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Leming Shi,
Zhenqiang Su,
Aihua Xie,
Chenzhong Liao,
Wei Qiao,
Dajie Zhang,
Song Shan,
Desi Pan,
Zibin Li,
Zhiqiang Ning,
Weiming Hu,
Xianping Lu
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ABSTRACT: Chipscreen Biosciences, Ltd. (www.chipscreen.com) is a drug discovery company specialized in novel small molecule therapeutics.
Chipscreen has developed a proprietary chemical genomics approach to accelerate the discovery of new medicines from its collection
of natural products, traditional Chinese medicines, and synthetic chemical libraries. Central to its drug discovery platform
is Chipscreen’s capability of integrating in silico drug design, chemical synthesis, unique parallel multi-target high throughput screening, global gene expression profiling,
and informatics to rapidly and effectively advance the drug discovery process. To fulfill Chipscreen’s drug discovery needs,
we have developed an integrated biochemoinformatics system to efficiently manage and mine various types of experimental data,
including chemical structure information, biological activity fingerprints, and gene expression profiling patterns. Well-informed
decision on which drug candidates should be advanced into preclinical and clinical development can be made by maximizing the
utilities of experimental data stored in the database, thereby lowering the risk and increasing the success rate of the drug
discovery and development process.
12/2005: pages 191-206;
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ABSTRACT: Based on the structural characters of PPAR modulators, a virtual combinatorial library containing 1226,625 compounds was constructed using SMILES strings. Selected ADME filters were employed to compel compounds having poor drug-like properties from this library. This library was converted to sdf and mol2 files by CONCORD 4.0, and was then docked to PPARgamma by DOCK 4.0 to identify new chemical entities that may be potential drug leads against type 2 diabetes and other metabolic diseases. The method to construct virtual combinatorial library using SMILES strings was further visualized by Visual Basic.net that can facilitate the needs of generating other type virtual combinatorial libraries.
European Journal of Medicinal Chemistry 08/2005; 40(7):632-40. · 3.35 Impact Factor
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Zhibin Li,
Chenzhong Liao,
Ben C B Ko,
Song Shan,
Edith H Y Tong,
Zihui Yin,
Desi Pan,
Vincent K W Wong, Leming Shi,
Zhi-Qiang Ning,
Weiming Hu,
Jiaju Zhou,
Stephen S M Chung,
Xian-Ping Lu
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ABSTRACT: Lipid accumulation in nonadipose tissues is increasingly linked to the development of type 2 diabetes in obese individuals. We report here the design, synthesis, and evaluation of a series of novel PPARalpha selective activators containing 1,3-dicarbonyl moieties. Structure-activity relationship studies led to the identification of PPARalpha selective activators (compounds 10, 14, 17, 18, and 21) with stronger potency and efficacy to activate PPARalpha over PPARgamma and PPARdelta. Experiments in vivo showed that compounds 10, 14, and 17 had blood glucose lowering effect in diabetic db/db mouse model after two weeks oral dosing. The data strongly support further testing of these lead compounds in other relevant disease animal models to evaluate their potential therapeutic benefits.
Bioorganic & Medicinal Chemistry Letters 08/2004; 14(13):3507-11. · 2.55 Impact Factor
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ABSTRACT: The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma.
Journal of Molecular Modeling 07/2004; 10(3):165-77. · 1.80 Impact Factor
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ABSTRACT: Histone deacetylases (HDACs) play a critical role in gene transcription and have become a novel target for the discovery of drugs against cancer and other diseases. During the past several years there have been extensive efforts in the identification and optimization of histone deacetylase inhibitors (HDACIs) as novel anticancer drugs. Here we report a comprehensive quantitative structure-activity relationship (QSAR) study of HDACIs in the hope of identifying the structural determinants for anticancer activity. We have identified, collected, and verified the structural and biological activity data for 124 compounds from various literature sources and performed an extensive QSAR study on this comprehensive data set by using various QSAR and classification methods. A highly predictive QSAR model with R(2) of 0.76 and leave-one-out cross-validated R(2) of 0.73 was obtained. The overall rate of cross-validated correct prediction of the classification model is around 92%. The QSAR and classification models provided direct guidance to our internal programs of identifying and optimizing HDAC inhibitors. Limitations of the models were also discussed.
Current Medicinal Chemistry - Anti-Cancer Agents 06/2004; 4(3):273-99.
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ABSTRACT: Eigenvalue analysis (EVA) was conducted on a series of potent agonists of peroxisome proliferator-activated receptor gamma (PPARgamma). Predictive EVA quantitative structure-activity relationship (QSAR) models were established using the SYBYL package, which had conventional r2 and cross-validated coefficient (q2) values up to 0.920 and 0.587 for the AM1 method and 0.863 and 0.586 for the PM3 method, respectively. These models were validated by a test set containing 18 compounds. The capability to predict by these two models for PPARgamma agonists, with the best predictive r2pred value of 0.614 for AM1 and 0.822 for PM3 methods, set a successful example for applying a similar approach in building QSAR models for PPARalpha and -delta that could potentially offer a new opportunity in the design of novel PPAR modulators.
Journal of Chemical Information and Computer Sciences 44(1):230-8.
