Protein-ligand blind docking is a powerful method for exploring the binding sites of receptors and the corresponding binding poses of ligands. It has seen wide applications in pharmaceutical and biological researches. Previously, we proposed a blind docking server, CB-Dock, which has been under heavy use (over 200 submissions per day) by researchers worldwide since 2019. Here, we substantially improved the docking method by combining CB-Dock with our template-based docking engine to enhance the accuracy in binding site identification and binding pose prediction. In the benchmark tests, it yielded the success rate of ∼85% for binding pose prediction (RMSD < 2.0 Å), which outperformed original CB-Dock and most popular blind docking tools. This updated docking server, named CB-Dock2, reconfigured the input and output web interfaces, together with a highly automatic docking pipeline, making it a particularly efficient and easy-to-use tool for the bioinformatics and cheminformatics communities. The web server is freely available at https://cadd.labshare.cn/cb-dock2/.

Protein–ligand docking is an essential method in computer-aided drug design and structural bioinformatics. It can be used to identify active compounds and reveal molecular mechanisms of biological processes. A successful docking usually requires thorough conformation sampling and scoring, which are computationally expensive and difficult. Recent studies demonstrated that it can be beneficial to docking with the guidance of existing similar co-crystal structures. In this work, we developed a protein–ligand docking method, named FitDock, which fits initial conformation to the given template using a hierarchical multi-feature alignment approach, subsequently explores the possible conformations and finally outputs refined docking poses. In our comprehensive benchmark tests, FitDock showed 40%–60% improvement in terms of docking success rate and an order of magnitude faster over popular docking methods, if template structures exist (> 0.5 ligand similarity). FitDock has been implemented in a user-friendly program, which could serve as a convenient tool for drug design and molecular mechanism exploration. It is now freely available for academic users at http://cao.labshare.cn/fitdock/.

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass of early stages of drug development, and to reduction of the time and cost associated with bringing new therapies to market. Traditional experimental methods are often time‐consuming and expensive, making artificial intelligence (AI) a promising alternative due to its lower cost, computational advantages, and ability to uncover hidden patterns. This review focuses on the availability of AI algorithms in drug development, and their positive and specific roles in revealing repurposing of the existing drugs, especially being integrated with virtual screening. It is shown that the existing AI algorithms excel at analyzing large‐scale datasets, identifying the complicated patterns of drug responses from these datasets, and making predictions for potential drug repurposing. Building on these insights, challenges remain in developing efficient AI algorithms and future research, including integrating drug‐related data across databases for better repurposing, enhancing AI computational efficiency, and advancing personalized medicine.