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The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and development stage. To address this, we introduce an advanced ensemble machine learning technique designed to estimate the binding affinity between molecules and LCK. This comprehensive method includes the generation and selection of molecular fingerprints, the design of the machine learning model, hyperparameter tuning, and a model ensemble. Through rigorous optimization, the predictive capabilities of our model have been significantly enhanced, raising test R2 values from 0.644 to 0.730 and reducing test RMSE values from 0.841 to 0.732. Utilizing these advancements, our refined ensemble model was employed to screen an MCE -like drug library. Through screening, we selected the top ten scoring compounds, and tested them using the ADP-Glo bioactivity assay. Subsequently, we employed molecular docking techniques to further validate the binding mode analysis of these compounds with LCK. The exceptional predictive accuracy of our model in identifying LCK inhibitors not only emphasizes its effectiveness in projecting LCK-related safety panel predictions but also in discovering new LCK inhibitors. For added user convenience, we have also established a webserver, and a GitHub repository to share the project. [ABSTRACT FROM AUTHOR]

G 2 /M cell cycle checkpoint protein WEE1 kinase is a promising target for inhibiting tumor growth. Although various WEE1 inhibitors have entered clinical investigations, their therapeutic efficacy and safety profile remain unsatisfactory. In this study, we employed a comprehensive virtual screening workflow, which included Schrödinger-Glide molecular docking at different precision levels, as well as the utilization of tools such as MM/GBSA and Deepdock to predict the binding affinity between targets and ligands, in order to identify potential WEE1 inhibitors. Out of ten molecules screened, 50% of these molecules exhibited strong inhibitory activity against WEE1. Among them, compounds 4 and 5 showed excellent inhibitory activity with IC 50 values of 1.069 and 3.77 nM respectively, which was comparable to AZD1775. Further investigations revealed that compound 4 displayed significant anti-proliferative effects in A549, PC9, and HuH-7 cells and could also induce apoptosis and G1 phase arrest in PC9 cells. Additionally, molecular dynamics simulations unveiled the binding details of compound 4 with WEE1, notably the crucial hydrogen bond interactions formed with Cys379. In summary, this comprehensive virtual screening workflow, combined with in vitro testing and computational modeling, holds significant importance in the development of promising WEE1 inhibitors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jin, Xu, Chen, Shen, Gao, Xu, Chi, Lin, Zhou, Shen and Zhang.)

COVID-19 caused by SARS-CoV-2 in late 2019 is still threatening global human health. Although some vaccines and drugs are available in the market, controlling the spread of the SARS-CoV-2 virus remains a huge challenge. 3C-like protease (3CL pro ) is a highly conserved key protease for SARS-CoV-2 replication, and no relevant homologous protein with a similar cleavage site to 3CL pro has been identified in humans, highlighting that development of 3CL pro inhibitors exhibits great promise for treatment of COVID-19. In this review, the authors describe the structure and function of 3CL pro . To better understand the characteristics of SARS-CoV-2 3CL pro inhibitors, the SARS-CoV-2 3CL pro inhibitors reported since 2020 are classified into peptidomimetic covalent inhibitors, non-peptidomimetic covalent inhibitors and non-covalent small molecule inhibitors, and the representative inhibitors, their biological activities and binding models are highlighted. Collectively, we hope that all the information presented here will provide new insights into the design and development of more effective 3CL pro inhibitors against SARS-CoV-2 as novel anti-coronavirus drugs., Competing Interests: There is no conflict of interest to declare., (This journal is © The Royal Society of Chemistry.)