Balancing the Functionality and Biocompatibility of Materials with a Deep-Learning-Based Inverse Design Framework

作者：Xiaofang Li, Hanle Chen, Jiachen Yan, Guohong Liu, Chengjun Li, Xiaoxia Zhou, Yan Wang, Yinbao Wu, Bing Yan, Xiliang Yan · 发表于：Environment & Health · 年份：2024 · DOI：10.1021/envhealth.4c00088 · 被引用次数：7 · 研究领域：Ionic liquids properties and applications、Innovative Microfluidic and Catalytic Techniques Innovation、Machine Learning in Materials Science

The rational design of molecules with the desired functionality presents a significant challenge in chemistry. Moreover, it is worth noting that making chemicals safe and sustainable is crucial to bringing them to the market. To address this, we propose a novel deep learning framework developed explicitly for inverse design of molecules with both functionality and biocompatibility. This innovative approach comprises two predictive models and one generative model, facilitating the targeted screening of novel molecules from created virtual chemical space. Our method's versatility is highlighted in the inverse design process, where it successfully generates molecules with specified motifs or composition, discovers synthetically accessible molecules, and jointly targets functional and safe properties beyond the training regime. The utility of this method is demonstrated in its ability to design ionic liquids (ILs) with enhanced antibacterial properties and reduced cytotoxicity, addressing the issue of balancing functionality and biocompatibility in molecular design.