The Evaluation Of Molecular Similarity And Molecular Diversity Methods Using Biological Activity Data

This paper reviews the techniques available for quantifying the effectiveness of methods for molecule similarity and molecular diversity, focusing in particular on similarity searching and on compound selection procedures. The evaluation criteria considered are based on biological activity data, both qualitative and quantitative, with rather different criteria needing to be used depending on the type of data available

Theoretical and experimental studies of corrosion inhibition of thiohene-2-ethylamine on mild steel in acid media

Corrosion inhibition of mild steel in 0.5M H2SO4 at 30°C with thiophene-2- ethylamine (TEA) as inhibitor has been assess by quantitative structure activity relation (QSAR) model and quantum chemical calculations. The results were evaluated using weight loss and electrochemical methods such as potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS). The results showed good performance of TEA in corrosion protection which behaves as mixed inhibitor from PDP. The micrograph from FESEM and EDX dot mapping showed that the inhibitor adsorbed onto the metal surface with different distribution for S, C and N atoms which indicate less damage on the metal surface in the presence of TEA

Identification of levothyroxine antichagasic activity through computer-aided drug repurposing

Cruzipain (Cz) is the major cysteine protease of the protozoan Trypanosoma cruzi, etiological agent of Chagas disease. A conformation-independent classifier capable of identifying Cz inhibitors was derived from a 163-compound dataset and later applied in a virtual screening campaign on the DrugBank database, which compiles FDA-approved and investigational drugs. 54 approved drugs were selected as candidates, 3 of which were acquired and tested on Cz and T. cruzi epimastigotes proliferation. Among them, levothyroxine, traditionally used in hormone replacement therapy in patients with hypothyroidism, showed dose-dependent inhibition of Cz and antiproliferative activity on the parasite.Facultad de Ciencias Exacta

Molecular Modeling on Structure-Function Analysis of Human Progesterone Receptor Modulators

Considering the significance of progesterone receptor (PR) modulators, the present study is explored to envisage the biophoric signals for binding to selective PR subtype-A using ligand-based quantitative structure activity relationship (QSAR) and pharmacophore space modeling studies on nonsteroidal substituted quinoline and cyclocymopol monomethyl ether derivatives. Consensus QSAR models (Training set (Tr): nTr=100, R2pred=0.702; test set (Ts): nTs=30, R2pred=0.705, R2m=0.635; validation set (Vs): nVs=40, R2pred=0.715, R2m=0.680) suggest that molecular topology, atomic polarizability and electronegativity, atomic mass and van der Waals volume of the ligands have influence on the presence of functional atoms (F, Cl, N and O) and consequently contribute significant relations on ligand binding affinity. Receptor independent space modeling study (Tr: nTr=26, Q2=0.927; Ts: nTs=60, R2pred=0.613, R2m=0.545; Vs: nVs=84, R2pred=0.611, R2m=0.507) indicates the importance of aromatic ring, hydrogen bond donor, molecular hydrophobicity and steric influence for receptor binding. The structure-function characterization is adjudged with the receptor-based docking study, explaining the significance of the mapped molecular attributes for ligand-receptor interaction in the catalytic cleft of PR-A

Cheminformatics Modeling of Diverse and Disparate Biological Data and the Use of Models to Discover Novel Bioactive Molecules

Ligand-based drug design is a popular and efficient computational approach to facilitate the drug discovery process. Current approaches mainly focus on optimizing the computational algorithms to improve the efficiency or accuracy of virtual screening; however, the success of ligand-based drug design relies not only on the effectiveness and robustness of the underlying algorithms, but much more importantly, on the quality of the data for model building. Although numerous chemical probe databases have emerged recently, few evaluation of data quality and reliability have been performed. Building upon our lab's experience in Quantitative Structure-Activity Relationship (QSAR) method and methods developed in the field of cheminformatics, this dissertation focuses on: 1) Investigation and comparison of the predictive power of QSAR methods with other ligand-based drug discovery approaches, such as Similarity Ensemble Approach (SEA) and Prediction of Activity Spectra for Substances (PASS); 2) Using QSAR methods to validate the consistency and reliability of biomedical data in disparate data sources. 3) Developing a novel, rigorous and dataset-specific QSAR workflow for the application on multiple therapeutic targets in order to identify diverse hits with high potency in practical virtual screening projects. These works succeed in thoroughly investigating the current approaches for ligand-based drug discovery, exploring the consistency and quality of major annotated cheminformatics databases, and identifying many pharmaceutically important ligands. The success of our studies harshly challenges some popular multi-target profile prediction methods and contributes to the development of cheminformatics by emphasizing the importance of determining trustworthy data sources.Doctor of Philosoph

Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity

BackgroundTo develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem.ObjectivesWe have explored these data in terms of their utility for predicting adverse health effects of the environmental agents.Methods and resultsInitially, the classification k nearest neighbor (kNN) quantitative structure–activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP–HTS studies. We found that compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors.ConclusionsOur studies suggest that combining NTP–HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology