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 m...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.展开更多
Nanoplastics toxicity has been framed as an emerging,distinct research area,purportedly addressing a new threat.While this focus has heightened public awareness and influenced the regulation of plastics,isolating nano...Nanoplastics toxicity has been framed as an emerging,distinct research area,purportedly addressing a new threat.While this focus has heightened public awareness and influenced the regulation of plastics,isolating nanoplastics toxicity risks inefficiently allocating research resources and hindering sustainable management strategies.Here,using data mining and machine learning,we show that research on nanoplastics toxicity closely mirrors that of engineered nanoparticles,a well-established domain of nanotoxicology.Examining 154,745 research articles on nanoparticle and nanoplastics toxicology,we find that both particle types share similar physicochemical properties,biological uptake mechanisms,toxicity profiles,and structure−toxicity relationships.Although nanoplastics pollution is more pervasive in scale and morphological diversity,its toxicological attributes align with those documented for other nanoscale materials.We challenge the notion that nanoplastics pose a distinct,separate risk,proposing instead that integrating nanoplastics toxicity into the broader field of nanotoxicology can streamline research,prevent duplication of effort,and more efficiently guide policies,resource use,and remediation strategies toward globally sustainable outcomes.展开更多
基金supported by the National Natural Science Foundation of China(22106025,22036002)the Introduced Innovative R&D Team Project under the“The Pearl River Talent Recruitment Program”of Guangdong Province(2019ZT08L387)+1 种基金the Basic and Applied Basic Research Foundation of Guangzhou,China(202201010541)the Guangdong Basic and Applied Basic Research Foundation(2022A1515111082).
文摘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.
基金funded by the National Natural Science Foundation of China(22036002,22106025,and 22476056)the introduced innovative R&D team project under the“The Pearl River Talent Recruitment Program”of Guangdong Province(2019ZT08L387)+1 种基金the National Basic Research Program of China(2022 YFC 3701301 and SQ2023YFA1700109)the Specific University Discipline Construction Project(2023B10564001).
文摘Nanoplastics toxicity has been framed as an emerging,distinct research area,purportedly addressing a new threat.While this focus has heightened public awareness and influenced the regulation of plastics,isolating nanoplastics toxicity risks inefficiently allocating research resources and hindering sustainable management strategies.Here,using data mining and machine learning,we show that research on nanoplastics toxicity closely mirrors that of engineered nanoparticles,a well-established domain of nanotoxicology.Examining 154,745 research articles on nanoparticle and nanoplastics toxicology,we find that both particle types share similar physicochemical properties,biological uptake mechanisms,toxicity profiles,and structure−toxicity relationships.Although nanoplastics pollution is more pervasive in scale and morphological diversity,its toxicological attributes align with those documented for other nanoscale materials.We challenge the notion that nanoplastics pose a distinct,separate risk,proposing instead that integrating nanoplastics toxicity into the broader field of nanotoxicology can streamline research,prevent duplication of effort,and more efficiently guide policies,resource use,and remediation strategies toward globally sustainable outcomes.
