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.
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 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 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 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 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.
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
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.
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.
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: [email protected].
MPharm, PhD, DSci.
Irini Doytchinova received her MPharm and PhD 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 MUS. ID is within the top 1% of scientists in Med Biomol Chem
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.
MChem, PhD
Mariyana Atanasova received MChem and PhD from the Faculty of Chemistry at 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. Her research interests are focused on the design of novel acetylcholiesterase inhibitors and anticancer agents.
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. Her research interests are in the field of drug metabolism and pharmacokinetics. She has developed Quantitative Structure - Pharmacokinetics Relationship (QSPkR) models for prediction of pharmacokinetic parameters like fraction of the unbound-to-plasma-proteins molecules, total clearance,steady-state volume of distribution, half-life.
MSc in Molecular Biology, PhD
Danislav Spassov holds a Master of Science in Molecular Biology from the Faculty of Biology at Sofia University, Bulgaria. He earned his PhD in Immunology and Microbiology from the University of Miami, Florida, USA. From 2004 to 2016, he worked as a postdoctoral fellow and Associate Specialist at the University of California, San Francisco (UCSF), specializing in cancer biology and kinase signaling. More recently, he joined the Drug Design and Bioinformatics Lab as a part of a Marie Skłodowska-Curie fellowship.
MPharm, PhD
Stefan Ivanov graduated with a Master of Pharmacy from the Medical University of Sofia in 2010 and completed a split-site PhD in computational chemistry at The University of Manchester (2013 – 2017) and the Bioinformatics Institute in Singapore (2014 – 2016). After three postdocs in South Korea, his native Bulgaria, and the US, he moved to industry working in drug discovery. His work focuses on computational chemistry, cheminformatics, protein homology modeling, and molecular dynamics (MD) simulations.
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.
MPharm
MSCA fellow
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.
Dimitrov I, Doytchinova I. An Alignment-Independent Platform for Allergenicity Prediction
In: Tomar N. (eds) Immunoinformatics. Methods in Molecular Biology, vol 2131. Humana, New York, NY, pp. 147-153, 2020.
Dimitrov I, Zaharieva N, Doytchinova I. Bacterial immunogenicity prediction by machine learning methods
Vaccines 8, 709, 2020.
Atanasova M, Dimitrov I, Ivanov S. Molecular dynamics simulations of acetylcholinesterase - beta-amyloid peptide complex
Cybern. Inf. Technol. 20, 140-154, 2020.
Dimitrov I, Atanasova M. AllerScreener - a server for allergenicity and cross-reactivity prediction
Cybern. Inf. Technol. 20, 175-184, 2020.
Doytchinova I, Tchorbanov A. Design of multi-epitope vaccine against SARS-CoV-2
Cybern. Inf. Technol. 20, 185-193, 2020.
Doytchinova I, Tchorbanov A, Ivanov S. Immunoinformatic analysis of human thyroglobulin
Cybern. Inf. Technol. 20, 194-200, 2020.
Doytchinova I. Drug design - past, present, future.
Molecules 27, 1496, 2022.
Atanasova M, Dimitrov I, Ivanov S, Georgiev B, Berkov S, Zheleva-Dimitrova D, Doytchinova I. Virtual screening and hit selection of natural compounds as acetylcholonesterase inhibitors.
Molecules 27, 3139, 2022.
Spassov D., Atanasova M, Doytchinova I. Novel hits for N-myristoyltransferase inhibition discovered by docking-based screening.
Molecules 27, 5478, 2022.
Berkov S., Atanasova M, Georgiev B, Bastida J, Doytchinova I. The Amaryllidaceae alkaloids: an untapped source of acetylcholinesterase inhibitors.
Phytochem Rev 21, 1415-1443, 2022.
Simeonova R, Atanasova M, Stavrakov G, Philipova I, Doytchinova I. Ex vivo antioxidant and cholinesterase inhibiting effects of a novel galantamine–curcumin hybrid on scopolamine-induced neurotoxicity in mice
Int J Mol Sci 23, 14843, 2022.
Results are published in:
Dimitrov I, Zaharieva N, Doytchinova I. Bacterial immunogenicity prediction by machine learning methods
Vaccines 8, 709, 2020.
Doneva N, Doytchinova I, Dimitrov I. Predicting immunogenicity risk in biopharmaceuticals
Symmetry, 13, 388, 2021.
Simeonova R, Zheleva D, Valkova I, Stavrakov G, Philipova I, Atanasova M, Doytchinova I. A novel galantamine-curcumin hybrid as a potential multi-target agent agains neurodegenerative disorders
Molecules , 26, 1865, 2021.
Stavrakov G, Philipova I, Lukarski A, Atanasova M, Georgiev B, Atanasova T, Konstantinov S, Doytchinova I. Discovery of a novel acetylcholinesterase inhibitor by fragment-based design and virtual screening
Molecules , 26, 2058, 2021.
Salamanova E, Atanasova M, Dimitrov I, Doytchinova I. Effects of curcumin and ferulic acid on the folding of amyloid-beta peptide
Molecules , 26, 2815, 2021.
Atanasova M, Dimitrov I, Ivanov S, Georgiev B, Berkov S, Zheleva-Dimitrova D, Doytchinova I. Virtual screening and hit selection of natural compounds as acetylcholonesterase inhibitors.
Molecules 27, 3139, 2022.
Doneva N, Dimitrov I. Viral Immunogenicity Prediction by Machine Learning Methods.
Int. J. Mol. Sci. 25, 2949, 2024.
Atanasova M., Stavrakov G., Philipova I., BGeorgiev B., Bastida J., Doytchinova I., Berkov S. AChE inhibitory activity of N-substituted natural galanthamine derivatives.
Bioorg. Med. Chem. Letters 112, 129937, 2024.
Results are published in:
Angelova V.T., Tatarova T., Mihaylova R., Vassilev N., Petrov B., Zhivkova Z., Doytchinova I.
Novel arylsulfonylhydrazones as breast anticancer agents discovered by Quantitative Structure - Activity Relationships.
Molecules, 28, 2058, 2023.
Nikolova-Mladenova B., Momekov G., Zhivkova Z., Doytchinova I. Design, synthesis and cytotoxic activity of novel salicylaldehyde hydrazones against leukemia and breast cancer.
Int. J. Mol. Sci. , 24, 7352, 2023.
Spassov D. S., Atanasova M., Doytchinova I. Inhibitor trapping in N-myristoyltransferases as a mechanism for drug potency.
Int. J. Mol. Sci. , 24, 11610, 2023.
Ruseva N., Atanasova M., Sbirkova-Dimitrova H., Marković A, Šmelcerović Z., Šmelcerović A., Cherneva E., Bakalova A. Chloro-substituted pyridine squaramates as new DNase I inhibitors: Synthesis, structural characterization, in vitro evaluation and molecular docking studies.
Chem. Biol. Interact. , 24, 110772, 2023.
Spassov D. S., Atanasova M., Doytchinova I. Inhibitor trapping in kinases.
Int. J. Mol. Sci. , 25, 3249, 2024.
Sotirov, S., Dimitrov, I. Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines.
Int. J. Mol. Sci. 2024, 25, 4934.
Sotirov, S., Dimitrov, I. Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens.
Appl. Sci. 2024 14, 4034.
Spassov D.S. Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models.
Int. J. Mol. Sci. 2024, 25, 7124.
Ivanov S.M. Calculated hydration free energies become less accurate with increases in molecular weight.
PLoS ONE 2024, 19, e0309996.
Results are published in:
Doytchinova I., Atanasova M., Sotirov, S., Dimitrov, I. In silico identification of peanut peptides suitable for allergy immunotherapy in HLA-DRB1*03:01-restricted patients.
Pharmaceuticals , 17, 1097, 2024.
Results are published in:
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.
Ivanov SM, Atanasova M, Dimitrov I, Doytchinova I. Cellular polyamines condense hyperphosphorylated Tau, triggering Alzheimer's disease
Scientific reports , 10, 10098, 2020.
Stavrakov G, Philipova I, Lukarski A, Atanasova M, Zheleva D, Zhivkova ZD, Ivanov S, Atanasova T, Konstantinov S, Doytchinova I. Galantamine-curcumin hybrids as dual-site binding acetylholinesterase inhibitors
Molecules , 25, 3341, 2020.
Doytchinova I, Atanasova M, Salamanova E, Ivanov S, Dimitrov, I. Curcumin inhibits the primary nucleation of amyloid-beta peptide: a molecular dynamics study
Biomolecules , 10, 1323, 2020.
Atanasova M, Dimitrov I, Ivanov S. Molecular dynamics simulations of acetylcholinesterase - beta-amyloid peptide complex
Cybern. Inf. Technol. 20, 140-154, 2020.
Philipova I, Stavrakov G, Dimitrov V, Vassilev N. Galantamine derivatives: Synthesis, NMR study, DFT calculations and application in asymmetric catalysis
J. Mol. Struc. 1219, 128568, 2020.
Simeonova R, Zheleva D, Valkova I, Stavrakov G, Philipova I, Atanasova M, Doytchinova I. A novel galantamine-curcumin hybrid as a potential multi-target agent against neurodegenerative disorders
Molecules , 26, 1865, 2021.
Stavrakov G, Philipova I, Lukarski A, Atanasova M, Georgiev B, Atanasova T, Konstantinov S, Doytchinova I. Discovery of a novel acetylcholinesterase inhibitor by fragment-based design and virtual screening
Molecules , 26, 2058, 2021.
Salamanova E, Atanasova M, Dimitrov I, Doytchinova I. Effects of curcumin and ferulic acid on the folding of amyloid-beta peptide
Molecules , 26, 2815, 2021.
Mladenova K, Stavrakov G, Philipova I, Atanasova M, Petrova S, Doumanov J, Doytchinova I. A galantamine-curcumin hybrid decreases the cytotoxicity of amyloid-beta peptide on SH-SY5Y cells
Int. J. Mol. Sci. , 22, 7592, 2021.
Simeonova R, Atanasova M, Stavrakov G, Philipova I, Doytchinova I. Ex vivo antioxidant and cholinesterase inhibiting effects of a novel galantamine–curcumin hybrid on scopolamine-induced neurotoxicity in mice.
Int. J. Mol. Sci. , 23, 14843, 2022.
1. Atanasova M., Dimitrov I., Fernandez A., Moreno J., Koning F., Doytchinova I. Assessment of novel proteins triggering celiac disease via docking-based approach. Molecules 2024, 29, 138.
2. Doytchinova I., Atanasova M., Fernandez A., Moreno J., Koning F., Dimitrov I. Modeling peptide-protein interactions by a logo-based method: Application in peptide-HLA binding predictions. Molecules 2024, 29, 284.
3. Doneva N, Dimitrov I. Viral Immunogenicity Prediction by Machine Learning Methods. Int. J. Mol. Sci. 2024, 25, 2949.
4. Kostadinova I, Atanasova M, Stavrakov G, Philipova I, Doytchinova I. A galantamine-curcumin hybrid lacks the depressant side effect of acetylcholinesterase inhibitors. Biotechnol. Biotechnol. Equip. 2024, 38, 2305903.
5. Spassov D.S., Atanasova M., Doytchinova I. Inhibitor trapping in kinases. Int. J. Mol. Sci. 2024, 25, 3249.
6. Sotirov, S., Dimitrov, I. Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines. Int. J. Mol. Sci. 2024, 25, 4934.
7. Sotirov, S., Dimitrov, I. Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens. Appl. Sci. 2024, 14, 4034.
8. Doytchinova I., Atanasova M., Sotirov, S., Dimitrov, I. In silico identification of peanut peptides suitable for allergy immunotherapy in HLA-DRB1*03:01-restricted patients. Pharmaceuticals 2024, 17, 1097.
9. Atanasova M., Stavrakov G., Philipova I., Georgiev B., Bastida J., Doytchinova I., Berkov S. AChE inhibitory activity of N-substituted natural galanthamine derivatives. Bioorg. Med. Chem. Letters 2024, 112, 129937.
10. Spassov D.S. Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models. Int. J. Mol. Sci. 2024, 25, 7124.
11. Ivanov S.M. Calculated hydration free energies become less accurate with increases in molecular weight. PLoS ONE 2024, 19, e0309996.
12. Atanasova M., Dimitrov, I., Ralchev N., Markovski A., Manoylov I., Bradyanova S., Mihaylova N., Tchorbanov A.,Doytchinova I. Design, Development and Immunogenicity Study of a Multi-Epitope Vaccine Prototype Against SARS-CoV-2. Pharmaceuticals 2024, 17, 1498.
13. Salamanova E., Atanasova M., Doytchinova I. A Novel Galantamine– Curcumin Hybrid Inhibits
Butyrylcholinesterase: A Molecular Dynamics Study. Chemistry 2024, 6, 1645-1657.
1. Spassov D.S., Atanasova M., Doytchinova I. A role of salt bridges in mediating drug potency: A lesson from the N-myristoyltransferase inhibitors. Front. Mol. Biosci. 2023, 9, 1066029.
2. Angelova V.T., Tatarova T., Mihaylova R., Vassilev N., Petrov B., Zhivkova Z., Doytchinova I. Novel arylsulfonylhydrazones as breast anticancer agents discovered by Quantitative Structure - Activity Relationships. Molecules. 2023, 28, 2058.
3. Nikolova-Mladenova B., Momekov G., Zhivkova Z., Doytchinova I. Design, synthesis and cytotoxic activity of novel salicylaldehyde hydrazones against leukemia and breast cancer. Int. J. Mol. Sci. 2023, 24, 7352.
4. Marković A, Živković A, Atanasova M, Doytchinova I, Hofmann B, George S, Kretschmer S, Rödl C, Steinhilber D, Stark H, Šmelcerović A. Thiazole derivatives as dual inhibitors of deoxyribonuclease I and 5-lipoxygenase: A promising scaffold for the development of neuroprotective drugs. Chem. Biol. Interact. 2023, 386, 110542.
5. Spassov D. S., Atanasova M., Doytchinova I. Inhibitor trapping in N-myristoyltransferases as a mechanism for drug potency. Int. J. Mol. Sci. 2023, 24, 11610.
6. Ruseva N., Atanasova M., Sbirkova-Dimitrova H., Marković A, Šmelcerović Z., Šmelcerović A., Cherneva E., Bakalova A. Chloro-substituted pyridine squaramates as new DNase I inhibitors: Synthesis, structural characterization, in vitro evaluation and molecular docking studies. Chem. Biol. Interact. 2023, 386, 110772.
7. Doytchinova I, Dimitrov I, Atanasova M. preDQ – a software tool for peptide binding prediction to HLA-DQ2 and HLA-DQ8. EFSA Support. Publ. 2023, 7, 8108E.
8. Atanasova M, Doytchinova I. Docking-Based Prediction of Peptide Binding to MHC Proteins. Methods Mol. Biol. 2023, 2673, 237-249.
9. Dimitrov I, Doytchinova I. Prediction of Bacterial Immunogenicity by Machine Learning Methods. Methods Mol. Biol. 2023, 2673, 289-303.
1. Thomas S, Doytchinova I. In silico identification of the B-cell and T-cell epitopes of the antigenic proteins of Staphylococcus aureus for potential vaccines. In: Thomas S. (ed) Vaccine Design: Methods and Protocols. Volume 3: Resources for Vaccine Development. Methods in Molecular Biology, vol 2412. Springer Science+Business Media, LLC, pp. 439-447, 2022.
2. Chakuleska L, Shkondrov A, Popov G, Zlateva-Panayotova N, Petrova R, Atanasova M, Krasteva I, Doytchinova I, Simeonova R. Beneficial effects of the fructus Sophorai extract on experimentally induced osteoporosis in New Zealand white rabbits. Acta Pharm. 2022, 72, 289-302.
3. Doytchinova I. Drug design - past, present, future. Molecules 2022, 27, 1496.
4. Atanasova M, Dimitrov I, Ivanov S, Georgiev B, Berkov S, Zheleva-Dimitrova D, Doytchinova I. Virtual screening and hit selection of natural compounds as acetylcholonesterase inhibitors. Molecules 2022, 27, 3139.
5. Spassov D, Atanasova M, Doytchinova I. Novel hits for N-myristoyltransferase inhibition discovered by docking-based screening. Molecules 2022, 27, 5478.
6. Berkov S, Atanasova M, Georgiev B, Bastida J, Doytchinova I. The Amaryllidaceae alkaloids: an untapped source of acetylcholinesterase inhibitors. Phytochem Rev 2022, 21, 1415-1443.
7. Simeonova R, Atanasova M, Stavrakov G, Philipova I, Doytchinova I. Ex vivo antioxidant and cholinesterase inhibiting effects of a novel galantamine–curcumin hybrid on scopolamine-induced neurotoxicity in mice. Int J Mol Sci 2022, 23, 14843.
8. Ralchev NR,Markovski AM, Yankova IA, Manoylov IK, Doytchinova IA, Mihaylova NM, Shinkov AD, Tchorbanov AI. Selective Silencing of Disease-Associated B Lymphocytes from Hashimoto’s Thyroiditis Patients by Chimeric Protein Molecules. Int J Mol Sci 2022, 23, 15083.
1. Ogrodowczyk AM, Dimitrov I, Wróblewska B. Two Faces of Milk Proteins Peptides with Both Allergenic and Multidimensional Health Beneficial Impact- Integrated In Vitro/In Silico Approach. Foods. 2021; 10(1):163.
2. Doneva N, Doytchinova I, Dimitrov I. Predicting immunogenicity risk in biopharmaceuticals. Symmetry 13, 388, 2021.
3. 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. 74, 219-225, 2021.
4. Simeonova R, Zheleva D, Valkova I, Stavrakov G, Philipova I, Atanasova M, Doytchinova I. A novel galantamine-curcumin hybrid as a potential multi-target agent against neurodegenerative disorders. Molecules 26, 1865, 2021.
5. Stavrakov G, Philipova I, Lukarski A, Atanasova M, Georgiev B, Atanasova T, Konstantinov S, Doytchinova I. Discovery of a novel acetylcholinesterase inhibitor by fragment-based design and virtual screening. Molecules 26, 2058, 2021.
6. Salamanova E, Atanasova M, Dimitrov I, Doytchinova I. Effects of curcumin and ferulic acid on the folding of amyloid-beta peptide. Molecules 26, 2815, 2021.
7. 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. 74, 906-913, 2021.
8. Mladenova K, Stavrakov G, Philipova I, Atanasova M, Petrova S, Doumanov J, Doytchinova I. A galantamine-curcumin hybrid decreases the cytotoxicity of amyloid-beta peptide on SH-SY5Y cells. Int. J. Mol. Sci. 22, 7592, 2021.
9. Voynikov, Y.; Nedialkov, P.;
Gevrenova, R.; Zheleva-Dimitrova, D.; Balabanova, V.; Dimitrov, I. UHPLC-Orbitrap-MS Tentative Identification of 51 Oleraceins (Cyclo-Dopa Amides) in Portulaca oleracea L. Cluster Analysis and MS2 Filtering by Mass Difference. Plants 2021, 10, 1921.
1. Dimitrov I, Doytchinova I. An Alignment-Independent Platform for Allergenicity Prediction. In: Tomar N. (eds) Immunoinformatics. Methods in Molecular Biology, vol 2131. Humana, New York, NY, pp. 147-153, 2020.
2. Ivanov SM, Atanasova M, Dimitrov I, Doytchinova I. Cellular polyamines condense hyperphosphorylated Tau, triggering Alzheimer's disease. Scientific reports 10, 10098, 2020.
3. Doytchinova I, Atanasova M, Salamanova E, Ivanov S, Dimitrov, I. Curcumin inhibits the primary nucleation of amyloid-beta peptide: a molecular dynamics study. Biomolecules 10, 1323, 2020 + Suppl.
4. Stavrakov G, Philipova I, Lukarski A, Atanasova M, Zheleva D, Zhivkova ZD, Ivanov S, Atanasova T, Konstantinov S, Doytchinova I. Galantamine-curcumin hybrids as dual-site binding acetylholinesterase inhibitors. Molecules 25, 3341, 2020 + Suppl.
5. Dimitrov I, Zaharieva N, Doytchinova I. Bacterial immunogenicity prediction by machine learning methods. Vaccines 8, 709, 2020.
6. Atanasova M, Dimitrov I, Ivanov S. Molecular dynamics simulations of acetylcholinesterase - beta-amyloid peptide complex. Cybern. Inf. Technol. 20, 140-154, 2020.
7. Dimitrov I, Atanasova M. AllerScreener - a server for allergenicity and cross-reactivity prediction. Cybern. Inf. Technol. 20, 175-184, 2020.
8. Doytchinova I, Tchorbanov A. Design of multi-epitope vaccine against SARS-CoV-2. Cybern. Inf. Technol. 20, 185-193, 2020.
9. Doytchinova I, Tchorbanov A, Ivanov S. Immunoinformatic analysis of human thyroglobulin. Cybern. Inf. Technol. 20, 194-200, 2020.
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 Drug Des. 15, 398-400, 2019
4. 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. 197, 329-340, 2019.
5. Chakuleska L, Michailova R, Shkondrov A, Manov V, Zlateva-Panayotova N, Marinov G, Petrova R, Atanasova M, Krasteva I, Danchev N, Doytchinova I, Simeonova R. Bone protective effects of purified extract from Ruscus aculeatus on ovariectomy-induced osteoporosis in rats. Food Chem. Toxicol. 132, 110668, 2019.
6. Kondeva-Burdina M, Doytchinova I, Krasteva I, Ionkova I, Manov V. Hepato-, neuroprotective effects and QSAR studies on flavoalkaloids and flavonoids from Astragalus monspessulanus. Biotechnol. Biotechnol. Equip. 33, 1434-1443, 2019.
7. Manoylov IK, Boneva GV, Doytchinova IA, Mihaylova NM, Tchorbanov AI. Suppression of disease-associated B lymphocytes by GAD65 epitope-carrying protein-engineered molecules in a streptozotocin-induced mouse model of diabetes. Monoclon. Antib. Immunodiagn. Immunother. 38, 201-208, 2019.
8. Ivanov SM, Dimitrov I, Doytchinova IA. Bridging solvent molecules mediate RNase A – ligand binding. PLoS ONE 14, e0224271, 2019.
9. Voynikov Y, Gevrenova R, Balabanova V, Doytchinova I, Nedialkov P, Zheleva-Dimitrova D. LC-MS analysis of phenolic compounds and oleraceins in aerial parts of Portulaca oleracea L. J Appl. Bot. Food Qual. 92, 298-312, 2019.
10. Atanasova MD, Sasheva P, Yonkova IM, Doytchinova IA.Modelling the interaction and prediction of mictrotubule assembly inhibition of podophyllotoxin and its derivatives by molecular docking.Bul. Chem. Com. 51, 513-520, 2019.
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.
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.
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
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.
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.
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.
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.
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
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
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.
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 + Suppl.
2. Doytchinova, I.A., D.R. Flower: QSAR and the prediction of T-cell epitopes. Current Proteomics, 5(2), 73-95, 2008.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
Department of Chemistry
Faculty of Pharmacy
Medical University of Sofia