Index of ideality of correlation and correlation contradiction index: a confluent perusal on acetylcholinesterase inhibitors (original) (raw)
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A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against ea...
Sigma-2 receptor ligands QSAR model dataset
Data in Brief
The data have been obtained from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) and refined according to the QSAR requirements. These data provide information about a set of 548 Sigma-2 (σ 2) receptor ligands selective over Sigma-1 (σ 1) receptor. The development of the QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). Data here reported include the regression for σ 2 receptor pK i QSAR models. The QSAR model was also employed to predict the σ 2 receptor pK i values of the FDA approved drugs that are herewith included.
Journal of chemical information and modeling, 2017
Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drug's potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drug's Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. This MOA-DILI approach was examined using a data set of 333 drugs. The drugs were first grouped according to their MOA profiles (positive or negative in each MOA) based on the Tox21 qHTS assays. QSAR models for individual MOA assays were developed and subsequently combined to obtain the MOA-DILI model. A hold-out testing strategy (222 drugs for training and 111 drugs as a test set) was employed, which yielded a predictive accuracy of 0.711. The MOA-DILI model was directly compared with t...
Chemosphere, 2006
The performance of decision tree (DT), learning vector quantization (LVQ), and k-nearest neighbour (kNN) methods classifying active and inactive estrogenic compounds in terms of their structure activity relationship (SAR) was evaluated. A set of 311 compounds was used for construction of the models, the predictive power of which was verified with separate training and test sets. Principal components derived from molecular descriptors calculated with DRA-GON software were used as variables representing the structures of the compounds. Broadly, kNN had the best classification ability and DT the weakest, although the performance of each method was dependent on the group of compounds used for modelling. The best performance was obtained with kNN for the calf estrogen receptor data, averaging 98.3% of correctly classified compounds in the external tests. Overall, the results indicate that all the methods tested are suitable for the SAR classification of estrogenic compounds, producing models with a predictive power ranging from adequate to excellent.
Expanding the Armory: Predicting and Tuning Covalent Warhead Reactivity
Journal of chemical information and modeling, 2017
Targeted covalent inhibition is an established approach for increasing the potency and selectivity of potential drug candidates, as well as identifying potent and selective tool compounds for target validation studies. It is evident that identification of reversible recognition elements is essential for selective covalent inhibition, but this must also be achieved with the appropriate level of inherent reactivity of the reactive functionality (or "warhead"). Structural changes that increase or decrease warhead reactivity, guided by methods to predict the effect of those changes, have the potential to tune warhead reactivity and negate issues related to potency and/or toxicity. The half-life to adduct formation with glutathione (GSH t) is a useful assay for measuring the reactivity of cysteine-targeting covalent warheads but is limited to synthesized molecules. In this manuscript we assess the ability of several experimental and computational approaches to predict GSH tfor ...