity, charge neutrality, and synthetic accessibility, advancing 3D molecular generation toward more reliable use in practical organic molecular design.