SYNCOGEN: A Machine Learning Framework for Synthesizable 3D Molecular Generation Through Joint Graph and Coordinate Modeling

SYNCOGEN introduces a novel machine learning framework that jointly models molecular graphs and 3D atomic coordinates to generate synthesizable molecules, addressing a critical gap in drug discovery. By integrating 2D structural information with 3D geometry, this approach ensures that generated molecules are not only chemically valid and functionally promising but also practically synthesizable using known chemical reactions and building blocks. This advancement enhances the reliability of AI-driven molecular design, bridging the gap between theoretical compound generation and laboratory feasibility, and holds significant potential for accelerating the development of new pharmaceuticals and chemicals.