STRIDE: Structure-guided Generation for Inverse Design of Molecules

• URL: http://arxiv.org/abs/2311.06297v1
• Date: Mon, 6 Nov 2023 08:22:35 GMT
• Title: STRIDE: Structure-guided Generation for Inverse Design of Molecules
• Authors: Shehtab Zaman, Denis Akhiyarov, Mauricio Araya-Polo, Kenneth Chiu
• Abstract summary: $textbfSTRIDE$ is a generative molecule workflow that generates novel molecules with an unconditional generative model guided by known molecules without any retraining. 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.
• Score: 0.24578723416255752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\textbf{STRIDE}$, 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.

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