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Abstract Protein de novo design is a longstanding challenge in biological field due to the multifaceted physicochemical properties and complexities of proteins. Several deep learning models have emerged for protein de novo design. However, each model exhibits distinct strengths and limitations because of the differences in training strategies or neural network architectures. Therefore, improving the effectiveness of protein design, particularly for functional protein, requires further improvement.
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Wei Zhao SciProfiles Scilit Preprints.org Google Scholar *, Ao Tan SciProfiles Scilit Preprints.org Google Scholar and Congcong Ren SciProfiles Scilit Preprints.org Google Scholar School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471000, China * Author to whom correspondence should be addressed.
1. Introduction With the rapid development of intelligent transportation systems, research on nighttime pedestrian trajectory tracking has become increasingly important [1]. During nighttime driving, poor lighting conditions pose significant challenges to pedestrian-detection and -tracking [2], including multi-task detection [3], pedestrian occlusion [4], detection accuracy in complex environments [5], and real-time performance.
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