Efficient and Optimized Small Organic Molecular Graph Generation Pathway Using a Quantum Generative Adversarial Network

Contemporary drug discovery and development processes require billions of dollars and lengthy amounts of time, which is why researchers are utilizing computational chemistry methods like machine learning to speed up molecular synthesis pathways. Generative Adversarial Networks, in particular, have shown immense potential for accelerating the drug discovery pipeline. However, these models consume a significant amount of computational resources, as they must perform adversarial training on expansive datasets to accurately sample the vast chemical space. Aiming for more efficient runtimes and rigorous analyses of high-dimensional molecular data with quantum computing, we previously developed a hybrid quantum-classical generative adversarial network (QGAN) to generate potential small organic drug candidates. Later, inspired by the classical NetGAN, we upgraded QGAN to a hybrid quantum-classical graph generative adversarial network (QNetGAN) which generated graph structures via random walks. This model was more efficient and had a higher accuracy rate (47.0%) than QGAN (2.3%). Most recently, we developed QNetGAN v2, an upgraded version of QNetGAN that features graph convolution. This model was able to generate 273 out of 300 structurally valid molecules that satisfy the octet rule and Lipinski's Rule of Five, yielding an 91.0% success rate and a training time of 35 minutes. This was made possible by integrating several post-processing algorithms into the original QNetGAN model, including the octet rule satisfaction validator, hydrogen addition algorithm, formal charge calculation, and molecular geometry (.XYZ file coordinate) calculation. In the future, we plan to use the QNetGAN v2 pathway to generate molecules with a wider range of elements and add functionality to calculate important molecular properties, ideally developing a third and more capable version of the QNetGAN molecular generation pathway that is able to outperform the accuracy of classical NetGAN.