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Computational Methods in Medicinal Chemistry: Mechanistic Investigations and Virtual Screening Development
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Computational methods have become an integral part of drug development and can help bring new and better drugs to the market faster. The process of predicting the biological activity of large compound collections is known as virtual screening, and has been instrumental in the development of several drugs today in the market. Computational methods can also be used to elucidate the energies associated with chemical reactivity and predict how to improve a synthetic protocol. These two applications of computational medicinal chemistry is the focus of this thesis.

In the first part of this work, quantum mechanics has been used to probe the energy surface of palladium(II)-catalyzed decarboxylative reactions in order to gain a better understating of these systems (paper I-III). These studies have mapped the reaction pathways and been able to make accurate predictions that were verified experimentally.

The other focus of this work has been to develop virtual screening methodology. Our first study in the area (paper IV) investigated if the results from several virtual screening methods could be combined using data fusion techniques in order to get a more consistent result and better performance. The study showed that the results obtained from data fusion were more consistent than the results from any single method. The data fusion methods also for several target had a better performance than any of the included single methods.

Next, we developed a dataset suitable for evaluating the performance of virtual screening methods when applied to large compound collection as a replacement or complement for high throughput screening (paper V). This is the first benchmark dataset of its kind.

Finally, a method for using computationally derived reaction coordinates as basis for virtual screening was developed. The aim was to find inhibitors that resemble key steps in the mechanism (paper VI). This initial proof of concept study managed to locate several known and one previously not reported reaction mimetics against insulin regulated amino peptidase.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. , 65 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 201
Keyword [en]
DFT, IRAP, Virtual Screening, Catalysis, Palladium
National Category
Medicinal Chemistry Organic Chemistry
Research subject
Medicinal Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-259443ISBN: 978-91-554-9293-9 (print)OAI: oai:DiVA.org:uu-259443DiVA: diva2:844370
Public defence
2015-09-25, A1:107a, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2015-09-03 Created: 2015-08-04 Last updated: 2015-10-01
List of papers
1. Theoretical and Experimental Investigation of Palladium(II)-Catalyzed Decarboxylative Addition of Arenecarboxylic Acid to Nitrile
Open this publication in new window or tab >>Theoretical and Experimental Investigation of Palladium(II)-Catalyzed Decarboxylative Addition of Arenecarboxylic Acid to Nitrile
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2013 (English)In: Organometallics, ISSN 0276-7333, E-ISSN 1520-6041, Vol. 32, no 2, 490-497 p.Article in journal (Refereed) Published
Abstract [en]

The reaction mechanism of palladium(II)-catalyzed decarboxylative addition of 2,6-dimethoxybenzoic acid to acetonitrile was investigated by means of density functional theory (DFT) calculations. Calculations of the free energy profile for decarboxylation and carbopalladation indicated carbopalladation as the rate-determining step of the reaction. Investigation of the free energy profile for a series of experimentally evaluated nitrogen-based bidentate palladium ligands revealed that higher energy is required for decarboxylation and carbopalladation employing the experimentally least efficient ligand. The DFT investigation also showed that the relative free energies of the transition states were lowered in polar solvent, and preparative experiments confirmed that a nonoptimal ligand could be greatly improved by addition of water to the reaction system.

National Category
Medical and Health Sciences Chemical Sciences
Identifiers
urn:nbn:se:uu:diva-196041 (URN)10.1021/om3009525 (DOI)000314332100017 ()
Available from: 2013-03-04 Created: 2013-03-04 Last updated: 2017-12-06Bibliographically approved
2. Decarboxylative Palladium(II)-Catalyzed Synthesis of Aryl Amidines from Aryl Carboxylic Acids: Development and Mechanistic Investigation
Open this publication in new window or tab >>Decarboxylative Palladium(II)-Catalyzed Synthesis of Aryl Amidines from Aryl Carboxylic Acids: Development and Mechanistic Investigation
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2013 (English)In: Chemistry - A European Journal, ISSN 0947-6539, E-ISSN 1521-3765, Vol. 19, no 41, 13803-13810 p.Article in journal (Refereed) Published
Abstract [en]

A fast and convenient synthesis of aryl amidines starting from carboxylic acids and cyanamides is reported. The reaction was achieved by palladium(II)-catalysis in a one-step microwave protocol using [Pd(O2CCF3)(2)], 6-methyl-2,2-bipyridyl and trifluoroacetic acid (TFA) in N-methylpyrrolidinone (NMP), providing the corresponding aryl amidines in moderate to excellent yields. The protocol is very robust with regards to the cyanamide coupling partner but requires electron-rich ortho-substituted aryl carboxylic acids. Mechanistic insight was provided by a DFT investigation and direct ESI-MS studies of the reaction. The results of the DFT study correlated well with the experimental findings and, together with the ESI-MS study, support the suggested mechanism. Furthermore, a scale-out (scale-up) was performed with a non-resonant microwave continuous-flow system, achieving a maximum throughput of 11mmolh(-1) by using a glass reactor with an inner diameter of 3mm at a flow rate of 1mLmin(-1).

Keyword
decarboxylation, density functional calculations, mass spectrometry, microwave chemistry, palladium
National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-210180 (URN)10.1002/chem.201301809 (DOI)000325135800026 ()
Available from: 2013-11-04 Created: 2013-11-04 Last updated: 2017-12-06Bibliographically approved
3. Mechanistic Investigation of Palladium(II)-Catalyzed Decarboxylative Synthesis of Electron Rich Styrenes and 1,1-Diarylethenes
Open this publication in new window or tab >>Mechanistic Investigation of Palladium(II)-Catalyzed Decarboxylative Synthesis of Electron Rich Styrenes and 1,1-Diarylethenes
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(English)Manuscript (preprint) (Other academic)
Keyword
palladium, DFT, mechanism, styrene
National Category
Organic Chemistry
Identifiers
urn:nbn:se:uu:diva-259441 (URN)
Available from: 2015-08-04 Created: 2015-08-04 Last updated: 2015-10-01
4. Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods
Open this publication in new window or tab >>Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods
2012 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 52, no 1, 225-232 p.Article in journal (Refereed) Published
Abstract [en]

Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.

National Category
Medicinal Chemistry
Identifiers
urn:nbn:se:uu:diva-169381 (URN)10.1021/ci2004835 (DOI)000299351600021 ()
Available from: 2012-02-28 Created: 2012-02-28 Last updated: 2015-10-01Bibliographically approved
5. Toward a Benchmarking Data Set Able to Evaluate Ligand- and Structure-based Virtual Screening Using Public HTS Data
Open this publication in new window or tab >>Toward a Benchmarking Data Set Able to Evaluate Ligand- and Structure-based Virtual Screening Using Public HTS Data
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2015 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 55, no 2, 343-353 p.Article in journal (Refereed) Published
Abstract [en]

Virtual screening has the potential to accelerate and reduce costs of probe development and drug discovery. To develop and benchmark virtual screening methods, validation data sets are commonly used. Over the years, such data sets have been constructed to overcome the problems of analogue bias and artificial enrichment. With the rapid growth of public domain databases containing high-throughput screening data, such as the PubChem BioAssay database, there is an increased possibility to use such data for validation. In this study, we identify PubChem data sets suitable for validation of both structure- and ligand-based virtual screening methods. To achieve this, high-throughput screening data for which a crystal structure of the bioassay target was available in the PDB were identified. Thereafter, the data sets were inspected to identify structures and data suitable for use in validation studies. In this work, we present seven data sets (MMP13, DUSP3, PTPN22, EPHX2, CTDSP1, MAPK10, and CDK5) compiled using this method. In the seven data sets, the number of active compounds varies between 19 and 369 and the number of inactive compounds between 59 405 and 337 634. This gives a higher ratio of the number of inactive to active compounds than what is found in most benchmark data sets. We have also evaluated the screening performance using docking and 3D shape similarity with default settings. To characterize the data sets, we used physicochemical similarity and 2D fingerprint searches. We envision that these data sets can be a useful complement to current data sets used for method evaluation.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2015
National Category
Structural Biology Pharmaceutical Chemistry
Research subject
Chemistry with specialization in Bioorganic Chemistry
Identifiers
urn:nbn:se:uu:diva-248018 (URN)10.1021/ci5005465 (DOI)000349943100014 ()25564966 (PubMedID)
Available from: 2015-03-26 Created: 2015-03-26 Last updated: 2017-08-24Bibliographically approved
6. Virtual Screening for Transition State Analogue Inhibitors of IRAP Based on Quantum Mechanically Derived Reaction Coordinates
Open this publication in new window or tab >>Virtual Screening for Transition State Analogue Inhibitors of IRAP Based on Quantum Mechanically Derived Reaction Coordinates
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2015 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-960X, Vol. 55, no 9, 1984-1993 p.Article in journal (Refereed) Published
Abstract [en]

Transition state- and high energy intermediate mimetics have the potential to be very potent enzyme inhibitors. In this study a model of peptide hydrolysis in the active site of insulin-regulated aminopeptidase (IRAP) was developed using density functional theory calculations and the cluster approach. The 3D structure models of the reaction coordinates were used for virtual screening to obtain new chemical starting points for IRAP inhibitors. This mechanism-based virtual screening process managed to identify several known peptidase inhibitors from a library of over five million compounds and biological testing identified one compound not previously reported as an IRAP inhibitor. This novel methodology for virtual screening is a promising approach to identify new inhibitors mimicking key transition states or intermediates of an enzymatic reaction.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2015
National Category
Medicinal Chemistry
Identifiers
urn:nbn:se:uu:diva-259442 (URN)10.1021/acs.jcim.5b00359 (DOI)000362056900018 ()26252078 (PubMedID)
Funder
Carl Tryggers foundation Swedish Research Council
Available from: 2015-08-05 Created: 2015-08-04 Last updated: 2015-11-04Bibliographically approved

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