Search results for "Quantitative structure"
showing 10 items of 192 documents
Biopartitioning micellar separation methods: modelling drug absorption
2003
The search for new pharmacologically active compounds in drug discovery programmes often neglects biopharmaceutical properties as drug absorption. As a result, poor biopharmaceutical characteristics constitute a major reason for the low success rate for candidates in clinical development. Since the cost of drug development is many times larger than the cost of drug discovery, predictive methodologies aiding the selection of bioavailable drug candidates are of profound significance. This paper has been focussed on recent developments and applications of chromatographic systems, particularly those systems based on amphiphilic structures, in the frame of alternative approaches for estimating t…
Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
2006
Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure …
Opioid analgetics retention–pharmacologic activity models using biopartitioning micellar chromatography
2002
Opioids are drugs used in medicine for pain control. In this paper, retention-pharmacokinetics and retention-pharmacodynamics relationships of opioids are proposed and statistically validated. These models are based on the compound retention in the biopartitioning micellar chromatography system (BMC), a new methodology which has successfully been used to develop QRAR models for many other families of compounds. The obtained results are compared to the traditional QSAR models using lipophilicity data. The adequacy of QRAR models is due to the fact that the characteristics of the compounds such as the hydrophobicity, electronic charge and steric effects determine both their retention in BMC a…
Applications of Bond-Based 3D-Chiral Quadratic Indices in QSAR Studies Related to Central Chirality Codification
2009
The concept of bond-based quadratic indices is generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design, we have modeled several well-known data sets. In particularly, Cramer's steroid data set has become a benchmark for the assessment of novel QSAR methods. This data set has been used by several researchers using 3D-QSAR approaches. Therefore, it is selected by us for the shake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design, we model the angiotensin-converting enzyme inhibitory activity o…
Alzheimer: A Decade of Drug Design. Why Molecular Topology can be an Extra Edge?
2017
Background The last decade was characterized by a growing awareness about the severity of dementia in the field of age-related and no age-related diseases and about the importance to invest resources in the research of new, effective treatments. Among the dementias, Alzheimer's plays a substantial role because of its extremely high incidence and fatality. Several pharmacological strategies have been tried but still now, Alzheimer keeps being an untreatable disease. In literature, the number of QSAR related drug design attempts about new treatments for Alzheimer is huge, but only few results can be considered noteworthy. Providing a detailed analysis of the actual situation and reporting the…
A3 adenosine receptor: Homology modeling and 3D-QSAR studies
2012
Adenosine receptors (AR) belong to the superfamily of G-protein-coupled receptors (GPCRs). They are divided into four subtypes (A1, A2A, A2B, and A3) [1], and can be distinguished on the basis of their distinct molecular structures, distinct tissues distribution, and selectivity for adenosine analogs [2,3]. The hA3R, the most recently identified adenosine receptor, is involved in a variety of intracellular signaling pathways and physiological functions [4]. Expression of A3R was reported to be elevated in cancerous tissues [5], and A3 antagonists have been proposed for therapeutic treatments of cancer. The recent literature availability of crystal structure of hA2A adenosine receptor (PDB c…
Artificial neural network applied to the discrimination of antibacterial activity by topological methods
2000
Abstract A new topological method that makes it possible to discriminate the active and inactive molecules on the basis of their chemical structures is applied in the present study to the antibacterial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.
3D-QSAR study of ligands for a human olfactory receptor
2005
Abstract Only about 350 olfactory receptors (OR) provide a basis for the remarkable ability of humans to recognise and discriminate a large number of odorants. A recent study reports the odorant repertoire of a human class II OR called OR1G1, including both agonists and antagonists. We used these affinity data to perform a 3D molecular modelling study of these ligands using Catalyst/HypoGen software (Catalyst v4.9.1, Accelrys Inc., San Diego, 2004) to propose alignment models for OR1G1 ligands. We obtained a triple-alignment model, which satisfactorily explained the experimental activities and was able both to predict the antagonist effects of some compounds and to identify new potent agoni…
TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-bas…
2006
Abstract A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure–activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six us…
<strong>Predicting Proteasome Inhibition using Atomic Weighted Vector and Machine Learning</strong>
2018
Ubiquitin/Proteasome System (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. Through the concerted actions of a series of enzymes, proteins are marked for proteasomal degradation by being linked to the polypeptide co-factor, ubiquitin. The UPS participates in a wide array of biological functions such as antigen presentation, regulation of gene transcription and the cell cycle, and activation of NF-κB. Some researchers have applied QSAR method and machine learning in the study of proteasome inhibition (EC50(µmol/L)), such as: the analysis of proteasome inhibition prediction, in the prediction of multi-target inhibitors of UPP and in the prediction of p…