Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that have the capability to induce specific protein degradation. While playing a revolutionary role in effectively degrading the protein of interest (POI), PROTACs encounter certain limitations that impede their clinical translation. These limitations encompass off-target effects, inadequate cell membrane permeability, and the hook effect. The advent of nanotechnology presents a promising avenue to surmount the challenges associated with conventional PROTACs. The utilization of nano-proteolysis targeting chimeras (nano-PROTACs) holds the potential to enhance specific tissue accumulation, augment membrane permeability, and enable controlled release. Consequently, this approach has the capacity to significantly enhance the controllable degradation of target proteins. Additionally, they enable a synergistic effect by combining with other therapeutic strategies. This review comprehensively summarizes the structural basis, advantages, and limitations of PROTACs. Furthermore, it highlights the latest advancements in nanosystems engineered for delivering PROTACs, as well as the development of nano-sized PROTACs employing nanocarriers as linkers. Moreover, it delves into the underlying principles of nanotechnology tailored specifically for PROTACs, alongside the current prospects of clinical research. In conclusion, the integration of nanotechnology into PROTACs harbors vast potential in enhancing the anti-tumor treatment response and expediting clinical translation.

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city’s dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area’s fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province.

With the advancement of urbanization and the impact of industrial pollution, the issue of urban ventilation has attracted increasing attention. Research on urban ventilation corridors is a hotspot in the field of urban planning. Traditional studies on ventilation corridors mostly focus on qualitative or simulated research on urban climate issues such as the intensified urban heat island effect, serious environmental pollution, and insufficient climate adaptability. Based on the high-precision urban remote sensing image data obtained by aeromagnetic oblique photography, this paper calculates the frontal area density of the city with reference to the urban wind statistics. Based on the existing urban patterns, template matching technology was used to automatically excavate urban ventilation corridors, which provides scientific and reasonable algorithmic support for the rapid construction of potential urban ventilation corridor paths. It also provides technical methods and decision basis for low-carbon urban planning, ecological planning and microclimate optimization design. This method was proved to be effective through experiments in Deqing city, Zhejiang Province, China.