3 research outputs found
STRIDE: Structure-guided Generation for Inverse Design of Molecules
Machine learning and especially deep learning has had an increasing impact on
molecule and materials design. In particular, given the growing access to an
abundance of high-quality small molecule data for generative modeling for drug
design, results for drug discovery have been promising. However, for many
important classes of materials such as catalysts, antioxidants, and
metal-organic frameworks, such large datasets are not available. Such families
of molecules with limited samples and structural similarities are especially
prevalent for industrial applications. As is well-known, retraining and even
fine-tuning are challenging on such small datasets. Novel, practically
applicable molecules are most often derivatives of well-known molecules,
suggesting approaches to addressing data scarcity. To address this problem, we
introduce , a generative molecule workflow that generates
novel molecules with an unconditional generative model guided by known
molecules without any retraining. We generate molecules outside of the training
data from a highly specialized set of antioxidant molecules. Our generated
molecules have on average 21.7% lower synthetic accessibility scores and also
reduce ionization potential by 5.9% of generated molecules via guiding