ABSTRACT: Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug metabolism in the human body, and CYP activity toward compounds can significantly affect druggability, making early prediction of CYP activity and substrate identification essential for therapeutic development. Here, we established a deep learning model for assessing potential CYP substrates, DeepP450, by fine-tuning protein and molecule pretrained models through feature integration with cross-attention and self-attention layers. This model exhibited high prediction accuracy (0.92) on the test set, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.89 to 0.98 in substrate/nonsubstrate predictions across the nine major human CYPs, surpassing current benchmarks for CYP activity prediction. Notably, DeepP450 uses only one model to predict substrates/nonsubstrates for any of the nine CYPs and exhibits certain generalizability on novel compounds and different categories of human CYPs, which could greatly facilitate early stage drug design by avoiding CYP-reactive compounds. For detail：https://pubs.acs.org/doi/10.1021/acs.jcim.4c00115

ABSTRACT: Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called CLAPE, which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the AUC values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and…

ABSTRACT: Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model, and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding, and to engineer therapeutic antibodies with biophysical properties for specialized drug administration route. For detail：https://elifesciences.org/reviewed-preprints/91512

ABSTRACT: Methods for incorporating alkynes into molecular skeletons have been the subject of continued research. Compared to the cross-couplings used to form C–C(sp3) and C–C(sp2) bonds, couplings that form C–C(sp) bonds, particularly those catalyzed by nickel, remain less explored. In this study, we present a single nickel-catalyzed cross-coupling of terminal alkynes with aryl iodides or bromides for constructing C(sp2)–C(sp) bonds. Our method provides high functional group tolerance and broad substrate scope, resulting in good yields. This nickel-catalyzed coupling reaction is a straightforward way to prepare optoelectronic materials and key building blocks for chemical biology. Notably, our method displays decent selectivity when multi-halide-substituted arenes are used as substrates. Finally, a plausible mechanism is postulated based on experimental studies and density functional theory calculations. For detail：https://doi.org/10.1016/j.xcrp.2023.101573

On August 26th, the finals of the inaugural AI Drug Development Algorithm Competition were successfully held at Tsinghua University School of Pharmacy. The competition was jointly initiated by Tsinghua University School of Pharmacy, Baidu PaddlePaddle, Baidu AI Cloud, and Lingang Laboratory, with strong support from organizations such as the Chinese Pharmaceutical Association. Several pharmaceutical experts were invited to serve on the expert committee. Following a captivating morning of presentations, the ViSNet-Drug team from Microsoft Research Asia was awarded first prize. The second prize was shared by the Blue Wind team from the Shanghai Institute of Materia Medica, Chinese Academy of Sciences, and the MolAI team from Shanghai Jiao Tong University. The third prize was awarded to the Sword team from the Shanghai Institute of Materia Medica, Chinese Academy of Sciences, the Lightning team from Zhejiang University, the Hulala! team from Nanjing University, and the Paipai team. Additionally, eight other teams received honorable mentions.

Tian Yang, from the Tian Bo-Xue research group at Tsinghua University, led a team that introduced an opportunity discovery system developed using computational chemistry, machine learning, and expert systems. This system has identified various ultra-fine pharmaceutical building blocks and conducted their synthesis and industrialization. The 9th China International "Internet+" College Student Innovation and Entrepreneurship Competition, Beijing Division, was organized by the Beijing Municipal Education Commission. It is renowned for its extensive participation, with the highest number of universities involved, the highest level of competition, and the most significant impact in the field of innovation and entrepreneurship. It has become an important platform for advancing reforms in innovation and entrepreneurship education at universities and promoting the comprehensive development of university students.

The SRT project "AI Algorithm for Optimizing Small Molecule Drug ADMET Properties" guided by Professor Boxue-Tian, was awarded the Third Prize in the Basic Science category at the 41st "Challenge Cup" Extracurricular Academic Science and Technology Works Competition held at Tsinghua University in April 2023. team: liaoyizheng; xx yizheng liao

On April 30, 2023, the tenth edition of Tsinghua University's "President's Cup" Innovation Challenge concluded successfully in the Jianhua Building of Tsinghua School of Economics and Management. Following intense competition, 15 out of 127 registered teams advanced to the finals, showcasing remarkable innovation and entrepreneurial potential. Led by Ph.D. student Tian Yang from Tsinghua University's Tian Bo Xue research group, the team introduced the development and industrialization of a national first-class new animal medicine, " HEALITY," targeting major economic animal diseases, particularly prevalent diarrhea. Through a captivating presentation and insightful responses during the competition, they demonstrated the project's innovation, feasibility, and significant societal contributions, ultimately earning them the Challenge Award. This competition not only highlighted the innovative spirit and entrepreneurial potential of Tsinghua University students but also emphasized the importance of innovation for societal progress. The successful organization of Tsinghua University's "President's Cup" Innovation Challenge provided a platform for students to showcase their creative and innovative projects. Additionally, it promoted the continuity and development of the culture of innovation and entrepreneurship, infusing fresh vitality into…

DeeCamp is a philanthropic project initiated by Innovation Works, targeting university students worldwide. The theme of the 2022 edition was "Exploring New Frontiers of Life Sciences with AI" Co-hosted by Innovation Works and Tsinghua University's Institute for Artificial Intelligence Industry Research (AIR), the camp saw the participation of 150 students from top universities worldwide, specializing in fields such as computer science and life sciences. These students formed 30 teams voluntarily. The Boxue-Tian Team successfully made it to the final six teams in the DeeCamp 2022 AI Training Camp and Innovation Challenge. Ultimately, they were honored with the Excellence Award. Team members participating in the competition

ABSTRACT: Enantioselective chemical transformations to introduce sp carbons and trifluoromethyl group into 3,3-disubstituted-2-oxindoles is among chemists' most wanted. We report a single nickel-catalyzed enantioselective domino Heck/Sonogashira annulation/alkynylation process to construct an all-carbon C(sp)-C(sp3) bond-substituted or C(sp)-C(sp3) bond- and trifluoromethyl-disubstituted quaternary center at the C3 position of 2-oxindole, resulting in corresponding 3,3-disubstituted-2-oxindole in high yield with excellent enantioselectivity. Of note, we have isolated and characterized structurally a resting state intermediate, a diphosphorus complex of nickel, (dppp)NiII(alkyl)I, which provided a crucial evidence to support the mechanistic postulation and guided DFT calculations. THE BIGGER PICTURE: Alkynes are important structural motifs in a wide range of natural products and bioactive compounds, as well as synthetic versatility and broad applications in bio-orthogonal labelling, pharmaceuticals, and material science. Although alkynylation using transition metal has historically been accomplished, these processes are restricted to palladium, iridium, and copper catalysis. We report novel single nickel-catalyzed enantioselective domino Heck/Sonogashira coupling for construction of C(sp)-C(sp3) bond-substituted or C(sp)-C(sp3) bond- and trifluoromethyl-disubstituted quaternary carbon centers. Experimental studies, including isolation of organonickel complex combining DFT calculations demonstrate reaction pathway. This single nickel catalyzed asymmetric annulation/couplings of terminal alkynes method also provided a…