Changing trend of spatial temperature score and spatial humidity intelligent score of test model data in the spatial intelligent auxiliary model.

Changing trend of spatial temperature score and spatial humidity intelligent score of test model data in the spatial intelligent auxiliary model.</p

Overall function and structure diagram of architectural space design.

Overall function and structure diagram of architectural space design.</p

Spatial intelligent auxiliary design process of AI model.

Spatial intelligent auxiliary design process of AI model.</p

The changing trend of accuracy loss of different architectural space auxiliary models with the increase of node number.

The changing trend of accuracy loss of different architectural space auxiliary models with the increase of node number.</p

The trend of the compliance of the prediction values of different architectural space auxiliary models with the increase of the number of network nodes.

The trend of the compliance of the prediction values of different architectural space auxiliary models with the increase of the number of network nodes.</p

Experimental environment settings.

In order to carry out a comprehensive design description of the specific architectural model of AI, the auxiliary model of AI and architectural spatial intelligence is deeply integrated, and flexible design is carried out according to the actual situation. AI assists in the generation of architectural intention and architectural form, mainly supporting academic and working theoretical models, promoting technological innovation, and thus improving the design efficiency of the architectural design industry. AI-aided architectural design enables every designer to achieve design freedom. At the same time, with the help of AI, architectural design can complete the corresponding work faster and more efficiently. With the help of AI technology, through the adjustment and optimization of keywords, AI automatically generates a batch of architectural space design schemes. Against this background, the auxiliary model of architectural space design is established through the literature research of the AI model, the architectural space intelligent auxiliary model, and the semantic network and the internal structure analysis of architectural space. Secondly, to ensure compliance with the three-dimensional characteristics of the architectural space from the data source, based on the analysis of the overall function and structure of space design, the intelligent design of the architectural space auxiliary by Deep Learning is carried out. Finally, it takes the 3D model selected in the UrbanScene3D data set as the research object, and the auxiliary performance of AI’s architectural space intelligent model is tested. The research results show that with the increasing number of network nodes, the model fitting degree on the test data set and training data set is decreasing. The fitting curve of the comprehensive model shows that the intelligent design scheme of architectural space based on AI is superior to the traditional architectural design scheme. As the number of nodes in the network connection layer increases, the intelligent score of space temperature and humidity will continue to rise. The model can achieve the optimal intelligent auxiliary effect of architectural space. The research has practical application value for promoting the intelligent and digital transformation of architectural space design.</div

Changing trend of spatial temperature score and spatial humidity intelligent score of training model data in the spatial intelligent auxiliary model.

Changing trend of spatial temperature score and spatial humidity intelligent score of training model data in the spatial intelligent auxiliary model.</p

Data transmission process of the architectural space model.

Data transmission process of the architectural space model.</p

Intelligent auxiliary design structure of architectural space.

Intelligent auxiliary design structure of architectural space.</p

NLDock: a Fast Nucleic Acid–Ligand Docking Algorithm for Modeling RNA/DNA–Ligand Complexes

Nucleic acid–ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid–ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein–ligand interactions, only a few docking programs were presented for nucleic acid–ligand interactions. Here, we have developed a fast nucleic acid–ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid–ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency