Pharmacophore modelling and electronic feature analysis of hydroxamic acid derivatives,the HIV integrase inhibitors

Hydroxamic acid derivatives with metal ion binding properties were collected from the literature to generate a pharmacophore and 3D-QSAR model for HIV strand transfer inhibition. The derived pharmacophore model (AAAHRR) recognizes both metal ion binding site and hydrophobic group. The QSAR model generated using this hypothesis expressed statistical significance (r ² = 0.971 for the training set and q ² = 0.913 for the test set). The ability of this pharmacophore model to retrieve other metal ion binding inhibitors was examined by screening the ChemBank database (ligandinfo) incorporated with 10 known strand transfer inhibitors. The studied favourable and unfavourable contours of chemical features (H-bond donor, acceptor and hydrophobic sites) revealed the role of hydrophobic substitution at the fluorobenzene ring and cyclization of the metal ion binding hydroxamic acid in effective integrase inhibition. Analysis of the frontier orbitals, HOMO and LUMO revealed that the nucleophilic / electrophilic interactions depend on the significant overlapping observed at the azaindole and hydroxamic acid groups. In essence, the generated pharmacophore model is competent enough to disclose the essential site-specific interactions involved in the inhibition of HIV integrase, and hence can be used in virtual screening to identify novel scaffolds as leads with increased anti-viral potency.     

Pharmacophore modelling,virtual screening and molecular docking studies on PLD1 inhibitors

Lipid metabolism plays a significant role in influenza virus replication and subsequent infection. The regulatory mechanism governing lipid metabolism and viral replication is not properly understood to date, but both Phospholipase D (PLD1 and PLD2) activities are stimulated in viral infection. In vitro studies indicate that chemical inhibition of PLD1 delays viral entry and reduction of viral loads. The current study reports a three-dimensional pharmacophore model based on 35 known PLD1 inhibitors. A sub-set of 25 compounds was selected as the training set and the remaining 10 compounds were kept in the test set. One hundred and twelve pharmacophore models were generated; a six-featured pharmacophore model (AADDHR.57) with survival score (2.69) produced a statistically significant three-dimensional quantitative structure–activity relationship model with r² = 0.97 (internal training set), r² = 0.71 (internal test set) and Q² = 0.64. The predictive power of the pharmacophore model was validated with an external test set (r² = 0.73) and a systematic virtual screening work-flow was employed showing an enrichment factor of 23.68 at the top 2% of the dataset (active and decoys). Finally, the model was used for screening of the filtered PubChem database to fetch molecules which can be proposed as potential PLD1 inhibitors for blocking influenza infection.     

Toward resistance‐compromised DHFR inhibitors part 1: Combined structure/ligand‐based virtual screenings and ADME‐Tox profiling

The front‐line antimalarial drugs, for example, chloroquine, mefloquine, sulfadoxine, pyrimethamine, atovaquone, and artemether, are often failing because of the worldwide spread of drug‐resistant parasites. There has been significant recent interest in virtual screening to drive innovative drug discovery and to combat resistance efforts for a wide range of diseases. In fact, virtual screening has become the “gold standard” for major pharmaceutical industries and some university groups. Therefore, we present herein a structure‐based LibDock/CHARMM modeling and a set of appropriate scoring function evaluation criteria: correlation, consensus score, correlation‐based score, generally applicable replacement for root‐mean‐square deviation using a training set of 38 phenylthiazolyl‐1,3,5‐triazines from our previous reports and followed by a ligand‐based model to identify molecular features like hydrogen‐bond acceptor, hydrogen‐bond donor, hydrophobicity, and ring aromatic (RA) using Catalyst HipHop/HypoGen module. Next, TOPKAT module was applied to predict ADME‐Tox properties. The combined structure/ligand‐based approaches inadvertently arrived at a conserved Arg122 binding site from reliable LigScore1_dreiding top scoring function and are subsequently attributed to reserve important interactions and combat mutational drug resistance. The best pharmacophore model suggested that 1 hydrogen‐bond acceptor, 2 hydrophobicities, and 1 ring aromatic feature with good sensitivity at 0.50, specificity at 0.66, enrichment at 1.60, and accuracy at 0.50. Finally, good pharmacokinetics, metabolic stability, and toxicity endpoints were predicted in the comparison of proguanil and cycloguanil. These druggability insights are useful for researchers to deliver more effective, safer, both wild‐type and resistance‐compromised, and more economical dihydrofolate reductase inhibitors in the near future.     

Toxicity remission of PAEs on multireceptors after molecular modification through a 3D-QSAR pharmacophore model coupled with a gray interconnect degree method

In the proposed model, the gray interconnect degree method was employed to process the acute toxicity values of phthalate acid esters (PAEs) to green algae, daphnia, mysid, and fish (predicted by EPI Suite software) and to obtain the comprehensive characterization value of the multireceptor toxicity effect (MTE) of PAEs. The 3D-QSAR pharmacophore model indicated that hydrophobic groups significantly affected the MTE of PAEs. Based on this, 16 PAEs derivative molecules with significantly decreased comprehensive characterization value (more than 10%) of the toxic effects of multireceptors were designed. Among them, 13 PAEs derivative molecules reduced the toxicity values (predicted by the EPI Suite software) of four receptor organisms to varying degrees. Finally, two derivative molecules from PAEs were screened and could exist stably in the environment. The derivative molecule’s reduced toxicity to the receptor was obtained through molecular docking methods and simulated the PAEs’ primary metabolic response pathways. The above research results break through the pharmacophore model’s limitation of only being suitable for the single effect of pollutants. Its application provides a new theoretical verification basis for expanding the multieffect pharmacophore model.     

基于PPARα激动剂的融合药效团模型的构建及评价

Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore (MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha (PPARα) agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.     

基于虚拟筛选和分子动力学模拟发现新型ULK1抑制剂

Unc-51样自噬激活激酶1（unc-51-like autophagy activating kinase 1,ULK1）作为自噬启动的重要调控因子,是肿瘤治疗的关键靶点之一。首先,以已知ULK1抑制剂为基础构建药效团模型,通过药效团模型筛选、分子对接以及分子力学广义波恩表面积（Molecular Mechanics/Generalized Born Surface Area,MM/GBSA）结合自由能计算等方法,对含有52万多个类药性小分子的数据库进行虚拟筛选,得到具有较高理论亲和力的化合物。随后,50ns的分子动力学模拟验证了蛋白质-配体复合物结合的稳定性,最后10ns的平均结合自由能的计算研究进一步验证了配体的结合能力。结果表明,6个化合物（F5258-0159、F3407-0428、F0529-1100、F0696-3531、F3222-5280、F6525-5596）具有骨架新颖、分子对接分数和结合自由能数值优异及与ULK1的结合状态稳定等特点,可以作为新型潜在的ULK1抑制剂用于肿瘤治疗的研究,也为新型ULK1抑制剂的设计和研发提供新的研究思路。     

基于药效团模型从中草药数据库中搜索gpIIb/IIIa受体抑制剂

选择20 个3,4-二氢-1(1H)-异喹啉酮类gpIIb/IIIa受体抑制剂作为训练集, 利用Catalyst软件包建立了gpIIb/IIIa受体抑制剂三维药效团模型. 探讨了药效团作用模式. 并通过建立的可靠性最佳的药效团模型(线性回归系数r=0.7715), 从中草药数据库中虚拟筛选了gpIIb/IIIa受体抑制剂, 通过实验活性测定得到了8个抑制ADP活化全血血小板聚集的IC50从40到100 μmol·L-1的化合物, 进一步证明了所建药效团模型的有效性.