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Kim T, Chung KC, Park H. Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm. Pharmaceuticals (Basel) 2023; 16:1509. [PMID: 38004375 PMCID: PMC10675541 DOI: 10.3390/ph16111509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.
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Affiliation(s)
| | - Kee-Choo Chung
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Republic of Korea;
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Republic of Korea;
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2
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Derbali I, Aroule O, Hoffmann G, Thissen R, Alcaraz C, Romanzin C, Zins EL. On the relevance of the electron density analysis for the study of micro-hydration and its impact on the formation of a peptide-like bond. Theor Chem Acc 2022. [DOI: 10.1007/s00214-022-02893-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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3
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Kim T, You BH, Han S, Shin HC, Chung KC, Park H. Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage. Int J Mol Sci 2021; 22:ijms222010995. [PMID: 34681653 PMCID: PMC8537149 DOI: 10.3390/ijms222010995] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 01/07/2023] Open
Abstract
A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.
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Affiliation(s)
- Taeho Kim
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
| | - Byoung Hoon You
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Songhee Han
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Ho Chul Shin
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Kee-Choo Chung
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
- Correspondence: (K.-C.C.); (H.P.); Tel.: +82-2-2963-1635 (K.-C.C.); +82-2-3408-3766 (H.P.); Fax: +82-2-3408-4334 (K.-C.C. & H.P.)
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
- Correspondence: (K.-C.C.); (H.P.); Tel.: +82-2-2963-1635 (K.-C.C.); +82-2-3408-3766 (H.P.); Fax: +82-2-3408-4334 (K.-C.C. & H.P.)
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Current and Future Challenges in Modern Drug Discovery. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2114:1-17. [PMID: 32016883 DOI: 10.1007/978-1-0716-0282-9_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.
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5
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Hassanzadeh P. Towards the quantum-enabled technologies for development of drugs or delivery systems. J Control Release 2020; 324:260-279. [DOI: 10.1016/j.jconrel.2020.04.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 12/20/2022]
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6
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Zins EL. Microhydration of a Carbonyl Group: How does the Molecular Electrostatic Potential (MESP) Impact the Formation of (H 2O) n:(R 2C═O)Complexes? J Phys Chem A 2020; 124:1720-1734. [PMID: 32049521 DOI: 10.1021/acs.jpca.9b09992] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The presence of a carbonyl group in a molecule usually leads to the identification of a π-hole on the molecular electrostatic potential (MESP) of the species. How does this electrophilic site influence the formation of microhydrated complexes? To address this point, a panel of R2CO solutes with various MESPs was selected, and we identified the structures and properties of several complexes containing one, two, three and six water molecules. The following solutes were considered in the present study: H2CO, F2CO, Cl2CO,(NC)2CO and H2C═CO. Geometry optimizations and frequency calculations were carried out at the LC-ωPBE/6-311++G(d,p) level, with the GD3BJ empirical correction for dispersion. For a number of n water molecules around the R2CO solute, the structure and the features of the most stable (H2O)n:(R2CO) complexes are highly dependent on the MESP of the isolated R2CO solute. The formation of pi-hole bondings appears to play a decisive role in the initiation of a three-dimensional organization of water molecules around the solute.
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Affiliation(s)
- Emilie-Laure Zins
- De la Molécule aux Nano-Objets: Réactivité, Interactions Spectroscopies, MONARIS, CNRS, Sorbonne Université, 75005, Paris, France
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7
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Schindl A, Hawker RR, Schaffarczyk McHale KS, Liu KTC, Morris DC, Hsieh AY, Gilbert A, Prescott SW, Haines RS, Croft AK, Harper JB, Jäger CM. Controlling the outcome of S N2 reactions in ionic liquids: from rational data set design to predictive linear regression models. Phys Chem Chem Phys 2020; 22:23009-23018. [PMID: 33043942 DOI: 10.1039/d0cp04224b] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Rate constants for a bimolecular nucleophilic substitution (SN2) process in a range of ionic liquids are correlated with calculated parameters associated with the charge localisation on the cation of the ionic liquid (including the molecular electrostatic potential). Simple linear regression models proved effective, though the interdependency of the descriptors needs to be taken into account when considering generality. A series of ionic liquids were then prepared and evaluated as solvents for the same process; this data set was rationally chosen to incorporate homologous series (to evaluate systematic variation) and functionalities not available in the original data set. These new data were used to evaluate and refine the original models, which were expanded to include simple artificial neural networks. Along with showing the importance of an appropriate data set and the perils of overfitting, the work demonstrates that such models can be used to reliably predict ionic liquid solvent effects on an organic process, within the limits of the data set.
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Affiliation(s)
- Alexandra Schindl
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Rebecca R Hawker
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | | | - Kenny T-C Liu
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Daniel C Morris
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia. and School of Chemical Engineering, University of New South Wales, UNSW Sydney, 2052, Australia
| | - Andrew Y Hsieh
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Alyssa Gilbert
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Stuart W Prescott
- School of Chemical Engineering, University of New South Wales, UNSW Sydney, 2052, Australia
| | - Ronald S Haines
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Anna K Croft
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Jason B Harper
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Christof M Jäger
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
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8
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Campos DMO, Bezerra KS, Esmaile SC, Fulco UL, Albuquerque EL, Oliveira JIN. Intermolecular interactions of cn-716 and acyl-KR-aldehyde dipeptide inhibitors against Zika virus. Phys Chem Chem Phys 2020; 22:15683-15695. [DOI: 10.1039/d0cp02254c] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Structural representation and graphic panel showing the most relevant residues that contribute to the ZIKV NS2B–NS3–ligand complexes.
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Affiliation(s)
- Daniel M. O. Campos
- Departamento de Biofísica e Farmacologia
- Universidade Federal do Rio Grande do Norte
- Natal
- Brazil
| | - Katyanna S. Bezerra
- Departamento de Biofísica e Farmacologia
- Universidade Federal do Rio Grande do Norte
- Natal
- Brazil
| | - Stephany C. Esmaile
- Departamento de Biofísica e Farmacologia
- Universidade Federal do Rio Grande do Norte
- Natal
- Brazil
| | - Umberto L. Fulco
- Departamento de Biofísica e Farmacologia
- Universidade Federal do Rio Grande do Norte
- Natal
- Brazil
| | | | - Jonas I. N. Oliveira
- Departamento de Biofísica e Farmacologia
- Universidade Federal do Rio Grande do Norte
- Natal
- Brazil
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9
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Lima Neto JX, Bezerra KS, Barbosa ED, Oliveira JIN, Manzoni V, Soares-Rachetti VP, Albuquerque EL, Fulco UL. Exploring the Binding Mechanism of GABAB Receptor Agonists and Antagonists through in Silico Simulations. J Chem Inf Model 2019; 60:1005-1018. [DOI: 10.1021/acs.jcim.9b01025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- José X. Lima Neto
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
| | - Katyanna S. Bezerra
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
| | - Emmanuel D. Barbosa
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
| | - Jonas I. N. Oliveira
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
| | - Vinícius Manzoni
- Instituto de Física, Universidade Federal do Alagoas, 57072-970 Maceió-AL, Brazil
| | - Vanessa P. Soares-Rachetti
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
| | - Eudenilson L. Albuquerque
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
| | - Umberto L. Fulco
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970 Natal-RN, Brazil
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10
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Dral PO, Wu X, Thiel W. Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections. J Chem Theory Comput 2019; 15:1743-1760. [PMID: 30735388 PMCID: PMC6416713 DOI: 10.1021/acs.jctc.8b01265] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Indexed: 12/31/2022]
Abstract
We present two new semiempirical quantum-chemical methods with orthogonalization and dispersion corrections: ODM2 and ODM3 (ODM x). They employ the same electronic structure model as the OM2 and OM3 (OM x) methods, respectively. In addition, they include Grimme's dispersion correction D3 with Becke-Johnson damping and three-body corrections E ABC for Axilrod-Teller-Muto dispersion interactions as integral parts. Heats of formation are determined by adding explicitly computed zero-point vibrational energy and thermal corrections, in contrast to standard MNDO-type and OM x methods. We report ODM x parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine that are optimized with regard to a wide range of carefully chosen state-of-the-art reference data. Extensive benchmarks show that the ODM x methods generally perform better than the available MNDO-type and OM x methods for ground-state and excited-state properties, while they describe noncovalent interactions with similar accuracy as OM x methods with a posteriori dispersion corrections.
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Affiliation(s)
- Pavlo O. Dral
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Xin Wu
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Walter Thiel
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
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11
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Choi H, Kang H, Chung KC, Park H. Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties. Phys Chem Chem Phys 2019; 21:5189-5199. [PMID: 30775759 DOI: 10.1039/c8cp07002d] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.
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Affiliation(s)
- Hwanho Choi
- Department of Bioscience and Biotechnology, Sejong University, 209 Neungdong-ro, Kwangjin-gu, Seoul 05006, Korea.
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12
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Fouad MA, Tolba EH, El-Shal MA, El Kerdawy AM. QSRR modeling for the chromatographic retention behavior of some β-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression. J Chromatogr A 2018; 1549:51-62. [DOI: 10.1016/j.chroma.2018.03.042] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 03/15/2018] [Accepted: 03/20/2018] [Indexed: 11/28/2022]
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13
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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14
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Ryde U, Söderhjelm P. Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods. Chem Rev 2016; 116:5520-66. [DOI: 10.1021/acs.chemrev.5b00630] [Citation(s) in RCA: 175] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ulf Ryde
- Department of Theoretical
Chemistry and ‡Department of Biophysical Chemistry, Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Pär Söderhjelm
- Department of Theoretical
Chemistry and ‡Department of Biophysical Chemistry, Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
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15
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Dral P, Wu X, Spörkel L, Koslowski A, Thiel W. Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties. J Chem Theory Comput 2016; 12:1097-120. [PMID: 26771261 PMCID: PMC4785506 DOI: 10.1021/acs.jctc.5b01047] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Indexed: 11/30/2022]
Abstract
The semiempirical orthogonalization-corrected OMx methods (OM1, OM2, and OM3) go beyond the standard MNDO model by including additional interactions in the electronic structure calculation. When augmented with empirical dispersion corrections, the resulting OMx-Dn approaches offer a fast and robust treatment of noncovalent interactions. Here we evaluate the performance of the OMx and OMx-Dn methods for a variety of ground-state properties using a large and diverse collection of benchmark sets from the literature, with a total of 13035 original and derived reference data. Extensive comparisons are made with the results from established semiempirical methods (MNDO, AM1, PM3, PM6, and PM7) that also use the NDDO (neglect of diatomic differential overlap) integral approximation. Statistical evaluations show that the OMx and OMx-Dn methods outperform the other methods for most of the benchmark sets.
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Affiliation(s)
- Pavlo
O. Dral
- Max-Planck-Institut für
Kohlenforschung, Kaiser-Wilhelm-Platz
1, 45470 Mülheim
an der Ruhr, Germany
| | - Xin Wu
- Max-Planck-Institut für
Kohlenforschung, Kaiser-Wilhelm-Platz
1, 45470 Mülheim
an der Ruhr, Germany
| | - Lasse Spörkel
- Max-Planck-Institut für
Kohlenforschung, Kaiser-Wilhelm-Platz
1, 45470 Mülheim
an der Ruhr, Germany
| | - Axel Koslowski
- Max-Planck-Institut für
Kohlenforschung, Kaiser-Wilhelm-Platz
1, 45470 Mülheim
an der Ruhr, Germany
| | - Walter Thiel
- Max-Planck-Institut für
Kohlenforschung, Kaiser-Wilhelm-Platz
1, 45470 Mülheim
an der Ruhr, Germany
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16
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Ginex T, Muñoz-Muriedas J, Herrero E, Gibert E, Cozzini P, Luque FJ. Development and validation of hydrophobic molecular fields derived from the quantum mechanical IEF/PCM-MST solvation models in 3D-QSAR. J Comput Chem 2016; 37:1147-62. [DOI: 10.1002/jcc.24305] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/17/2015] [Accepted: 12/17/2015] [Indexed: 12/15/2022]
Affiliation(s)
- Tiziana Ginex
- Dipartimento Di Scienze Degli Alimenti; University of Parma; Parco Area Delle Scienze 59/a Parma 43121 Italy
| | - Jordi Muñoz-Muriedas
- GlaxoSmithKline; Medicines Research Centre; Gunnels Wood Road Stevenage SG1 2NY United Kingdom
| | - Enric Herrero
- Pharmacelera, Jordi Girona 1-3, Campus Nord Universitat Politècnica De Catalunya; Edifici K2M Barcelona 08034 Spain
| | - Enric Gibert
- Pharmacelera, Jordi Girona 1-3, Campus Nord Universitat Politècnica De Catalunya; Edifici K2M Barcelona 08034 Spain
| | - Pietro Cozzini
- Dipartimento Di Scienze Degli Alimenti; University of Parma; Parco Area Delle Scienze 59/a Parma 43121 Italy
| | - F. J. Luque
- Department of Chemical Physics and Institut De Biomedicina (IBUB), Faculty of Pharmacy; University of Barcelona; Av. Prat De La Riba 171 Santa Coloma De Gramenet 08921 Spain
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17
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20D-dynamic representation of protein sequences. Genomics 2016; 107:16-23. [DOI: 10.1016/j.ygeno.2015.12.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 12/10/2015] [Accepted: 12/14/2015] [Indexed: 11/23/2022]
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18
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Al-Dabbagh MM, Salim N, Himmat M, Ahmed A, Saeed F. A Quantum-Based Similarity Method in Virtual Screening. Molecules 2015; 20:18107-27. [PMID: 26445039 DOI: 10.3390/molecules201018107] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 09/22/2015] [Accepted: 09/23/2015] [Indexed: 11/16/2022] Open
Abstract
One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.
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Affiliation(s)
| | - Naomie Salim
- Faculty of Computing, Universiti Teknologi Malaysia, Skudia 81310, Malaysia.
| | - Mubarak Himmat
- Faculty of Computing, Universiti Teknologi Malaysia, Skudia 81310, Malaysia.
| | - Ali Ahmed
- Faculty of Computing, Universiti Teknologi Malaysia, Skudia 81310, Malaysia.
- Faculty of Engineering, Karary University, Khartoum 12304, Sudan.
| | - Faisal Saeed
- Faculty of Computing, Universiti Teknologi Malaysia, Skudia 81310, Malaysia.
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Venkatraman V, Åstrand PO, Alsberg BK. Quantitative structure-property relationship modeling of Grätzel solar cell dyes. J Comput Chem 2013; 35:214-26. [PMID: 24222335 DOI: 10.1002/jcc.23485] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2013] [Revised: 10/04/2013] [Accepted: 10/11/2013] [Indexed: 11/05/2022]
Abstract
With fossil fuel reserves on the decline, there is increasing focus on the design and development of low-cost organic photovoltaic devices, in particular, dye-sensitized solar cells (DSSCs). The power conversion efficiency (PCE) of a DSSC is heavily influenced by the chemical structure of the dye. However, as far as we know, no predictive quantitative structure-property relationship models for DSSCs with PCE as one of the response variables have been reported. Thus, we report for the first time the successful application of comparative molecular field analysis (CoMFA) and vibrational frequency-based eigenvalue (EVA) descriptors to model molecular structure-photovoltaic performance relationships for a set of 40 coumarin derivatives. The results show that the models obtained provide statistically robust predictions of important photovoltaic parameters such as PCE, the open-circuit voltage (V(OC)), short-circuit current (J(SC)) and the peak absorption wavelength λ(max). Some of our findings based on the analysis of the models are in accordance with those reported in the literature. These structure-property relationships can be applied to the rational structural design and evaluation of new photovoltaic materials.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway
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