MolCode Ltd.

Tartu, Estonia

MolCode Ltd.

Tartu, Estonia
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Karelson M.,University of Tartu | Karelson M.,Tallinn University of Technology | Dobchev D.,Tallinn University of Technology | Dobchev D.,MolCode Ltd
Expert Opinion on Drug Discovery | Year: 2011

Introduction: Membrane-cell penetration is a key property for drug candidates, particularly those related to CNS and gastrointestinal diseases. The ability to know whether a drug or compound has the ability to perform this complex characteristic in advance would save time and money for pharmaceutical companies. One robust and fast solution is to use artificial neural networks (ANNs) to predict the cell penetration of the compound candidates. Areas covered: The authors review the application of ANN methods for ANN modeling in the discovery of cell-penetrating drugs. The article looks at three main systems including the BBB, gastrointestinal absorption and permeation in addition to discussing a new approach for cell-penetrating peptide discovery. This review provides the reader with an overview of the ANN methods and applications for the broader audience interested in prediction of cell penetration of drugs. Expert opinion: ANNs can be successfully applied to the prediction of cell-penetrating drugs. Researchers have a broad field of applications for the use of quantitative structureactivity relationship neural networks in drug discovery and development, and can use these areas to further investigate this important pharmaceutical topic. © 2011 Informa UK, Ltd.


Sidorova Y.A.,University of Helsinki | Bespalov M.M.,University of Helsinki | Wong A.W.,University of Melbourne | Kambur O.,University of Helsinki | And 10 more authors.
Frontiers in Pharmacology | Year: 2017

Neuropathic pain caused by nerve damage is a common and severe class of chronic pain. Disease-modifying clinical therapies are needed as current treatments typically provide only symptomatic relief; show varying clinical efficacy; and most have significant adverse effects. One approach is targeting either neurotrophic factors or their receptors that normalize sensory neuron function and stimulate regeneration after nerve damage. Two candidate targets are glial cell line-derived neurotrophic factor (GDNF) and artemin (ARTN), as these GDNF family ligands (GFLs) show efficacy in animal models of neuropathic pain (Boucher et al., 2000; Gardell et al., 2003; Wang et al., 2008, 2014). As these protein ligands have poor drug-like properties and are expensive to produce for clinical use, we screened 18,400 drug-like compounds to develop small molecules that act similarly to GFLs (GDNF mimetics). This screening identified BT13 as a compound that selectively targeted GFL receptor RET to activate downstream signaling cascades. BT13 was similar to NGF and ARTN in selectively promoting neurite outgrowth from the peptidergic class of adult sensory neurons in culture, but was opposite to ARTN in causing neurite elongation without affecting initiation. When administered after spinal nerve ligation in a rat model of neuropathic pain, 20 and 25 mg/kg of BT13 decreased mechanical hypersensitivity and normalized expression of sensory neuron markers in dorsal root ganglia. In control rats, BT13 had no effect on baseline mechanical or thermal sensitivity, motor coordination, or weight gain. Thus, small molecule BT13 selectively activates RET and offers opportunities for developing novel disease-modifying medications to treat neuropathic pain. © 2017 Sidorova, Bespalov, Wong, Kambur, Jokinen, Lilius, Suleymanova, Karelson, Rauhala, Karelson, Osborne, Keast, Kalso and Saarma.


Katritzky A.R.,University of Florida | Kuanar M.,University of Florida | Slavov S.,University of Florida | Hall C.D.,University of Florida | And 4 more authors.
Chemical Reviews | Year: 2010

A study was conducted to demonstrate quantitative correlation of physical and chemical properties with chemical structure. It was demonstrated that the establishment of quantitative correlations between diverse molecular properties and chemical structure was essential to assess and improve environmental, medicinal, and technological aspects of life. These were expressed as quantitative structure-property relationships (QSPR) that related physical, chemical, or physicochemical properties of compounds to their structures. A significant objective of the QSPR studies was to find a mathematical relationship between the property of interest and one or more descriptive parameters derived from the structure of the molecule. The descriptors used in the study included experimental properties or properties derived from readily available experimental characteristics of the structure or computed based on the structure.


Katritzky A.R.,University of Florida | Kasemets K.,University of Florida | Kasemets K.,MolCode Ltd. | Kasemets K.,University of Tartu | And 8 more authors.
Water Research | Year: 2010

The experimental logEC50 toxicity values of 104 compounds causing bioluminescent repression of the bacterium strain Pseudomonas isolated from an industrial wastewater were studied. Using the Best Multilinear Regression method implemented in CODESSA PRO, models with up to 8 theoretical descriptors were obtained. Utilizing a rigorous descriptor selection and validation procedure a reliable QSAR model with four parameters was selected as best. The proposed model emphasizes the importance of the halogen atoms presented in each compound, the possibility of H-bond formation and the flexibility and degree of branching of the molecules. As pointed out by many researchers, the contribution of the octanol-water partition coefficient to the explanation of the toxicity effect was also found to be significant. In addition, the model currently proposed was compared to those reported earlier and its advantages were discussed in detail. © 2010 Elsevier Ltd.


Katritzky A.R.,University of Florida | Radzvilovits M.,University of Florida | Radzvilovits M.,MolCode Ltd. | Radzvilovits M.,University of Tartu | And 8 more authors.
Toxicological and Environmental Chemistry | Year: 2010

The bioconcentration factors (BCFs) of 57 polychlorinated biphenyl (PCB) congeners were modeled by quantitative structure-activity relationship (QSAR) based on 486 constitutional, topological, geometrical, electro- static, quantum chemical, and thermodynamic descriptors derived solely from molecular structure and calculated using CODESSA Pro (comprehensive descriptors for structural and statistical analysis) software. Descriptors utilized for the general model were selected by various statistical validation techniques. Multilinear models were developed using the best multilinear regression algorithm to relate experimental BCF to a set of molecular descriptors. The proposed two-parameter model satisfactorily describes the relationship between observed and calculated values in terms of statistical parameters. Polarity, structural flexibility, and spatial mass distribution of a molecule were demonstrated to be the main factors influencing the ability of PCB to (1) penetrate through lipophilic cell membranes and (2) bind specifically and nonspecifically to biological targets, hence affecting BCF. Comparison to other models indicated advantages of the proposed model over previously reported ones. Derived two-parameter regression equation has improved statistics and is based on theoretical descriptors with a definite physicochemical meaning; it is easier to use and interpret due to the mathematical simplicity of the linear QSAR approach. Internal validation and scrambling procedure confirmed the stability and reliable predictive ability of the general model and indicated the absence of chance correlations. External validation demonstrated that the presented model can be applied to structurally similar sets of compounds, thus extending the domain of applicability of the model. © 2010 Taylor & Francis.


Dobchev D.A.,Tallinn University of Technology | Dobchev D.A.,MolCode Ltd. | Mager I.,University of Tartu | Mager I.,University of Stockholm | And 11 more authors.
Current Computer-Aided Drug Design | Year: 2010

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features. © 2010 Bentham Science Publishers Ltd.


PubMed | MolCode Ltd.
Type: Journal Article | Journal: Current computer-aided drug design | Year: 2012

A novel computational technology based on fragmentation of the chemical compounds has been used for the fast and efficient prediction of activities of prospective protease inhibitors of the hepatitis C virus. This study spans over a discovery cycle from the theoretical prediction of new HCV NS3 protease inhibitors to the first cytotoxicity experimental tests of the best candidates. The measured cytotoxicity of the compounds indicated that at least two candidates would be suitable further development of drugs.

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