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Bruton's tyrosine kinase (BTK) is an attractive target for the treatment of malignancy and inflammatory/autoimmune diseases. Most of the covalent BTK inhibitors would induce off-target side effects and drug resistance. To improve the drug safety of BTK inhibitors, non-covalent inhibitors have attracted more and more attention. We designed a series of novel pyrido[3,4-b]indol-1-one derivatives (N-A and N-B) via scaffold hopping from CGI-1746. The structure-activity relationship (SAR) of the newly-synthesized compounds was explored. The results showed that compounds 12 and 18 exhibited potent enzymatic potency against BTK with IC 50 values of 0.22 μM and 0.19 μM, respectively. In lymphoma cell lines U-937 cells and Ramos cells, compounds 12 and 18 displayed comparative antiproliferative activity with Ibrutinib. Moreover, compound 12 induced G1-phase cell cycle arrest and apoptosis in U-937 cells. And it could effectively inhibit tumor growth in U-937 xenograft mouse model (TGI = 41.90% at 50 mg/kg). In all, the new pyrido[3,4-b]indol-1-one derivatives have the antitumor potency by BTK inhibition and were worthy of further exploration., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software., (© 2021 Wiley Periodicals LLC.)

Janus kinases (JAKs) including JAK1, JAK2, JAK3, and TYK2 are members of a family of intracellular nonreceptor tyrosine kinases, which have been demonstrated to be critical in the cell signaling pathway and involved in inflammatory diseases and cancer. V617F mutation in JAK2 has been implicated in polycythaemia vera (PV), essential thrombocythaemia (ET) and myelofibrosis (MF). Here, we described the design, synthesis, and biological evaluation of a series of 2-aminopyridine derivatives. The results of enzymatic activity assays supported compound 16m-(R) as a potential and selective JAK2 inhibitor, which exhibited high inhibitory activity with an IC 50 of 3 nM against JAK2, and 85- and 76-fold selectivity over JAK1 and JAK3, respectively. Structure-activity relationships (SAR) and mechanistic analysis demonstrated that 16m-(R) might be a promising selective JAK2 inhibitor for further study., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)

Aberrant activation of B cell receptor (BCR) signal transduction cascade contributes to the propagation and maintenance of B cell malignancies. The discovery of mall molecules with high potency and selectivity against Bruton's tyrosine kinase (BTK), a key signaling molecule in this cascade, is particularly urgent in modern treatment regimens. Herein, a series of pyrimido[4,5-d]pyrimidine-2,4(1H,3H)-dione derivatives were reported as potent BTK inhibitors. Compounds 17 and 18 displayed strong BTK inhibitory activities in the enzymatic inhibition assay, with the IC 50 values of 1.2 and 0.8 nM, respectively, which were comparable to that of ibrutinib (IC 50  = 0.6 nM). Additionally, compound 17 had a more selective profile over EGFR than ibrutinib. According to the putative binding poses, the molecular basis of this series of compounds with respect to potency against BTK and selectivity over EGFR was elucidated. In further experiments at cellular level, compounds 17 and 18 significantly inhibited the proliferation of Ramos and TMD8 cells. And they arrested 75.4% and 75.2% of TMD8 cells in G1 phase, respectively, at the concentration of 1 µM., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

EGFR-targeted inhibitors (gefitinib and erlotinib) provided an effective strategy for the treatment of non-small-cell lung cancer. However, the EGFR T790M secondary mutation has become a leading cause of clinically acquired resistance to these agents. Herein, on the basis of the previously reported irreversible EGFR inhibitor (compound 9), we present a structure-based design approach, which is rationalized via analyzing its binding model and comparing the differences of gatekeeper pocket between the T790M mutant and wild-type (WT) EGFR kinases. Guided by these results, a novel 6,7-dioxo-6,7-dihydropteridine scaffold was discovered and hydrophobic modifications at N5-position were conducted to strengthen nonpolar contacts and improve mutant selectivity over EGFR(WT). Finally, the most representative compound 17d was identified. This work demonstrates the power of structure-based strategy in discovering lead compounds and provides molecular insights into the selectivity of EGFR(L858R/T790M) over EGFR(WT), which may play an important role in designing new classes of mutant-selective EGFR inhibitors.

Starting from the lead isodaphnetin, a natural product inhibitor of DPP-4 discovered through a target fishing docking based approach, a series of novel 2-phenyl-3,4-dihydro-2H-benzo[f]chromen-3-amine derivatives as potent DPP-4 inhibitors are rationally designed utilizing highly efficient 3D molecular similarity based scaffold hopping as well as electrostatic complementary methods. Those ingenious drug design strategies bring us approximate 7400-fold boost in potency. Compounds 22a and 24a are the most potent ones (IC50 ≈ 2.0 nM) with good pharmacokinetic profiles. Compound 22a demonstrated stable pharmacological effect. A 3 mg/kg oral dose provided >80% inhibition of DPP-4 activity within 24 h, which is comparable to the performance of the long-acting control omarigliptin. Moreover, the efficacy of 22a in improving the glucose tolerance is also comparable with omarigliptin. In this study, not only promising DPP-4 inhibitors as long acting antidiabetic that are clinically on demand are identified, but the target fish docking and medicinal chemistry strategies were successfully implemented.

The chemical space is so vast that only a small portion of it has been examined. As a complementary approach to systematically probe the chemical space, virtual combinatorial library design has extended enormous impacts on generating novel and diverse structures for drug discovery. Despite the favorable contributions, high attrition rates in drug development that mainly resulted from lack of efficacy and side effects make it increasingly challenging to discover good chemical starting points. In most cases, focused libraries, which are restricted to particular regions of the chemical space, are deftly exploited to maximize hit rate and improve efficiency at the beginning of the drug discovery and drug development pipeline. This paper presented a valid methodology for fast target-focused combinatorial library design in both reaction-based and production-based ways with the library creating rates of approximately 70,000 molecules per second. Simple, quick and convenient operating procedures are the specific features of the method. SHAFTS, a hybrid 3D similarity calculation software, was embedded to help refine the size of the libraries and improve hit rates. Two target-focused (p38-focused and COX2-focused) libraries were constructed efficiently in this study. This rapid library enumeration method is portable and applicable to any other targets for good chemical starting points identification collaborated with either structure-based or ligand-based virtual screening.

Summary: ChemMapper is an online platform to predict polypharmacology effect and mode of action for small molecules based on 3D similarity computation. ChemMapper collects >350 000 chemical structures with bioactivities and associated target annotations (as well as >3 000 000 non-annotated compounds for virtual screening). Taking the user-provided chemical structure as the query, the top most similar compounds in terms of 3D similarity are returned with associated pharmacology annotations. ChemMapper is designed to provide versatile services in a variety of chemogenomics, drug repurposing, polypharmacology, novel bioactive compounds identification and scaffold hopping studies., Availability: http://lilab.ecust.edu.cn/chemmapper/., Contact: xfliu@ecust.edu.cn or hlli@ecust.edu.cn, Supplementary Information: Supplementary data are available at Bioinformatics online.

Plasmepsin II (PM II) is an attractive target for anti-malaria drug discovery, which involves in host hemoglobin degradation in the acidic food vacuole. In this study, we demonstrated the successful use of structure-based virtual screening to identify inhibitors of PM II from two chemical database. Five novel non-peptide inhibitors were identified and revealed moderate inhibitory potencies with IC50 ranged from 4.62 ± 0.39 to 9.47 ± 0.71 μM. The detailed analysis of binding modes using docking simulations for five inhibitors showed that the inhibitors could be stabilized by forming multiple hydrogen bonds with catalytic residues (Asp 34 and Asp 214) and also with other key residues., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Through a receptor-based and ligand-based combined virtual screening protocol, 21 novel compounds covering 15 scaffolds were identified as novel inhibitors for EGFR-T790M/L858R, among which, 12 of them were identified as selective inhibitors for EGFR-T790M/L858R to wild-type EGFR, and 5 of them exhibited 'dual-effective' to wild-type and mutant EGFR. Meanwhile, their antiproliferative effects toward EGFR high-expressing human lung cancer cell (A549), epidermoid carcinoma cell (A431), and the mutant EGFR-dependent cell (NCI-H1975) were also evaluated., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

P90 ribosomal S6 kinase 2 (RSK2), which was shown to be overexpressed in human cancers, is a serine/threonine kinase and a potential target for cancer treatment. RSK2 comprises two terminal kinase domains (NTKD and CTKD) that can be inhibited by binding with different types of inhibitors at the ATP binding sites. In the absence of a crystal structure of RSK2, we constructed a model for the 3D structure of the RSK2 NTKD by homology modeling and stepwise constrained refinement with the reported inhibitors using a molecular docking method. Structure-based virtual screening was subsequently performed against a library containing commercially available compounds using the refined model. This resulted in the identification of seven novel RSK2 inhibitors with IC₅₀ values ranging from 2.4 to 14.45 μM.

Background: Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields., Results: Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost., Conclusions: By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.

Insulin-like growth factor-1 receptor (IGF-1R) is a growth factor receptor tyrosine kinase acting as a critical mediator of cell proliferation and survival. Novel 5-benzylidenethiazolidine-2,4-dione (5) and 5-(furan-2-ylmethylene)thiazolidine-2,4-dione (6) compounds were identified as potent and selective IGF-1R inhibitors via hierarchical virtual screening. Initial SAR and biological activity of the analogues of 5 and 6 with thiazolidine-2,4-dione template are presented, and several inhibitors with low nanomolar potency are reported.

During the drug discovery and design process, the acid–base dissociation constant (p K a) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of p K a values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular p K a values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge p K a prediction model named GR-p K a (Graph Retention p K a), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular p K a values. The GR-p K a model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-p K a model outperforms several state-of-the-art models in predicting macro-p K a values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R 2 of 0.937 on the SAMPL7 dataset. [ABSTRACT FROM AUTHOR]

The article discusses the impact of artificial intelligence (AI) on drug discovery and development. AI-powered techniques are being used to analyze complex biomedical data and improve the efficiency of the drug discovery process. The article highlights advancements in target-based drug development, capturing the dynamic behavior of molecules, phenotypic drug discovery, automation of drug generation, and boosting drug development from a clinical perspective. While there are challenges in this field, AI-assisted drug development has the potential to improve access to new medicines. The research was supported by funding from the National Key R&D Program of China and the National Natural Science Foundation of China. [Extracted from the article]

Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/. [ABSTRACT FROM AUTHOR]

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target–disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug–target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target–disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19. [ABSTRACT FROM AUTHOR]

Streptococcus agalactiae , as one of the main pathogens of clinical and subclinical mastitis, affects animal welfare and leads to huge economic losses to farms due to the sharp decline in milk yield. However, both the real pathogenic mechanisms of S. agalactiae -induced mastitis and the regulator which controls the inflammation and autophagy are largely unknown. Served as a substrate of ubiquitin-like proteins of E3 ligase, CDK5RAP3 is widely involved in the regulation of multiple signaling pathways. Our findings revealed that CDK5RAP3 was significantly down-regulated in mastitis infected by S. agalactiae. Surprisingly, inflammasome activation was triggered by CDK5RAP3 knockdown: up-regulated NLRP3, IL1β and IL6, and cleaved caspase1 promoting by NF-κB, thereby resulting in pyroptosis. Additionally, the accumulation of autophagy markers (LC3B and p62) after CDK5RAP3 knockdown suggested that the autophagolysosome degradation pathway was inhibited, thereby activating the NF-κB pathway and NLRP3 inflammasome. Hence, our findings suggest that downregulation or ablation of CDK5RAP3 inhibits autophagolysosome degradation, causes inflammation by activating the NF-κB /NLRP3 inflammasome, and triggers cell death. In conclusion, CDK5RAP3 holds the key to understanding the interaction between autophagy and immune responses, its anti-inflammatory role in this study will throw new light on the clinical drug discovery to cure S. agalactiae mastitis. [ABSTRACT FROM AUTHOR]