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Jing Xing

Bio: Jing Xing is an academic researcher from Michigan State University. The author has contributed to research in topics: Virtual screening & Medicine. The author has an hindex of 13, co-authored 28 publications receiving 480 citations. Previous affiliations of Jing Xing include East China University of Science and Technology & Chinese Academy of Sciences.

Papers
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Journal ArticleDOI
TL;DR: The development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models are introduced.
Abstract: In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

198 citations

Journal ArticleDOI
TL;DR: Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.
Abstract: Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.

109 citations

Journal ArticleDOI
TL;DR: The discovery of novel, highly selective, potent small molecule inhibitors of p300/CBP histone acetyltransferases (HAT) with desired drug-like properties are reported, exemplified by B026, which achieves significant and dose-dependent tumor growth inhibition in an animal model of human cancer.
Abstract: p300 and CREB-binding protein (CBP) are ubiquitously expressed pleiotropic lysine acetyltransferases and play a key role as transcriptional co-activators that are essential for a multitude of cellular processes. Despite great importance, there is a lack of highly selective, potent, druglike p300/CBP inhibitors. Through the artificial-intelligence-assisted drug discovery pipeline and further optimization, we reported the discovery of novel, highly selective, potent small-molecule inhibitors of p300/CBP histone acetyltransferases (HAT) with desired druglike properties, exemplified by B026. Our data demonstrated that B026, with half maximal inhibitory concentration (IC50) values of 1.8 nM to p300 and 9.5 nM to CBP enzyme inhibitory activity, is the most potent, selective p300/CBP HAT inhibitor. Moreover, B026 achieves significant and dose-dependent tumor growth inhibition in an animal model of human cancer, suggesting that B026 is a highly promising p300/CBP HAT inhibitor and warrants extensive preclinical investigation as a potential clinical development candidate.

79 citations

Journal ArticleDOI
23 Sep 2020-Nature
TL;DR: It is concluded that pharmacological targeting of TRADD may represent a promising strategy for inhibiting cell death and restoring homeostasis to treat human diseases.
Abstract: Cell death in human diseases is often a consequence of disrupted cellular homeostasis. If cell death is prevented without restoring cellular homeostasis, it may lead to a persistent dysfunctional and pathological state. Although mechanisms of cell death have been thoroughly investigated1-3, it remains unclear how homeostasis can be restored after inhibition of cell death. Here we identify TRADD4-6, an adaptor protein, as a direct regulator of both cellular homeostasis and apoptosis. TRADD modulates cellular homeostasis by inhibiting K63-linked ubiquitination of beclin 1 mediated by TRAF2, cIAP1 and cIAP2, thereby reducing autophagy. TRADD deficiency inhibits RIPK1-dependent extrinsic apoptosis and proteasomal stress-induced intrinsic apoptosis. We also show that the small molecules ICCB-19 and Apt-1 bind to a pocket on the N-terminal TRAF2-binding domain of TRADD (TRADD-N), which interacts with the C-terminal domain (TRADD-C) and TRAF2 to modulate the ubiquitination of RIPK1 and beclin 1. Inhibition of TRADD by ICCB-19 or Apt-1 blocks apoptosis and restores cellular homeostasis by activating autophagy in cells with accumulated mutant tau, α-synuclein, or huntingtin. Treatment with Apt-1 restored proteostasis and inhibited cell death in a mouse model of proteinopathy induced by mutant tau(P301S). We conclude that pharmacological targeting of TRADD may represent a promising strategy for inhibiting cell death and restoring homeostasis to treat human diseases.

43 citations

Journal ArticleDOI
TL;DR: A novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is aBRD4i based on its binding pattern with BRD4 is demonstrated.
Abstract: Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4–ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD...

38 citations


Cited by
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Journal ArticleDOI
TL;DR: ProTox-II is presented, a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II.
Abstract: Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

942 citations

Journal ArticleDOI
TL;DR: A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.
Abstract: In recent years, tools for the development of new drugs have been dramatically improved. These include genomic and proteomic research, numerous biophysical methods, combinatorial chemistry and screening technologies. In addition, early ADMET studies are employed in order to significantly reduce the failure rate in the development of drug candidates. As a consequence, the lead finding, lead optimization and development process has gained marked enhancement in speed and efficiency. In parallel to this development, major pharma companies are increasingly outsourcing many components of drug discovery research to biotech companies. All these measures are designed to address the need for a faster time to market. New screening methodologies have contributed significantly to the efficiency of the drug discovery process. The conventional screening of single compounds or compound libraries has been dramatically accelerated by high throughput screening methods. In addition, in silico screening methods allow the evaluation of virtual compounds. A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.

803 citations

Journal ArticleDOI
TL;DR: This update of admetSAR, developed as a comprehensive source and free tool for the prediction of chemical ADMET properties, focuses on extension and optimization of existing models with significant quantity and quality improvement on training data.
Abstract: Summary admetSAR was developed as a comprehensive source and free tool for the prediction of chemical ADMET properties. Since its first release in 2012 containing 27 predictive models, admetSAR has been widely used in chemical and pharmaceutical fields. This update, admetSAR 2.0, focuses on extension and optimization of existing models with significant quantity and quality improvement on training data. Now 47 models are available for either drug discovery or environmental risk assessment. In addition, we added a new module named ADMETopt for lead optimization based on predicted ADMET properties. Availability and implementation Free available on the web at http://lmmd.ecust.edu.cn/admetsar2/. Supplementary information Supplementary data are available at Bioinformatics online.

606 citations

Journal ArticleDOI
Xin Yang1, Yifei Wang1, Ryan Byrne2, Gisbert Schneider2, Shengyong Yang1 
TL;DR: The current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects.
Abstract: Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

425 citations

Journal ArticleDOI
TL;DR: This web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures.
Abstract: Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .

378 citations