Computational Fragment-based Discovery of Allosteric Modulators on Metabotropic Glutamate Receptor 5

GPCR allosteric modulators target at the allosteric, “allo- from the Greek meaning "other", binding pockets of G protein-coupled receptors (GPCRs) with indirect influence on the effects of an agonist or inverse agonist. Such modulators exhibit significant advantages compared to the corresponding orthosteric ligands, including better chemical tractability or physicochemical properties, improved selectivity, and reduced risk of over-sensitization towards their receptors. Metabotropic glutamate receptor 5 (mGlu5), a member of GPCRs class C family, is a promising therapeutic target for treating many central nervous system (CNS) diseases. The crystal structure of mGlu5 in the complex with the negative allosteric modulator (NAM) mavoglurant was recently reported, providing a fundamental model for the design of new allosteric modulators. However, new NAM drugs are still in critical need for therapeutic uses. Computational fragment-based drug discovery (FBDD) represents apowerful scaffold-hopping and lead structure-optimization tool for drug design. In the present work, a set of integrated computational methodologies was first used, such as fragment library generation and retrosynthetic combinatorial analysis procedure (RECAP) for novel compound generation. Then, the new compounds generated were assessed by benchmark dataset verification, docking studies, and QS AR model simulation. Subsequently, the structurally diverse compounds, with reported or unreported scaffolds, can be observed from the top 20 in silico design/synthesized compounds, which were predicted to be potential mGlu5 allosteric modulators. The in silico designed compounds with reported scaffolds may fill SAR holes in the known, patented series of mGlu5 modulators . And the generation of compounds without reported activities on mGluR indicates that our approach is doable for exploring and designing novel compounds. Our case study of designing allosteric modulators on mGlu5 demonstrated that the established computational fragment-based approach is a useful methodology for facilitating new compound design and synthesis in the future

Defects engineering simultaneously enhances activity and recyclability of MOFs in selective hydrogenation of biomass

The development of synthetic methodologies towards enhanced performance in biomass conversion is desirable due to the growing energy demand. Here we design two types of Ru impregnated MIL-100-Cr defect engineered metal-organic frameworks (Ru@DEMOFs) by incorporating defective ligands (DLs), aiming at highly efficient catalysts for biomass hydrogenation. Our results show that Ru@DEMOFs simultaneously exhibit boosted recyclability, selectivity and activity with the turnover frequency being about 10 times higher than the reported values of polymer supported Ru towards D-glucose hydrogenation. This work provides in-depth insights into (i) the evolution of various defects in the cationic framework upon DLs incorporation and Ru impregnation, (ii) the special effect of each type of defects on the electron density of Ru nanoparticles and activation of reactants, and (iii) the respective role of defects, confined Ru particles and metal single active sites in the catalytic performance of Ru@DEMOFs for D-glucose selective hydrogenation as well as their synergistic catalytic mechanism

Viral DNA polymerase structures reveal mechanisms of antiviral drug resistance

DNA polymerases are important drug targets, and many structural studies have captured them in distinct conformations. However, a detailed understanding of the impact of polymerase conformational dynamics on drug resistance is lacking. We determined cryoelectron microscopy (cryo-EM) structures of DNA-bound herpes simplex virus polymerase holoenzyme in multiple conformations and interacting with antivirals in clinical use. These structures reveal how the catalytic subunit Pol and the processivity factor UL42 bind DNA to promote processive DNA synthesis. Unexpectedly, in the absence of an incoming nucleotide, we observed Pol in multiple conformations with the closed state sampled by the fingers domain. Drug-bound structures reveal how antivirals may selectively bind enzymes that more readily adopt the closed conformation. Molecular dynamics simulations and the cryo-EM structure of a drug-resistant mutant indicate that some resistance mutations modulate conformational dynamics rather than directly impacting drug binding, thus clarifying mechanisms that drive drug selectivity

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries

Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule generation model (DeepMGM) and applied it for the de novo molecular generation of scaffold-focused small-molecule libraries. In this study, a recurrent neural network (RNN) using long short-term memory (LSTM) units was trained with drug-like molecules to result in a general model (g-DeepMGM). Sampling practices on indole and purine scaffolds illustrate the feasibility of creating scaffold-focused chemical libraries based on machine intelligence. Subsequently, a target-specific model (t-DeepMGM) for cannabinoid receptor 2 (CB2) was constructed following the transfer learning process of known CB2 ligands. Sampling outcomes can present similar properties to the reported active molecules. Finally, a discriminator was trained and attached to the DeepMGM to result in an in silico molecular design-test circle. Medicinal chemistry synthesis and biological validation was performed to further investigate the generation outcome, showing that XIE9137 was identified as a potential allosteric modulator of CB2. This study demonstrates how recent progress in deep learning intelligence can benefit drug discovery, especially in de novo molecular design and chemical library generation

The Research and Development of an Artificial Intelligence Integrated Fragment-Based Drug Design Platform for Small Molecule Drug Discovery

Drug discovery is expensive. The average cost for the development of a new drug now hits $2.6 billion USD and the overall discovery process takes over 12 years to finish. Moreover, these numbers keep increasing. It is critical to think and explore efficient and effective strategies to confront the growing cost and to accelerate the discovery process. The rapid advancement in computational power and the blossom of Artificial Intelligence (AI) brought a promising solution to the field. There is increased availability of both chemical and biological data in drug discovery. The capability of dealing with large data to detect hidden patterns and to facilitate future data prediction in a time-efficient manner favored Machine Learning (ML) algorithms. One step further from the symbolic AI that uses explicit rules to maneuver knowledge, ML allows computers to solve specific tasks by learning on their own. Promising and compelling outcomes including the identification of DDR1 kinase inhibitors within 21 days using deep learning generative models may indicate that we are probably at the corner of an upcoming revolution of drug discovery in the AI era, and the good news is that we are witnessing the change. In this thesis, an AI integrated fragment-based drug design (FBDD) platform is proposed and developed as an innovative solution to the small molecule drug discovery in the big data era. The platform is constituted of three modules, (1) module of deep learning (DL) generative chemistry, (2) module of cheminformatics and computational chemistry, and (3) module of systems pharmacology network study. At module one, DL generative modeling with cutting-edge neural network architectures including a generative adversarial network (GAN) and a recurrent neural network (RNN) can realize the automated de novo molecule generative with target specificity. ML-based decision-making models can also be constructed facilitating large scale virtual screening for early-stage hit identification. At module two, in silico modeling and simulation of cheminformatics is focused to realize both structure-based and ligand-based drug design approaches. Computational chemistry methodologies are extensively integrated to develop an FBDD approach for lead identification and modification. At module three, the concept of systems pharmacology is fused to expedite the network analysis for the given small molecules. The compound-target network, target-pathway network, and target-disease network can be generated and analyzed to contribute a comprehensive understanding of the molecule of interest

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries

Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule generation model (DeepMGM) and applied it for the de novo molecular generation of scaffold-focused small-molecule libraries. In this study, a recurrent neural network (RNN) using long short-term memory (LSTM) units was trained with drug-like molecules to result in a general model (g-DeepMGM). Sampling practices on indole and purine scaffolds illustrate the feasibility of creating scaffold-focused chemical libraries based on machine intelligence. Subsequently, a target-specific model (t-DeepMGM) for cannabinoid receptor 2 (CB2) was constructed following the transfer learning process of known CB2 ligands. Sampling outcomes can present similar properties to the reported active molecules. Finally, a discriminator was trained and attached to the DeepMGM to result in an in silico molecular design-test circle. Medicinal chemistry synthesis and biological validation was performed to further investigate the generation outcome, showing that XIE9137 was identified as a potential allosteric modulator of CB2. This study demonstrates how recent progress in deep learning intelligence can benefit drug discovery, especially in de novo molecular design and chemical library generation

SH-Alb inhibits phenotype remodeling of pro-fibrotic macrophage to attenuate liver fibrosis through SIRT3-SOD2 axis

Albumin has a variety of biological functions, such as immunomodulatory and antioxidant activity, which depends largely on its thiol activity. However, in clinical trials, the treatment of albumin by injection of commercial human serum albumin (HSA) did not achieve the desired results. Here, we constructed reduced modified albumin (SH-Alb) for in vivo and in vitro experiments to investigate the reasons why HSA did not achieve the expected effects. SH-Alb was found to delay the progression of liver fibrosis in mice by alleviating liver inflammation and oxidative stress. Although R-Alb also has some of the above roles, the effect of SH-Alb is more remarkable. Mechanism studies have shown that SH-Alb reduces the release of pro-inflammatory and pro-fibrotic cytokine through the mitogen-activated protein kinase (MAPK) signaling pathway. In addition, SH-Alb deacetylates SOD2, a key enzyme of mitochondrial reactive oxygen species (ROS) production, by promoting the expression of SIRT3, thereby reducing the accumulation of ROS. Finally, macrophages altered by R-Alb or SH-Alb can inhibit the activation of hepatic stellate cells and endothelial cells, further delaying the progression of liver fibrosis. These results indicate that SH-Alb can remodel the phenotype of macrophages, thereby affecting the intrahepatic microenvironment and delaying the process of liver fibrosis. It provides a good foundation for the application of albumin in clinical treatment