• Slide 1

    Drug Design and Bioinformatics Lab

    Faculty of Pharmacy
    Medical University of Sofia
    Bulgaria

    Research
  • Slide 2

    Computer-aided drug discovery

    development of therapeutically important small molecules
    for over two decades

    Training

QSAR

Quantitative Structure - Activity Relationships relate changes in the chemical structures of the studied compounds to changes in their activities. The chemical structures are described by a wide range of molecular descriptors and relationships with the activities are identified using statistical and machine-learning methods.

QSPkR

Quantitative Structure - Pharmacokinetics Relationships model the main pharmacokinetic parameters like volume of distribution, half-life, clearance and protein binding. The chemical structures of acidic, basic or neutral molecules are described by 1D, 2D and 3D descriptors selected by genetic algorithm. The models are derived by multiple linear regression and validated by external test sets.

Proteochemometrics

Proteochemometrics is a QSAR approach designed to deal with sets of ligands binding to sets of related proteins. Both ligands and target proteins are described by appropriate molecular descriptors and enter the X matrix of QSAR. The proteochemometric QSAR model not only delineates the chemical features of the ligands responsible for their activities but also yields a detailed information about the interactions between ligands and proteins.

Immunoinformatics

Immunoinformatics is part of the bioinformatics and applies the methods of bioinformatics in the field of immunology. The main subject of immunoinformatics is T-cell and B-cell epitope prediction. Epitope is this part of the foreign protein that is recognized and interacts with the host proteins. The prediction of epitopes is the first step of the process of epitope-based vaccine development. As more precise is the epitope prediction as more efficient and less expensive is the subsequent experimental work.

Molecular docking

Molecular docking is a method of structure-based drug design that models in silico the interactions between ligands and proteins. Ligands are docked into the binding site of proteins in different poses and the resulting energies of interaction are assessed by scoring functions. The lowest energy ligand – protein complex gets the highest score and is ranked first in the list. Molecular docking techniques are the in silico alternative of the expensive X-ray studies.

Molecular dynamics

Molecular dynamics simulations mimic the motions of molecules and the interactions between them in the 3D space over a period of time. MD produces information at a microscopic level described by a molecular mechanics force field. The MD application in drug design is focused mainly on ligand-macromolecule interactions.

Building professionals

Creative Education & Talented Students

Training

Drug Design

Fifth year students and PhD students, winter term

The course gives basic knowledge on drug design approaches and methods. Lectures are based on the Molecular Conceptor learning series. Practicals are focused on molecular modeling and QSAR methods

Pharmacokinetics

Fifth year students, winter term

The course explores the kinetics aspects of the processes of absorption, distribution, metabolism and excretion (ADME) of drugs. It includes the following main topics: compartment and non-compartment analyses, linear and non-linear pharmacokinetics, ADME processes, dosage regimens and pharmacokinetic monitoring of drugs.

Physical Chemistry

Third year students, spring and winter terms

The physical chemistry is one of the fundamental pharmaceutical sciences. It explains the macroscopic phenomena in pharmacy. The course is focused on thermodynamics of drug – receptor interactions, physicochemical properties of drugs, interfacial phenomena and colloids, solutions of electrolytes and non-electrolytes, chemical kinetics, drug interactions.

Computational Chemistry

PhD students

PhD positions are opened in September every year. For non-EU students the tuition fee is 8,000 euro/year. For more details, please contact Prof. Doytchinova at: idoytchinova@pharmfac.mu-sofia.bg.

Meet the team

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Professor

Irini Doytchinova

MPharm, PhD, DSci.

Irini Doytchinova received her MPharm and PhD degrees from the Medical University of Sofia, Bulgariа. Between 2000 and 2006 she was a Research Scientist in the Edward Jenner Institute for Vaccine Research, UK. In 2007 she founded the Lab of Drug Design and Bionformatics in the School of Pharmacy at the Medical University of Sofia.

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Associate Professor

Zvetanka Zhivkova

ChemEngineer, PhD

Zvetanka Zhivkova has a MSc in Chemical Engineering from the University of Chemical Technology and Metallurgy and PhD degree in Pharmacy from MU-Sofia. She is a lecturer in Physical Chemistry and Pharmacokinetics in the School of Pharmacy, MU-Sofia. Her research interests are in the field of drug metabolism and pharmacokinetics, and QSPkR.

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Associate Professor

Ivan Dimitrov

ChemEngineer, PhD

Ivan Dimitrov has a Master of Science in Chemical Engineering and PhD degree in Physical Chemistry from the University of Chemical Technology and Metallurgy, Sofia, Bulgaria. He applies informatics and machine learning methods to develop models for immunogenicity and allergenicity prediction, and implement them to web servers.

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Head Assistant Professor

Iva Valkova

MPharm, PhD

Iva Valkova received her MPharm and PhD degrees from the Medical University of Sofia. Her research interests are in the field of drug design. She applies biophysical techniques (ITC, PAMPA) for hit identification and lead optimization.

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Head Assistant Professor

Mariyana Atanasova

MChem, PhD

Mariyana Atanasova received MChem and PhD from the University of Sofia, Bulgaria. In 2008 she joined the group of Prof. Irini Doytchinova as Assistant Professor. She applies molecular docking and MD simulations to study ligand-protein interactions.

Postdoc

Stafan Ivanov

MPharm, PhD

PhD Student

Nevena Zaharieva

MPharm

PhD Student

Atanas Lukarski

MPharm

ALUMNI PhD STUDENTS:

Atanas Patronov

Panayot Garnev

Iva Valkova

Nikola Yordanov

Vensislav Yordanov

Services

The databases and models developed in the Lab are freely available in the Web.

Databases

  • AntiJen

    Database containing quantitative binding data for peptides binding to MHC ligand, TCR-MHC complexes, T-cell epitopes, TAP, B-cell epitopes and immunological protein-protein interactions.
  • PPD

    Protein pKa database.
  • DSD

    Database of dehydrogenase stereospecificities.

Servers

  • AllergenFP

  • Bioinformatics tool for allergenicity prediction based on a novel descriptor fingerprint approach.
  • AllerTOP v.1

  • Bioinformatics tool for allergenicity prediction.
  • AllerTOP v.2

  • Bioinformatics tool for allergenicity prediction.
  • AllerScreener

    Bioinformatics tools for allergen prediction.
  • EpiTOP v.3

    Proteochemometrics-based tools for MHC class II binding prediction.
  • EpiDOCK

    Docking-based tool for MHC class II binding prediction.
  • EpiJen

    Multi-step algorithm for MHC class I binding prediction, icluding proteasome cleavage and TAP-binding predictoins.
  • MHCPred

    Additive method for MHC class I and class II binding prediction.
  • VaxiJen

    Server for prediction of protective antigens of viral, bacterial, tumour, parasite and fungal origin.

Current Projects

IT-based design of novel anti-Alzheimer drugs.

Development of web application for allergenicity prediction.

Upgrading of web application for HLA binding prediction.

Centre of Excellence on Informatics and Information and Communication Technologies, Bulgarian Ministry of Education and Science, 2018 - 2023

Results are published in:
Yordanov V, Dimitrov I, Doytchinova I. Proteochemometrics-based prediction of peptide binding to HLA-DP proteins.
J. Chem. Inf. Model., 58, 297-304, 2018.

Update of web application for immunogenicity prediction.

ICT in Science, Education and Security, Bulgarian Ministry of Education and Science, 2019 - 2020

MDML – a combined molecular dynamics/machine learning approach to drug design.

National Roadmap for Scientific Infrastructure, Government of Bulgaria, 2017 - 2023

Novel acetylcholinesterase inhibitors for treatment of Alzheimer's disease.

GRANT: DN 03/9/2016 Bulgarian Science Fund

Stage 1: 2017-2018

Stage 2: 2019-2020

Results are published in:
Doytchinova I, Atanasova M, Valkova I, Stavrakov G, Philipova I, Zhivkova Z, Zheleva-Dimitrova D, Konstantinov S, Dimitrov I. Novel hits for acetylcholinesterase inhibition derived by docking-based screening on ZINC database.
J. Enz. Inh. Med. Chem., 33, 768-776, 2018.


Stavrakov G, Philipova I, Lukarski A, Valkova I, Atanasova M, Dimitrov I, Konstantinov S, Doytchinova I. Acetylcholinesterase inhibitors selected by docking-based screening - proof-of-concept study.
Bulg. Chem. Commun., 50, Special Issue J, 40-48, 2018.

In vitro and in vivo studies of a newly synthesized galantamine derivative with potent anticholinesterase activity.

GRANT: D-78/2017 Medical Research Council, Medical University of Sofia

Results are published in:
Simeonova R, Vitcheva V, Kostadinova I, Valkova I, Philipova I, Stavrakov G, Danchev N, Doytchinova I. Biochemical Studies on a Novel Potent Acetylcholinesterase Inhibitor with Dual-site Binding for Treatment of Alzheimer’s Disease.
C. R. Acad. Bulg. Sci. 72, in press, 2019.


Simeonova R, Vitcheva V, Kostadinova I, Valkova I, Philipova I, Stavrakov G, Danchev N, Doytchinova I. In Vivo Studies on Novel Potent Acetylcholinesterase Inhibitors with Dual-site Binding for Treatment of Alzheimer’s Disease. C. R. Acad. Bulg. Sci. 72, in press, 2019.

Publications

  1. 2019

    1. Dimitrov I, Yordanov V, Flower DR, Doytchinova I. Proteochemometrics for the Prediction of Peptide Binding to Multiple HLA class II proteins. In: Multi-Target Drug Design Using Chem-Bioinformatic Approaches. Roy K, (Ed.), Methods in Pharmacology and Toxicology, Springer Protocols, Humana Press, New York, USA, 2019, pp. 395-404.
    2. Gevrenova R, Doytchinova I, Kolodziej B, Henry M. In-depth characterization of the GOTCAB saponins in seven cultivated Gypsophila L. species (Caryophyllaceae) by liquid chromatography coupled with quadrupole-Orbitrap mass spectrometer. Biochem. Sys. Eco. 83, 91-102, 2019.
    3. Zaharieva N, Dimitrov I, Flower DR, Doytchinova I. VaxiJen Dataset of Bacterial Immunogens: An Update. Curr. Comp.-Aided Srug Des. 15, in press, 2019
    4. Simeonova R, Vitcheva V, Kostadinova I, Valkova I, Philipova I, Stavrakov G, Danchev N, Doytchinova I. Biochemical Studies on a Novel Potent Acetylcholinesterase Inhibitor with Dual-site Binding for Treatment of Alzheimer’s Disease. C. R. Acad. Bulg. Sci. 72, in press, 2019.
    5. Simeonova R, Vitcheva V, Kostadinova I, Valkova I, Philipova I, Stavrakov G, Danchev N, Doytchinova I. In Vivo Studies on Novel Potent Acetylcholinesterase Inhibitors with Dual-site Binding for Treatment of Alzheimer’s Disease. C. R. Acad. Bulg. Sci. 72, in press, 2019.
    6. Manoylov IK, Boneva GV, Doytchinova IA, Mihaylova NM, Tchorbanov AI. Protein-engineered molecules carrying GAD65 epitopes and targeting CD35 selectively down-modulate disease-associated human B lymphocytes. Clin. Exp. Immunol. in press, 2019.

  2. 2018

    1. Philipova I, Valcheva V, Mihaylova R, Mateeva M, Doytchinova I, Stavrakov G. Synthetic piperine amide analogs with antimycobacterial activity. Chem. Biol. Drug Des., 91, 763-768, 2018
    2. Yordanov V, Dimitrov I, Doytchinova I. Proteochemometrics-based prediction of peptide binding to HLA-DP proteins. J. Chem. Inf. Model., 58, 297-304, 2018
    3. Doytchinova I. Flower DR. In silico prediction of cancer immunogens: current state of the art. BMC Immunology, 19, 11, 2018
    4. Doytchinova I. Atanasova M, Valkova I, Stavrakov G, Philipova I, Zhivkova Z, Zheleva-Dimitrova D, Konstantinov S, Dimitrov I. Novel hits for acetylcholinesterase inhibition derived by docking-based screening on ZINC database. J. Enz. Inh. Med. Chem, 33, 768-776, 2018
    5. Kadiyska T, Mladenova M, Dimitrov I, Doytchinova I. Milk allergy in HLA-DRB1*14:19/14:21 paediatric patients: a bioinformatics approach. Pharmacia Sofia, 65, 23-27, 2018.
    6. Zhivkova Z. Quantitative structure – pharmacokinetics relationship for the steady state volume of distribution of basic and neutral drugs. World J Pharm Pharm Sci, 7(2), 94-105, 2018.
    7. Zhivkova Z. Quantitative structure – pharmacokinetics modeling of the unbound clearance for neutral drugs. Int J Current Pharm Res, 10(2), 56-59, 2018.
    8. Zhivkova Z. Quantitative structure – pharmacokinetics relationship for plasma protein binding of neutral drugs. Int J Pharm Pharm Sci, 10(4), 88-93, 2018.
    9. Hristova M, Atanasova M, Valkova I, Andonova L, Doytchinova I, Zlatkov A. Molecular docking study on 1-(3-(4-benzylpiperazin-1-yl)propyl)-3,7-dimethyl-1H-purine-2,6(3H,7H)-dione as an acetylcholinesterase inhibitor. CBU International Conference on Innovations in Science and Education, Prague, March 21-23, 2018.
    10. Remington, B., Broekman, H.C.H., Blom, W.M., Capt, A., Crevel, R.W.R., Dimitrov, I., Faeste, C.K., Fernandez-Canton, R., Giavi, S., Houben, G.F., Glenn, K.C., Madsen, C.B., Kruizinga, A.K., Constable, A. Approaches to assess IgE mediated allergy risks (sensitization and cross-reactivity) from new or modified dietary proteins. Food Chem Toxicol, 112,97-107, 2018.
    11. Stavrakov G, Philipova I, Lukarski A, Valkova I, Atanasova M, Dimitrov I, Konstantinov S, Doytchinova I. Acetylcholinesterase inhibitors selected by docking-based screening - proof-of-concept study. Bulg. Chem. Commun. 50, Special Issue J, 40-48, 2018.

  3. 2017

    1. Yordanov, V., Dimitrov, I., Doytchinova, I. Proteochemometrics and the MHC Binding Prediction. Lett. Drug Des. Discov., 14, 2-9, 2017
    2. Stavrakov, G., Philipova, I., Zheleva, D., Valkova, I., Salamanova, E., Konstantinov, S., Doytchinova, I. Docking-based design and synthesis of galantamine - camphane hybrids as inhibitors of acetylcholinesterase. Chem. Biol. Drug Des., 90, 709-718, 2017
    3. Doytchinova, I., Atanasova M, Stavrakov, G., Philipova, I., Zheleva, D. Galantamine derivatives as acetylcholinesterase inhibitors: docking, design, synthesis, and inhibitory activity. Computational Modeling of Drugs Against Alzheimer's Disease, Neuromethods 132, 163-176, 2017, Springer
    4. Zaharieva N, Dimitrov I, Flower DF, Doytchinova, I. Immunogenicity prediction by VaxiJen: a ten year overview. J. Proteomics Bioinform. 10, 298-310, 2017
    5. Atanasova M, Dimitrov I, Doytchinova I. T-cell epitope prediction by sequence-based methods and molecular docking of proteins from Boophilus microplus. Pharmacia, Sofia, 64, 2017, 3, 13-21.
    6. Atanasova M, Dimitrov I, Doytchinova I. Prediction of peptide binding to swine leukocyte antigen (SLA-1) proteins by molecular docking. Pharmacia, Sofia, 64, 2017, 4, 3-15.
    7. Yordanov V, Dimitrov I, Doytchinova I. Proteochemometric analysis of peptides binding to human leucocyte antigen (HLA) proteins from locus DP. Pharmacia, Sofia, 64, 2017, 4, 31-42.
    8. Zhivkova Z. Quantitative structure – pharmacokinetic relationships for plasma clearance of basic drugs with consideration of the major elimination pathway, J Pharm Pharm Sci, 20, 135-147, 2017.
    9. Zhivkova Z. Quantitative structure – pharmacokinetics relationships for plasma protein binding of basic drugs. J Pharm Pharm Sci, 20, 349-359, 2017.
    10. Zhivkova Z. Application of QSPkR for prediction of key pharmacokinetgic parameters. LAMBERT Academic Publishing , 2017
    11. Angelova, V.T., Valcheva, V., Vassilev, N.G., Buyukliev, R., Momekov, G., Dimitrov, I., Saso, L., Djukic, M., Shivachev, B. Antimycobacterial activity of novel hydrazide-hydrazone derivatives with 2H-chromene and coumarin scaffold, Bioorg Med Chem Lett,27(2), 223-227, 2017.
     

  4. 2016

    1. Stavrakov, G., Valcheva, V., Voynikov, Y., Atanasova, M., Peikov, P.,Doytchinova, I. Design, synthesis and antimycobacterial activity of novel theophylline-7-acetic acid derivatives with amino acid moieties. Chem. Biol. Drug Des., 87, 335-341, 2016.
    2. Dimitrov, I., Doytchinova, I. Associations between main food allergens and HLA-DR/DQ polymorphism. Int. Arch. Allergy Immunol., 169, 33-39, 2016.
    3. Stavrakov, G., Philipova, I., Zheleva, D., Atanasova, M., Konstantinov, S., Doytchinova, I. Docking-based design of galantamine derivatives with dual-site binding to acetylcholinesterase. Mol. Inf., 35, 278-285, 2016.
    4. Dimitrov I., Atanasova M., Patronov A., Flower D.R., Doytchinova, I. A cohesive and integrated platform for immunogenicity prediction. Vaccine Design. Methods and Protocols., Volume 2. Thomas S. (Ed.), Methods in Molecular Biology. Springer, 2016, 1404, pp. 761-770
     

  5. 2015

    1. Atanasova, M., Yordanov, N., Dimitrov, I., Berkov, S., Doytchinova, I. Molecular docking study on galantamine derivatives as cholinesterase inhibitors. Mol. Inf., 34, 394-403, 2015.
    2. Dimitrov, I., Doytchinova, I. Peptide binding prediction to five most frequent HLA-DQ proteins – a proteochemometric approach. Mol. Inf., 34, 467-476, 2015.
    3. Atanasova, M., Doytchinova, I. Substrate - inositol transporte interactions: molecular docking study. Lett. Drug Des. Discov., 12, 622-627, 2015.
    4. Atanasova, M., Stavrakov, G., Philipova, I., Zheleva, D., Yordanov, N., Doytchinova, I. Galantamine derivatives with indole moiety: docking, design, synthesis and acetylcholinesterase activity Bioorg. Med. Chem., 23, 5382-5389, 2015.
    5. Zhivkova, Z., Mandova, T., Doytchinova, I. Quantitative structure - pharmacokinetics relationship analysis of basic drugs: volume of distribution. J. Pharm. Pharm. Sci., 18, 515-527, 2015.
    6. Gevrenova, R., Weng, A., Voutguenne-Nazabadioko, L., Thakur, M.,Doytchinova, I. Quantitative Structure - Activity Relationship study on saponins as cytotoxicity enhancers. Lett. Drug Des. Discov., 12, 166-171, 2015.
    7. Zheleva-Dimitrova D. Zh., Balabanova, V., Gevrenova, R., Doichinova, I., Vitkova A. Chemometrics-based Approach in Analysis of Arnicae flos. Phamacogn. Mag., 11, S538-S544, 2015.
    8. Zhivkova, Z.Studies on Drug-Human Serum Albumin Binding: The Current State of the Matter. Curr. Pharm. Des., 21, 1817-1830, 2015.
     

  6. 2014

    1. Patronov, A., Salamanova, E., Dimitrov, I., Flower, D. R., Doytchinova, I. Histidine hydrogen bonding in MHC at pH 5 and pH 7 modeled by molecular docking and molecular dynamics simulations. Curr. Comp.-Aid. Drug Des., 10, 41-49, 2014.
    2. Dimitrov, I., Naneva, L., Bangov, I., Doytchinova, I. Allergenicity prediction by artificial neural networks. J. Chemometr., 28, 282-286, 2014.
    3. Naneva, L., Dimitrov, I., Bangov, I., Doytchinova, I. Allergenicity prediction by partial least squares-based discriminant analysis. Bulg. Chem. Commun., 46, 389-396, 2014.
    4. Walshe V., Hattotuwagama C., Doytchinova I. , Flower D.R. A dataset of experimental HLA-B*2705 peptide binding affinities. Dataset Papers in Science, 2014, article ID 914684, 2014.
    5. Dimitrov, I., Naneva, L., Bangov, I., Doytchinova, I. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics, 30(6), 846-851, 2014.
    6. Petkova, Z., Valcheva, V., Momekov, G., Petrov, P., Dimitrov, V., Doytchinova, I., Stavrakov, G., Stoyanova, M. Antimycobacterial activity of chiral aminoalcohols with camphane scaffold. Eur. J. Med. Chem., 81, 150-157, 2014.
    7. Stavrakov, G., Valcheva, V., Philipova, I., Doytchinova, I. Design of novel camphene-based derivatives with antimycobacterial activity. J. Mol. Graph. Model., 51, 7-12, 2014.
    8. Dimitrov, I., Bangov, I., Flower, D.R., Doytchinova, I. AllerTOP v.2 - a server for in silico prediction of allergens. J. Mol. Model., 20, 2278, 2014.
    9. Zhivkova, Z., Doytchinova, I. In Silico Quantitative Structure - Pharmacokinetic Relationship Modeling on Acidic Drugs: Half life. Int. J. Pharm. Pharm. Sci., 6, 283-289, 2014.
    10. Gevrenova, R., Weng, A., Voutguenne-Nazabadioko, L., Thakur, M., Doytchinova, I. Quantitative Structure - Activity Relationship study on saponins as cytotoxicity enhancers. Lett. Drug Des. Discov., 12, 166-171, 2014.
     

  7. 2013

    1. Patronov, A., I.A. Doytchinova: T-cell epitope vaccine design by immunoinformatics. Open Biology, 3, 120139, 2013.
    2. Dimitrov I., Flower D. R., Doytchinova I. AllerTOP - a server for in silico prediction of allergens. BMC Bioinformatics, 14 (Suppl.6), S4, 2013.
    3. Gevrenova, R., Badjakov, I., Nikolova, M., Doytchinova, I. Phenolic derivatives in raspberry (Rubus L.) germplasm collection in Bulgaria. Biochem. Syst. Ecol., 50, 419-427, 2013.
    4. Zhivkova, Z., Doytchinova, I. Quantitative structure - clearance relationships of acidic drugs. Mol. Pharmaceutics, 10(10), 3758-3768, 2013.
    5. Atanasova, M., Patronov, A., Dimitrov, I., Flower, D. R., I.A. Doytchinova EpiDOCK - a molecular docking-based tool for MHC class II binding prediction. Protein Eng. Des. Sel., 26, 631-634, 2013.
    6. Stavrakov, G., Valcheva, V., Philipova, I., Doytchinova, I. Novel camphane-based anti-tuberculosis agents with nanomolar activity. Eur. J. Med. Chem., 70, 372-379, 2013.
    7. Ivanov, S., Dimitrov, I., Doytchinova, I. Quantitative prediction of peptide binding to HLA-DP1 protein. IEEE Trans. Comp. Biol. Bioinf., 10, 811-815, 2013.
     

  8. 2012

    1. Zhivkova, Z., Doytchinova, I. Prediction of steady-state volume of distribution of acidic drugs by quantitative structure - pharmacokinetics relationships. J. Pharm. Sci., 101(3), 1253-1266, 2012
    2. Valkova I., Zlatkov A., Krystyna Nedza K., Doytchinova I. Synthesis, 5-HT1A and 5-HT2A receptor affinity and QSAR study of 1-benzhydryl-piperazine derivatives with xanthine moiety at N4. Med. Chem. Res., 21(4), 477-486, 2012
    3. Zhivkova, Z., Doytchinova, I. Quantitative structure - plasma protein binding relationships of acidic drugs. J. Pharm. Sci., 101(12), 4627-4641, 2012
    4. Patronov, A., Dimitrov, I.Flower, D. R., Doytchinova, I. Peptide binding to HLA-DP2 proteins at pH 5.0 and pH 7.0: a quantitative molecular docking study. BMC Struct. Biol., 12, 20, 2012
    5. Yoncheva, K. Doytchinova, I., Tankova, L. Preparation and evaluation of isosorbide mononitrate hydrogels for topical fissure treatment. Curr. Drug Delivery, 9, 452-458, 2012.
     

  9. 2011

    1. Bakalova A., Varbanov H., Buyukliev R., Momekov G., Ivanov D., Doytchinova I. Platinum complexes with 5-methyl-5(4-pyridyl)hydantoin and its 3-methyl derivatives: Synthesis and cytotoxic activity - quantitative structure - activity relationships. Arch. Pharm. Chem. Life Sci., 344(4), 209-216, 2011
    2. Atanasova, M., Dimitrov, I., Flower, D. R., Doytchinova, I. MHC class II binding prediction by molecular docking. Mol. Informatics, 30(4), 368-375, 2011
    3. Dimitrov I., Flower D. R., Doytchinova I. Improving in silico prediction of epitope vaccine candidates by union and intersection of single predictors. World J. Vaccines, 1(2), 15-22, 2011
    4. Patronov, A., Dimitrov, I., Flower, D. R., Doytchinova, I. Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach. BMC Struct. Biol., 11:32, 2011
    5. Doytchinova, I., Petkov, P., Dimitrov, I., Atanasova, M., Flower, D. R. HLA-DP2 binding prediction by molecular dynamics simulations. Protein Sci., 20(11), 1918-1928, 2011
     

  10. 2010

    1. Dimitrov, I. , P. Garnev, D. R. Flower, I. Doytchinova: Peptide binding to the HLA-DRB1 sypertype: A proteochemometric analysis, Eur. J. Med. Chem., 45(1), 236-243, 2010
    2. Solankee, A., K. Kapadia, A. Ćirić, M. Soković, I. Doytchinova, A. Geronikaki: Synthesis of some new S-triazine based chalcones and their derivatives as potent antimicrobial agents, Eur. J. Med. Chem., 45(2), 510-518, 2010
    3. Yoncheva, K.,I. Doytchinova, J. M. Irache: Different approaches for determination of the attachment degree of polyethylene glycols to poly(anhydride) nanoparticles, Drug Develop. Ind. Pharm., 36(6), 676-680, 2010
    4. Dimitrov, I., P. Garnev, D. R. Flower, I. Doytchinova: MHC clas binding prediction - a little help from a friend. J. Biomed. Biotech., 2010, article ID 705821, 2010
    5. Dimitrov, I., P. Garnev, D. R. Flower, I. Doytchinova: EpiTOP - a proteochemometric tool for MHC class II binding prediction. Bioinformatics, 26(16), 2066-2068, 2010
    6. D.R. Flower, I.K. Macdonald, K. Ramakrishnan, M.N. Davies, I.A. Doytchinova: Computer-aided selection of candidate vaccine antigens. Immunome Res., 6(Suppl. 2), S1, 2010
     

  11. 2009

    1. Tankova, L., K. Yoncheva, D. Kovatchki, I. Doytchinova: Topical and fissure treatment: placebo-controlled study of mononitrate and trinitrate therapies. Int J. Colorectal Dis., 24(1), 461-464, 2009.
    2. Walshe, V. A., Hattotuwagama, C. K., Doytchinova, I., Wong, M., Macdonald, I. K., Mulder, A., Claas, F. H. J., Pellegrino, P., Turner, J., Williams, I., Turnbull, E. L., Borrow, P., Flower, D. R. Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: A bioinformatic approach to the prediction of new epitopes. PLoS ONE, 4(11), e8095, 2009.
     

  12. 2008

    1. Doytchinova, I.A., D.R. Flower: Bioinformatic approach for identifying parasite and fungal candidate subunit vaccines. The Open Vaccine Journal, 1(1), 22-26, 2008.
    2. Doytchinova, I.A., D.R. Flower: QSAR and the prediction of T-cell epitopes. Current Proteomics, 5(2), 73-95, 2008.
     

  13. 2007

    1. Doytchinova, I.A., D.R. Flower: Predicting T cell epitopes using multivariate statistics: Comparison of discriminant analysis and multiple linear regression. J. Chem. Inf. Model., 47(1), 234-238, 2007.
    2. Davies, M. N., P. Guan, M.J. Blythe, J. Salomon, C.P. Toselan, C. Hattotuwagama, V. Walshe, I.A. Doytchinova, D.R. Flower: Using databases and data mining in vaccinology. Expert Opin. Drug Discov., 2(1), 19-35, 2007.
    3. Hattotuwagama, C.K., P. Guan, M. Davies, D.J. Taylor, V. Walshe, S.L. Hemsley, C. Toseland, I.A. Doytchinova, P. Borrow, D.R. Flower: Empirical, AI, and QSAR Approaches to Peptide – MHC Binding Prediction. In: In silico Immunology. (Eds. D.R. Flower, J. Timmis), Springer, New York, 139-176, 2007.
    4. Guan, P., I.A. Doytchinova, D.R. Flower: Identifying Major Histocompatibility Complex Supertypes. In: In silico Immunology. (Eds. D.R. Flower, J. Timmis), Springer, New York, 197-234, 2007.
    5. Guan, P., I.A. Doytchinova, D.R. Flower: The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods. In: Immunoinformatics: Predicting Immunogenicity In Silico, Series: Methods in Molecular Biology, Vol. 409, (Ed. D.R. Flower), 143-154, 2007.
    6. Hattotuwagama, C., I.A. Doytchinova, D.R. Flower: Towards the Prediction of Class I and II Mouse Major Histocompatibility Complex Peptide Binding Affinity: In Silico Bioinformatic Step by Step Guide Using Quantitative Structure-Activity Relationships. In: Immunoinformatics: Predicting Immunogenicity In Silico, Series: Methods in Molecular Biology, Vol. 409, (Ed. D.R. Flower), 227-245, 2007.
    7. Hattotuwagama, C.K., P. Guan, I.A. Doytchinova, D.R. Flower: In Silico QSAR-Based Predictions of Class I and Class II MHC Epitopes. In: Immunoinformatics (Eds. C. Schoenbach, S. Ranganathan, V. Brusic), Sprinder Science+Business Media, LLC, New York , 63 – 89, 2007.
    8. Doytchinova, I.A., D.R. Flower: Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine, 25(5), 856-866, 2007.
    9. Doytchinova, I.A., D.R. Flower: VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics, 8, 4, http://www.biomedcentral.com/1471-2105/8/4, 2007.
    10. Slavov, S., M. Atanasova, B. Galabov: QSAR analysis of the anticancer activity of 2,5-disubstituted 9-aza-anthrapyrazoles, QSAR Comb. Chem., 26(2), 173-181, 2007.
    11. Atanasova, M., Ilieva, S., Galabov, B.: QSAR analysis of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines with anticancer activity, Eur. J. Med. Chem., 42(9), 1184-1192, 2007.
     

  14. 2006

    1. Vicini, P., M. Incerti, I. A. Doytchinova, P. La Colla, B. Busonera, R. Loddo. Synthesis and antiproliferative activity of benzo[d]isothiazole hydrazones. Eur. J. Med. Chem., 2006, 41, 624-632.
    2. Hattotuwagama, C.K., C.P. Toseland, P. Guan, D.J. Taylor, S.L. Hemsley, I.A. Doytchinova, D.R. Flower: Toward Prediction of Class II Mouse Major Histocompatibility Complex Peptide Binding Affinity: In Silico bioinformatic Evaluation Using Partial Least Squares, a Robust Multivariate Statistical Technique. J. Chem. Inf. Model., 46(3), 1491-1502, 2006.
    3. Guan, P., I. Doytchinova, C. Hattotuwagama, D.R. Flower: MHCPred 2.0, an updated quantitative T cell epitope prediction server. Appl. Bioinformatics, 5(1), 55-61, 2006.
    4. Doytchinova, I.A., D.R. Flower: Class I T cell epitope prediction: improvements using a combination of Proteasome cleavage, TAP affinity, and MHC binding. Mol. Immun., 43(13), 2037-2044, 2006.
    5. Doytchinova, I.A., P. Guan, D.R. Flower: EpiJen: a server for multi-step T cell epitope prediction. BMC Bioinformatics, 7, 131, http://www.biomedcentral.com/ 1471-2105/7/131, 2006.
    6. Doytchinova, I.A., D.R. Flower: Modeling the peptide - T cell receptor interaction by the Comparative Molecular Similarity Indices Analysis – Soft Independent Modeling of Class Analogy technique. J. Med. Chem., 49(7), 2193-2199, 2006.
     

  15. 2005

    1. Doytchinova, I.A., D.R. Flower: In silico identification of supertypes for Class II Major Histocompatibility Complexes. J. Immunol., 174(11), 7085-7095, 2005.
    2. Doytchinova, I.A., V. Walshe, P. Borrow, D.R. Flower: Towards the chemometric dissection of peptide-HLA-A*0201 binding affinity: comparison of local and global QSAR models. J. Comput. Aid. Mol. Des., 19(3), 203-212, 2005.
    3. Hattotuwagama, C.K., I.A. Doytchinova, D.R. Flower: In Silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: A Comparative Molecular Similarity Index Analysis (CoMSIA) study. J. Chem. Inf. Model., 45(5), 1415-1426, 2005.
    4. Guan, P., I.A. Doytchinova, V.A. Walshe, P. Borrow, D.R. Flower: Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for HLA-A*0201. J. Med. Chem., 48(23), 7418-7425, 2005.
    5. Toseland C.P., D.J. Taylor, H. McSparron, S.L. Hemsley, M.J. Blythe, K. Paine, I.A. Doytchinova, P. Guan, C.K. Hattotuwagama, D.R. Flower: AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical and cellular data. Immunome Res., 1: 4. http://www.immunone-research.com/content/ 1/1/4, 2005.
    6. Guan, P., M. Davies, D.J. Taylor , S. Wan, H. McSparron, S.L. Hemsley, C. Toseland, M.J. Blythe, P.D. Taylor, V. Walshe, C.K. Hattotuwagama, I.A. Doytchinova, P.V. Coveney, P. Borrow, D.R..Flower: Computational Chemistry, Informatics, and the Discovery of Vaccines. Current Computer-Aided Drug Design, 1(4), 377-398, 2005.
     

  16. 2004

    1. Doytchinova, I.A., P. Guan, D. R. Flower. Identifiying human MHC supertypes using bioinformatic methods. J. Immunol., 172(7), 4314 – 4323, 2004.
    2. Hattotuwagama, C.K., P. Guan, I.A. Doytchinova, C. Zygouri, D.R. Flower: Quantitative online prediction of peptide binding to the major histocompatibility complex. J. Mol. Graph. Model., 22(3), 195-207, 2004.
    3. Doytchinova, I.A., V.A. Walshe, N.A. Jones, S.E. Gloster, P. Borrow, D.R. Flower: Coupling in silico and in vitro analysis of peptide–MHC binding: A bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes. J. Immunol., 172(12), 7495-7502, 2004.
    4. Doytchinova, I.A., S. Hemsley, D.R. Flower: Transporter associated with antigen processing preselection of peptides binding to the MHC: A Bioinformatic evaluation. J. Immunol., 173(11), 6813-6819, 2004.
    5. Hattotuwagama, C.K., P. Guan, I.A. Doytchinova, D.R. Flower: New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity. Org. Biomol. Chem., 2(22), 3274-3283, 2004.
    6.Doytchinova, I.A., P. Guan, D.R. Flower: Quantitative structure – activity relationships and the prediction of MHC supermotifs. Methods, 34(4), 444-453, 2004.
    7. Valkova, I., Vračko, M., Basak, S.C., Modeling of structure-mutagenicity relationships: Counter propagation neural network approach using calculated structural descriptors Analytica Chimica Acta 509 (2), 179-186, 2004
     

  17. 2003

    1. Panico A.M., A. Geronikaki, R. Mgonzo, V. Cardile, B. Gentile, I. Doytchinova. Aminothiazole derivatives with antidegenerative activity on cartilage. Bioorg. ∧ Med. Chem., 11, 2003, 13, 2983 - 2989.
    2. Guan P., I.A. Doytchinova, D.R. Flower: HLA-A3-supermotif defined by quantitative structure-activity relationship analysis. Protein Eng., 16(1), 11-18, 2003.
    3. Guan, P., I.A. Doytchinova, C.Zygouri, D.R. Flower: MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Appl. Bioinformatics, 2(1), 63-66, 2003.
    4. Guan, P., I.A. Doytchinova, C.Zygouri, D.R. Flower: MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res., 31(13), 3621-3624, 2003.
    5. Guan, P., I.A. Doytchinova, D.R. Flower: A Comparative Molecular Similarity Indices (CoMSIA) study of peptides binding to the HLA-A3 superfamily. Bioorgan. Med. Chem., 11(10), 2307-2311, 2003.
    6. Doytchinova, I.A., D.R. Flower: The HLA-A2-supermotif: A QSAR definition. Org. Biomol. Chem., 1(15), 2648-2654, 2003.
    7. McSparron, H., M.J. Blythe, C. Zygouri, I.A. Doytchinova, D.R. Flower: JenPep: A Novel Computational Information Resource for Immunobiology and Vaccinology. J. Chem. Inf. Comp. Sci., 43(4), 1276 – 1287, 2003.
    8. Doytchinova, I.A., D.R. Flower: Towards the in silico identification of class II restricted T cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics, 19(17), 2263 – 2270, 2003.
    9. Doytchinova, I.A., P. Taylor, D.R. Flower: Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone. J. Biomed. Biotechnol., 2003(5), 267 – 290, 2003.
    10. Flower, D.R., H. McSparron, M.J. Blythe, C. Zygouri, D. Taylor, P. Guan, S. Wan, P. Coveney, V. Walshe, P. Borrow, I.A. Doytchinova: Computational vaccinology: quantitative approaches. In: Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function, (Eds. G. Bock, J. Goode), Wiley J. ∧ Sons Ltd., Chichester, 102-120, 2003.
     

  18. 2002

    1. Doytchinova, I., I. Valkova, R. Natcheva. Adenosine A2A receptor agonists: CoMFA - based selection of the most predictive conformation. SAR QSAR Environ. Res., 13, 2002, 2, 227-235.
    2. Vicini P., F. Zani, P. Cozzini, I. Doytchinova. Hydrazones of 1,2-benzisothiazole hydrazides: synthesis, antimicrobial activity and QSAR investigations. Eur. J. Med. Chem., 37, 2002, 553-564.
    3. Blythe, M.J., I.A. Doytchinova, D.R. Flower: JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics, 18(3), 434-439, 2002.
    4. Doytchinova, I.A., M.J. Blythe, D.R. Flower: Additive Method for the Prediction of Protein-Peptide Binding Affinity. Application to the MHC Class I Molecule HLA-A*0201. J. Proteome Res., 1(3), 263-272, 2002.
    5. Doytchinova, I.A., D.R. Flower: Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: A three-dimensional quantitative structure-activity relationship study. PROTEINS, 48(3), 505-518, 2002.
    6. Doytchinova, I.A., D.R. Flower: A Comparative Molecular Similarity Index Analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J. Comput. Aid. Mol. Des., 16(8-9), 535-544, 2002.
    7. Doytchinova I.A., D.R. Flower: Quantitative approaches to computational vaccinology. Immunol. Cell Biol., 80(3), 270-279, 2002.
    8. Flower, D.R., I.A. Doytchinova: Immunoinformatics and the prediction of immunogenicity. Appl. Bioinformatics, 1(4), 167-176, 2002.
    9. Flower, D.R., I.A. Doytchinova, K. Paine, P. Taylor, M.J. Blythe, D. Lamponi, C. Zygouri, P. Guan, H. McSparron, H. Kirkbride: Computational Vaccine Design. In: Drug Design: Cutting Edge Approaches, (Ed. D.R. Flower), RSC publications, Cambridge, 136-180, 2002.
    10. Aptula, A.O., Netzeva, T.I., Valkova, I.V., Cronin, M.T.D., Schultz, T.W., Kühne, R., Schüürmann, G,. Multivariate discrimination between modes of toxic action of phenols В Quantitative Structure-Activity Relationships 21 (1), 12-22, 2002.
    11. Mitcheva, M., Vitcheva, V., Manolov, I., Valkova, I., Effects of two newly synthesized 4-hydroxycoumarin derivatives (4-OHC) on isolated rat hepatocytes В Methods and Findings in Experimental and Clinical Pharmacology 24 (6), 345-349, 2002.
    12. Cronin, M.T.D., Aptula, A.O., Duffy, J.C., Netzeva, T.I., Rowe, P.H., Valkova, I.V., Schultz, T.W., Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 49 (10), 1201-1221, 2002.
     

  19. 2001

    1. Doytchinova, I. CoMFA-Based Comparison of Two Models for Binding Site on Adenosine A1 receptors. J. Comput.-Aid. Mol. Des., 15, 2001, 1, 29-39.
    2. Doytchinova, I., D.R.Flower. Towards the quantitative prediction of T-cell epitopes: CoMFA and CoMSIA studies of peptides with affinity to class I MHC molecule HLA-A*0201. J. Med. Chem., 44, 2001, 3572-3581.
    3. Doytchinova, I., I. Valkova, R. Natcheva. CoMFA Study on Adenosine A2A Receptor Agonists. Quant. Struct. – Act. Relat., 20, 2001, 2, 124-129.
     

  20. 2000

    1. Netzeva, T., I. Doytchinova, R. Natcheva. 2D and 3D QSAR analysis of some valproic acid metabolites and analogues as anticonvulsant agents. Pharmaceut. Res., 17, 2000, 6, 727-732.
    2. Dimitrova, B., I. Doytchinova, M. Zlatkova. Reversed-phase high-performance liquid chromatography for evaluating the distribution behavior of pharmaceutical substances in suppository base Witepsol H15-phosphate buffer system. J. Pharmaceut. Biomed. Anal., 23, 2000, 955-964.
     

  21. 1999

    1. Heun, G., N.Lambov, A.Zlatkov, P.Peikov, I.Doytchinova, K.Gesheva. Biodegradable cross-linked prodrug of the bronchial dilator Vephylline: II. Kinetics and quantum chemical studies on the release mechanism. J. Control. Release, 58, 1999, 189-194.
    2. Hadjipavlou-Litina, D., A. Geronikaki, R. Mgonzo, I. Doytchinova. Thiazolyl-N-substituted amides: a group of effective anti inflammatory agents with potential for local anaesthetic properties. Synthesis, biological evaluation and a QSAR approach. Drug Develop. Res., 48, 1999, 53-60.
    3. Netzeva, T., R. Natcheva, I. Doytchinova. A QSAR Study of Some Ethers of Dihydroartemisinin as Antimalarial Agents. Pharmacia, Sofia, 46, 1999, 1, 5-10.
     

  22. 1998

    1. Doytchinova, I. , S.Petrova. “N6-N7” - a Modification of the “N6-C8” Model for the Binding Site on Adenosine A1 Receptors with Improved Steric and Electrostatic Fit. Med.Chem.Res., 8, 1998, 3, 143-152.
    2. Netzeva, T., R.Natcheva, I.Doytchinova, I.Lesigiarska, D.Mihailova. Theoretical investigation of the chemical structure and QSAR-analysis of a series of 9-substituted artemisinin derivatives. Archives of the Balkan Medical Union , 33, 1998, 4, 189-198.
     

  23. 1997

    1. Doytchinova, I. , R.Natcheva. QSAR-Study on a Series of 1,4-disubstituted Piperazines with Analgesic Activity. Acta Pharm., 47, 1997, 3, 189-195.
     

  24. 1996

  25. 1995

    1. Дойчинова, И., Р. Начева, Д. Михайлова. QSAR анализ на серия новосинтезирани производни на ксантина. Фармация, София, 43, 1995, 1, 8-12.
    2. Дойчинова, И., Р. Начева, Д. Михайлова. QSAR анализ на серия 3-пропилксантини с бронходилатиращо действие. Фармация, София, 43, 1995, 5-6, 30-36.
     

  26. 1994

    1. Doichinova, I.A., R.N.Natcheva, D.N.Mihailova. QSAR-Studies of 8-Substituted Xanthines as Adenosine Receptors Antagonists. Eur. J. Med. Chem., 29, 1994, 2, 133-138.
     

Prof. Irini Doytchinova

Department of Chemistry
Faculty of Pharmacy
Medical University of Sofia

Office

2 Dunav st. 1000 Sofia Bulgaria

+359 2 9236506

idoytchinova@pharmfac.mu-sofia.bg

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