Here, we use a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our mutant and scaffold combination (FAST-PETase: functional, active, stable and tolerant PETase) contains five mutations compared to wild-type PETase (N233K/R224Q/S121E from prediction ...
Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604, 662–667 (2022). CAS PubMed ADS Google Scholar Giessel, A. et al. Therapeutic enzyme engineering using a generative neural network. Sci. Rep. 12, 1536 (2022). CAS PubMed PubMed Central ADS Google Scholar...
Lu XZ, Intelligent design method for beam and slab of shear wall structure based on deep learnin...
This study has aims to determine the development of implementing machine learning in several engineering majors. The used method was a literature study, and secondary data was used from reputable international journals and published in 2015 to 2019 from each publisher, which is IEEEXplore, Springer ...
An unsupervised machine learning approach for ground-motion spectra clustering and selection Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in appli... RB Bond,P Ren,JFSH Hajjar - 《Earthquake Engineeri...
Machine Learning: New Ideas and Tools in Environmental Science and Engineering 7032 01:09:00 如何识别陆地与海洋气溶胶来源? 3784 53:00 Carbon source/sink functions of rice paddies: Biogeochemical processes underlying the trade-off 1937 01:28:00 ...
application space to explore, an interesting question is: Can we employ high-throughput computation and machine learning to accelerate new HEAs developments from discovery to deployment? Recent breakthroughs in high-throughput computing techniques and machine learning techniques present a brand-new ...
Machine learning approaches and technologies have been extensively developed and widely applied in numerous areas for solving complex real-world problems in the last two decades. In line with this, machine learning assisted structural engineering is emerging as a highly-researched area and shows promisin...
Also, engineering design tasks such as topology optimization, generating unique design concepts, and even computer-aided engineering and simulation can be performed by deep learning methods (Oh et al., 2019). More details on such specific applications are given in the following sections. GAN, ...
Designing protein sequences towards desired properties is a fundamental goal of protein engineering, with applications in drug discovery and enzymatic engineering. Machine learning-guided directed evolution has shown success in expediting the optimization cycle and reducing experimental burden. However, efficie...