Fragment growth-based discovery of novel TNIK inhibitors for the treatment of colorectal cancer.

Traf2-and Nck-interacting protein kinase (TNIK) plays an important role in regulating signal transduction of the Wnt/ß-catenin pathway and is considered an important target for the treatment of colorectal cancer. Inhibiting TNIK has potential to block abnormal Wnt/ß-catenin signal transduction caused by colorectal cancer mutations. We discovered a series of 6-(1-methyl-1H-imidazole-5-yl) quinoline derivatives as TNIK inhibitors through Deep Fragment Growth and virtual screening. Among them, 35b exhibited excellent TNIK kinase and HCT116 cell inhibitory activity with IC50 values of 6 nM and 2.11 µM, respectively. 35b also shown excellent kinase selectivity, PK profiles, and oral bioavailability (84.64%). At a p. o. dosage of 50 mg/kg twice daily 35b suppressed tumor growth on the HCT116 xenograft model. Taken together, 35b is a promising lead compound of TNIK inhibitors, which merits further investigation.

Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors.

Deep generative models have become crucial tools in de novo drug design. In current models for multiobjective optimization in molecular generation, the scaffold diversity is limited when multiple constraints are introduced. To enhance scaffold diversity, we herein propose a local scaffold diversity-contributed generator (LSDC), which can be utilized to generate diverse lead compounds capable of satisfying multiple constraints. Compared to the state-of-the-art methods, molecules generated by LSDC exhibit greater diversity when applied to the generation of inhibitors targeting the NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3). We present 12 molecules, some of which feature previously unreported scaffolds, and demonstrate their reasonable docking binding modes. Consequently, the modification of selected scaffolds and subsequent bioactivity evaluation lead to the discovery of two potent NLRP3 inhibitors, A22 and A14, with IC50 values of 38.1 nM and 44.43 nM, respectively. And the oral bioavailability of compound A14 is very high (F is 83.09% in mice). This work contributes to the discovery of novel NLRP3 inhibitors and provides a reference for integrating AI-based generation with wet experiments.

Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning.

Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure-odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules.

DFBP: a comprehensive database of food-derived bioactive peptides for peptidomics research.

MOTIVATION: Food-derived bioactive peptides (FBPs) have demonstrated their significance in pharmaceuticals, diets and nutraceuticals, benefiting public health and global ecology. While significant efforts have been made to discover FBPs and to elucidate the underlying bioactivity mechanisms, there is lack of a systemic study of sequence-structure-activity relationship of FBPs in a large dataset. RESULTS: Here, we construct a database of food-derived bioactive peptides (DFBP), containing a total of 6276 peptide entries in 31 types from different sources. Further, we develop a series of analysis tools for function discovery/repurposing, traceability, multifunctional bioactive exploration and physiochemical property assessment of peptides. Finally, we apply this database and data-mining techniques to discover new FBPs as potential drugs for cardiovascular diseases. The DFBP serves as a useful platform for not only the fundamental understanding of sequence-structure-activity of FBPs but also the design, discovery, and repurposing of peptide-based drugs, vaccines, materials and food ingredients. AVAILABILITY AND IMPLEMENTATION: DFBP service can be accessed freely via http://www.cqudfbp.net/. All data are incorporated into the article and its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network.

Identifying the taste characteristics of molecules is essential for the expansion of their application in health foods and drugs. It is time-consuming and consumable to identify the taste characteristics of a large number of compounds through experiments. To date, computational methods have become an important technique for identifying molecular taste. In this work, bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener are predicted using three structure-taste relationship models based on the convolutional neural networks (CNN), multi-layer perceptron (MLP)-Descriptor, and MLP-Fingerprint. The results showed that all three models have unique characteristics in the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener. For the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener, the MLP-Fingerprint model exhibited a higher predictive AUC value (0.94, 0.94 and 0.95) than the MLP-Descriptor model (0.94, 0.84 and 0.87) and the CNN model (0.88, 0.90 and 0.91) by external validation, respectively. The MLP-Descriptor model showed a distinct structure-taste relationship of the studied molecules, which helps to understand the key properties associated with bitterants and sweeteners. The CNN model requires only a simple 2D chemical map as input to automate feature extraction for favorable prediction. The obtained models achieved accurate predictions of bitterant/non-bitterant, sweetener/non-sweetener and bitterant and sweetener, providing vital references for the identification of bioactive molecules and toxic substances.

Identification of the functional food ingredients with antithrombotic properties via virtual screen and experimental studies.

Thrombin is a key therapeutic target protein of thrombosis. To date, massive studies have focused on the exploration of antithrombotic compounds. Here we capitalize on molecular docking, molecular simulations and spectroscopic experiments for virtually screening natural products that can inhibit thrombin and elucidating their interaction mechanism. Six compounds are screened from a natural product database by a cross-analysis based on two semi-flexible molecular docking methods. We show that four compounds can effectively inhibit thrombin and Calceolarioside B is the most competitive one based on enzyme inhibition experiments. Moreover, the binding free energies of these compounds with thrombin exhibit a consistent rank trend with their enzyme inhibition assay results. In addition, the Van der Waals is the main force to drive the interaction between the ligands and the receptor, which can be deduced from the fluorescence spectral results. This work provides a new insight into the development of antithrombotic natural compounds.