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Saranjam L, Nedyalkova M, Fuguet E, Simeonov V, Mas F, Madurga S. Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions. Molecules 2023; 28:5729. [PMID: 37570699 PMCID: PMC10420229 DOI: 10.3390/molecules28155729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log p values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles.
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Affiliation(s)
- Leila Saranjam
- Department of Material Science and Physical Chemistry, Research Institute of Theoretical and Computational Chemistry (IQTCUB), University of Barcelona, C/Martí i Franquès 1, 08028 Barcelona, Spain; (L.S.); (F.M.)
| | - Miroslava Nedyalkova
- Faculty of Chemistry and Pharmacy, University of Sofia “St. Kl. Ohridski”, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria;
| | - Elisabet Fuguet
- Department of Chemical Engineering and Analytical Chemistry, Institute of Biomedicine (IBUB), University of Barcelona, C/Martí i Franquès 1, 08028 Barcelona, Spain;
- Serra Húnter Programme, Generalitat de Catalunya, 08017 Barcelona, Spain
| | - Vasil Simeonov
- Faculty of Chemistry and Pharmacy, University of Sofia “St. Kl. Ohridski”, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria;
| | - Francesc Mas
- Department of Material Science and Physical Chemistry, Research Institute of Theoretical and Computational Chemistry (IQTCUB), University of Barcelona, C/Martí i Franquès 1, 08028 Barcelona, Spain; (L.S.); (F.M.)
| | - Sergio Madurga
- Department of Material Science and Physical Chemistry, Research Institute of Theoretical and Computational Chemistry (IQTCUB), University of Barcelona, C/Martí i Franquès 1, 08028 Barcelona, Spain; (L.S.); (F.M.)
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2
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Sun Y, Hou T, He X, Man VH, Wang J. Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method. J Comput Chem 2023; 44:1300-1311. [PMID: 36820817 PMCID: PMC10101867 DOI: 10.1002/jcc.27086] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/16/2022] [Accepted: 01/29/2023] [Indexed: 02/24/2023]
Abstract
The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.
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Affiliation(s)
- Yuchen Sun
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xibing He
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Viet Hoang Man
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Correa GB, Maciel JCSL, Tavares FW, Abreu CRA. A New Formulation for the Concerted Alchemical Calculation of van der Waals and Coulomb Components of Solvation Free Energies. J Chem Theory Comput 2022; 18:5876-5889. [PMID: 36189930 DOI: 10.1021/acs.jctc.2c00563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Alchemical free energy calculations via molecular dynamics have been widely used to obtain thermodynamic properties related to protein-ligand binding and solute-solvent interactions. Although soft-core modeling is the most common approach, the linear basis function (LBF) methodology [Naden, L. N.; et al. J. Chem. Theory Comput.2014, 10 (3), 1128; 2015, 11 (6), 2536] has emerged as a suitable alternative. It overcomes the end-point singularity of the scaling method while maintaining essential advantages such as ease of implementation and high flexibility for postprocessing analysis. In the present work, we propose a simple LBF variant and formulate an efficient protocol for evaluating van der Waals and Coulomb components of an alchemical transformation in tandem, in contrast to the prevalent sequential evaluation mode. To validate our proposal, which results from a careful optimization study, we performed solvation free energy calculations and obtained octanol-water partition coefficients of small organic molecules. Comparisons with results obtained via the sequential mode using either another LBF approach or the soft-core model attest to the effectiveness and correctness of our method. In addition, we show that a reaction field model with an infinite dielectric constant can provide very accurate hydration free energies when used instead of a lattice-sum method to model solute-solvent electrostatics.
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Affiliation(s)
- Gabriela B Correa
- Chemical Engineering Program, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
| | - Jéssica C S L Maciel
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
| | - Frederico W Tavares
- Chemical Engineering Program, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil.,Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
| | - Charlles R A Abreu
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
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4
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Waibl F, Kraml J, Hoerschinger VJ, Hofer F, Kamenik AS, Fernández-Quintero ML, Liedl KR. Grid inhomogeneous solvation theory for cross-solvation in rigid solvents. J Chem Phys 2022; 156:204101. [PMID: 35649837 DOI: 10.1063/5.0087549] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Grid Inhomogeneous Solvation Theory (GIST) has proven useful to calculate localized thermodynamic properties of water around a solute. Numerous studies have leveraged this information to enhance structure-based binding predictions. We have recently extended GIST toward chloroform as a solvent to allow the prediction of passive membrane permeability. Here, we further generalize the GIST algorithm toward all solvents that can be modeled as rigid molecules. This restriction is inherent to the method and is already present in the inhomogeneous solvation theory. Here, we show that our approach can be applied to various solvent molecules by comparing the results of GIST simulations with thermodynamic integration (TI) calculations and experimental results. Additionally, we analyze and compare a matrix consisting of 100 entries of ten different solvent molecules solvated within each other. We find that the GIST results are highly correlated with TI calculations as well as experiments. For some solvents, we find Pearson correlations of up to 0.99 to the true entropy, while others are affected by the first-order approximation more strongly. The enthalpy-entropy splitting provided by GIST allows us to extend a recently published approach, which estimates higher order entropies by a linear scaling of the first-order entropy, to solvents other than water. Furthermore, we investigate the convergence of GIST in different solvents. We conclude that our extension to GIST reliably calculates localized thermodynamic properties for different solvents and thereby significantly extends the applicability of this widely used method.
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Affiliation(s)
- Franz Waibl
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Johannes Kraml
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Valentin J Hoerschinger
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Florian Hofer
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Anna S Kamenik
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Monica L Fernández-Quintero
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Klaus R Liedl
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
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5
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Falcioni F, Kalayan J, Henchman RH. Energy-entropy prediction of octanol-water logP of SAMPL7 N-acyl sulfonamide bioisosters. J Comput Aided Mol Des 2021; 35:831-840. [PMID: 34244906 PMCID: PMC8295089 DOI: 10.1007/s10822-021-00401-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/17/2021] [Indexed: 12/23/2022]
Abstract
Partition coefficients quantify a molecule's distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol-water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute.
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Affiliation(s)
- Fabio Falcioni
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
| | - Jas Kalayan
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Richard H Henchman
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
- Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
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Donyapour N, Hirn MJ, Dickson A. ClassicalGSG: Prediction of log P using classical molecular force fields and geometric scattering for graphs. J Comput Chem 2021; 42:1006-1017. [PMID: 33786857 PMCID: PMC8062296 DOI: 10.1002/jcc.26519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/11/2021] [Accepted: 02/21/2021] [Indexed: 12/15/2022]
Abstract
This work examines methods for predicting the partition coefficient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic attributes are transformed into index-invariant molecular features using a recently developed method called geometric scattering for graphs (GSG). We call this approach "ClassicalGSG" and examine its performance under a broad range of conditions and hyperparameters. We train ClassicalGSG log P predictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the log P values from four independent test sets. The ClassicalGSG method's performance is compared to a baseline model that employs graph convolutional networks. Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized force field and 2D molecular structures.
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Affiliation(s)
- Nazanin Donyapour
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Matthew J. Hirn
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Mathematics, Michigan State University, East Lansing, Michigan, USA
- Center for Quantum Computing, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Alex Dickson
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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8
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Niu XZ, Field JA, Paniego R, Pepel RD, Chorover J, Abrell L, Sierra-Alvarez R. Bioconcentration potential and microbial toxicity of onium cations in photoacid generators. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:8915-8921. [PMID: 33400114 DOI: 10.1007/s11356-020-12250-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/26/2020] [Indexed: 06/12/2023]
Abstract
Despite the widespread utilization of onium salts as photoacid generators (PAGs) in semiconductor photolithography, their environmental, health, and safety (EHS) properties remain poorly understood. The present work reports the bioconcentration potential of five representative onium species (four sulfonium and one iodonium compound) by determining the octanol-water partition coefficient (POW) and lipid membrane affinity coefficient (KMA); microbial toxicity was evaluated using the bioluminescent bacterium Aliivibrio fischeri (Microtox bioassay). Four of the oniums exhibited varying degrees of hydrophobic (lipophilic) partitioning (log POW: 0.08-4.12; KMA: 1.70-5.62). A strong positive linear correlation was observed between log POW and KMA (KMA = log POW + 1.76, R2 = 0.99). The bioconcentration factors (log BCF) estimated from POW and KMA for the four oniums ranged from 0.13 to 3.67 L kg-1. Bis-(4-tert-butyl phenyl)-iodonium and triphenylsulfonium had 50% inhibitory concentrations (IC50) of 4.8 and 84.6 μM, whereas the IC50 values of the other three oniums were not determined because these values were higher than their aqueous solubility. Given the increased regulatory scrutiny regarding the use and potential health impacts from onium PAGs, this study fulfills critical knowledge gaps concerning the EHS properties of PAG oniums, enabling more comprehensive evaluation of their environmental impacts and potential risk management strategies.
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Affiliation(s)
- Xi-Zhi Niu
- Department of Chemical & Environmental Engineering, The University of Arizona, 1133 James E. Rogers Way, P.O. Box 210011, Tucson, AZ, 85721, USA
- Department of Environmental Science & Arizona Laboratory for Emerging Contaminants, The University of Arizona, Tucson, AZ, 85721, USA
| | - Jim A Field
- Department of Chemical & Environmental Engineering, The University of Arizona, 1133 James E. Rogers Way, P.O. Box 210011, Tucson, AZ, 85721, USA
| | - Rodrigo Paniego
- Department of Chemical & Environmental Engineering, The University of Arizona, 1133 James E. Rogers Way, P.O. Box 210011, Tucson, AZ, 85721, USA
| | - Richard D Pepel
- Department of Chemical & Environmental Engineering, The University of Arizona, 1133 James E. Rogers Way, P.O. Box 210011, Tucson, AZ, 85721, USA
| | - Jon Chorover
- Department of Environmental Science & Arizona Laboratory for Emerging Contaminants, The University of Arizona, Tucson, AZ, 85721, USA
| | - Leif Abrell
- Department of Environmental Science & Arizona Laboratory for Emerging Contaminants, The University of Arizona, Tucson, AZ, 85721, USA
| | - Reyes Sierra-Alvarez
- Department of Chemical & Environmental Engineering, The University of Arizona, 1133 James E. Rogers Way, P.O. Box 210011, Tucson, AZ, 85721, USA.
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Kraml J, Hofer F, Kamenik AS, Waibl F, Kahler U, Schauperl M, Liedl KR. Solvation Thermodynamics in Different Solvents: Water-Chloroform Partition Coefficients from Grid Inhomogeneous Solvation Theory. J Chem Inf Model 2020; 60:3843-3853. [PMID: 32639731 PMCID: PMC7460078 DOI: 10.1021/acs.jcim.0c00289] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Indexed: 11/28/2022]
Abstract
Reliable information on partition coefficients plays a key role in drug development, as solubility decisively affects bioavailability. In a physicochemical context, the partition coefficient of a solute between two different solvents can be described as a function of solvation free energies. Hence, substantial scientific efforts have been made toward accurate predictions of solvation free energies in various solvents. The grid inhomogeneous solvation theory (GIST) facilitates the calculation of solvation free energies. In this study, we introduce an extended version of the GIST algorithm, which enables the calculation for chloroform in addition to water. Furthermore, GIST allows localization of enthalpic and entropic contributions. We test our approach by calculating partition coefficients between water and chloroform for a set of eight small molecules. We report a Pearson correlation coefficient of 0.96 between experimentally determined and calculated partition coefficients. The capability to reliably predict partition coefficients between water and chloroform and the possibility to localize their contributions allow the optimization of a compound's partition coefficient. Therefore, we presume that this methodology will be of great benefit for the efficient development of pharmaceuticals.
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Affiliation(s)
- Johannes Kraml
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Florian Hofer
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Franz Waibl
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Ursula Kahler
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Michael Schauperl
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Klaus R. Liedl
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
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10
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 351] [Impact Index Per Article: 70.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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11
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Assessment of the chromatographic lipophilicity of eight cephalosporins on different stationary phases. Eur J Pharm Sci 2017; 101:115-124. [DOI: 10.1016/j.ejps.2017.01.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 01/23/2017] [Accepted: 01/25/2017] [Indexed: 11/17/2022]
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12
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13
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Alvarsson J, Lampa S, Schaal W, Andersson C, Wikberg JES, Spjuth O. Large-scale ligand-based predictive modelling using support vector machines. J Cheminform 2016; 8:39. [PMID: 27516811 PMCID: PMC4980776 DOI: 10.1186/s13321-016-0151-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 07/12/2016] [Indexed: 12/25/2022] Open
Abstract
The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.
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Affiliation(s)
- Jonathan Alvarsson
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden
| | - Wesley Schaal
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden
| | - Claes Andersson
- Department of Medical Sciences, Uppsala University, 751 85 Uppsala, Sweden
| | - Jarl E S Wikberg
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden
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14
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Bannan CC, Calabró G, Kyu DY, Mobley DL. Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water. J Chem Theory Comput 2016; 12:4015-24. [PMID: 27434695 PMCID: PMC5053177 DOI: 10.1021/acs.jctc.6b00449] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Partition coefficients describe how a solute is distributed between two immiscible solvents. They are used in drug design as a measure of a solute's hydrophobicity and a proxy for its membrane permeability. We calculate partition coefficients from transfer free energies using molecular dynamics simulations in explicit solvent. Setup is done by our new Solvation Toolkit which automates the process of creating input files for any combination of solutes and solvents for many popular molecular dynamics software packages. We calculate partition coefficients between octanol/water and cyclohexane/water with the Generalized AMBER Force Field (GAFF) and the Dielectric Corrected GAFF (GAFF-DC). With similar methods in the past we found a root-mean-squared error (RMSE) of 6.3 kJ/mol in hydration free energies which would correspond to an error of around 1.6 log units in partition coefficients if solvation free energies in both solvents were estimated with comparable accuracy. Here we find an overall RMSE of about 1.2 log units with both force fields. Results from GAFF and GAFF-DC seem to exhibit systematic biases in opposite directions for calculated cyclohexane/water partition coefficients.
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Affiliation(s)
| | - Gaetano Calabró
- Department of Pharmaceutical Sciences, University of California, Irvine
| | - Daisy Y. Kyu
- Department of Pharmaceutical Sciences, University of California, Irvine
| | - David L. Mobley
- Department of Chemistry, University of California, Irvine
- Department of Pharmaceutical Sciences, University of California, Irvine
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15
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Chen G, Li Z, Chen L, Ji S, Shen W. Synthesis and properties of Alkyl α-D-Galactopyranoside. J DISPER SCI TECHNOL 2016. [DOI: 10.1080/01932691.2016.1180628] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Guoyong Chen
- College of Chemistry, Key Laboratory of Environmentally Friendly Chemistry and Application of Ministry of Education, Xiangtan University, Xiangtan, Hunan, People's Republic of China
| | - Zhencao Li
- College of Chemistry, Key Laboratory of Environmentally Friendly Chemistry and Application of Ministry of Education, Xiangtan University, Xiangtan, Hunan, People's Republic of China
| | - Langqiu Chen
- College of Chemistry, Key Laboratory of Environmentally Friendly Chemistry and Application of Ministry of Education, Xiangtan University, Xiangtan, Hunan, People's Republic of China
| | - Shanwei Ji
- College of Chemistry, Key Laboratory of Environmentally Friendly Chemistry and Application of Ministry of Education, Xiangtan University, Xiangtan, Hunan, People's Republic of China
| | - Wangzhen Shen
- College of Chemistry, Key Laboratory of Environmentally Friendly Chemistry and Application of Ministry of Education, Xiangtan University, Xiangtan, Hunan, People's Republic of China
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16
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Hua XW, Chen MG, Zhou S, Zhang DK, Liu M, Zhou S, Liu JB, Lei K, Song HB, Li YH, Gu YC, Li ZM. Research on controllable degradation of sulfonylurea herbicides. RSC Adv 2016. [DOI: 10.1039/c5ra25765d] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Through studying structure, bioassay and soil degradation tri-factor relationship, potential controllable degradation of SU was firstly explored and summarized.
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17
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Passalacqua TG, Dutra LA, de Almeida L, Velásquez AMA, Torres FAE, Yamasaki PR, dos Santos MB, Regasini LO, Michels PAM, Bolzani VDS, Graminha MAS. Synthesis and evaluation of novel prenylated chalcone derivatives as anti-leishmanial and anti-trypanosomal compounds. Bioorg Med Chem Lett 2015; 25:3342-5. [PMID: 26055530 DOI: 10.1016/j.bmcl.2015.05.072] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 05/19/2015] [Accepted: 05/21/2015] [Indexed: 01/03/2023]
Abstract
Chalcones form a class of compounds that belong to the flavonoid family and are widely distributed in plants. Their simple structure and the ease of preparation make chalcones attractive scaffolds for the synthesis of a large number of derivatives enabling the evaluation of the effects of different functional groups on biological activities. In this Letter, we report the successful synthesis of a series of novel prenylated chalcones via Claisen-Schmidt condensation and the evaluation of their effect on the viability of the Trypanosomatidae parasites Leishmania amazonensis, Leishmania infantum and Trypanosoma cruzi.
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Affiliation(s)
- Thais Gaban Passalacqua
- Instituto de Química, UNESP, Araraquara, SP 14800-060, Brazil; Programa de Pós Graduação em Biotecnologia, Brazil
| | - Luiz Antonio Dutra
- Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista, UNESP, Araraquara, SP 14801-902, Brazil; Programa de Pós Graduação em Ciências Farmacêuticas, Brazil
| | - Letícia de Almeida
- Instituto de Química, UNESP, Araraquara, SP 14800-060, Brazil; Programa de Pós Graduação em Biotecnologia, Brazil
| | | | - Fabio Aurelio Esteves Torres
- Instituto de Química, UNESP, Araraquara, SP 14800-060, Brazil; Programa de Pós Graduação em Biotecnologia, Brazil
| | - Paulo Renato Yamasaki
- Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista, UNESP, Araraquara, SP 14801-902, Brazil
| | - Mariana Bastos dos Santos
- Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista, UNESP, São José do Rio Preto, SP 15054-000, Brazil
| | - Luis Octavio Regasini
- Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista, UNESP, São José do Rio Preto, SP 15054-000, Brazil
| | - Paul A M Michels
- Institute of Structural and Molecular Biology, University of Edinburgh, UK
| | | | - Marcia A S Graminha
- Programa de Pós Graduação em Biotecnologia, Brazil; Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista, UNESP, Araraquara, SP 14801-902, Brazil.
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18
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Visconti A, Ermondi G, Caron G, Esposito R. Prediction and interpretation of the lipophilicity of small peptides. J Comput Aided Mol Des 2015; 29:361-70. [PMID: 25577035 DOI: 10.1007/s10822-015-9829-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 01/02/2015] [Indexed: 01/28/2023]
Abstract
Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict [Formula: see text] of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict [Formula: see text] of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.
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Affiliation(s)
- Alessia Visconti
- Department of Genomics of Common Disease, Imperial College London, Du Cane Road, W12 ONN, London, UK,
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19
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Jiang HX, Zhuang DM, Huang Y, Cao XX, Yao JH, Li JY, Wang JY, Zhang C, Jiang B. Design, synthesis, and biological evaluation of novel trifluoromethyl indoles as potent HIV-1 NNRTIs with an improved drug resistance profile. Org Biomol Chem 2014; 12:3446-58. [PMID: 24752610 DOI: 10.1039/c3ob42186d] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
A novel series of trifluoromethyl indole derivatives have been designed, synthesized and evaluated for anti-HIV-1 activities in MT-2 cells. The hydrophobic constant, acute toxicity, carcinogenicity and mutagenicity were predicted. Trifluoromethyl indoles 10i and 10k showed extremely promising activities against WT HIV-1 with IC50 values at the low nanomolar level, similar to efavirenz, better than nevirapine, and also possessed higher potency towards the drug-resistant mutant strain Y181C than nevirapine. Preliminary SAR and docking studies of detailed binding mode provided some insights for discovery of more potent NNRTIs.
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Affiliation(s)
- Hai-Xia Jiang
- CAS Key Laboratory of Synthetic Chemistry of Natural Substances, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, China.
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20
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Zhang C, Zhuang DM, Li J, Chen SY, Du XL, Wang JY, Li JY, Jiang B, Yao JH. Diverse reactivity in microwave-promoted catalyst-free coupling of substituted anilines with ethyl trifluoropyruvate and biological evaluation. Org Biomol Chem 2013; 11:5621-33. [DOI: 10.1039/c3ob40650d] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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21
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Dąbrowska M, Starek M, Skuciński J. Lipophilicity study of some non-steroidal anti-inflammatory agents and cephalosporin antibiotics: A review. Talanta 2011; 86:35-51. [DOI: 10.1016/j.talanta.2011.09.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2011] [Revised: 09/05/2011] [Accepted: 09/12/2011] [Indexed: 02/03/2023]
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22
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Cheng T, Li Q, Wang Y, Bryant SH. Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection. J Chem Inf Model 2011; 51:229-36. [PMID: 21214224 PMCID: PMC3047290 DOI: 10.1021/ci100364a] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.
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Affiliation(s)
- Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
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23
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Wang M, Zhu R, Fan Z, Fu Y, Feng L, Yao J, Maggiani A, Xia Y, Qu F, Peng L. Bitriazolyl acyclonucleosides synthesized via Huisgen reaction using internal alkynes show antiviral activity against tobacco mosaic virus. Bioorg Med Chem Lett 2011; 21:354-7. [DOI: 10.1016/j.bmcl.2010.10.141] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2010] [Revised: 10/29/2010] [Accepted: 10/31/2010] [Indexed: 11/28/2022]
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24
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Liao Q, Wang J, Webster Y, Watson IA. GPU Accelerated Support Vector Machines for Mining High-Throughput Screening Data. J Chem Inf Model 2009; 49:2718-25. [DOI: 10.1021/ci900337f] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Quan Liao
- ChemExplorer Co. Ltd., 965 Halei Road, Shanghai 201203, People’s Republic of China, and Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285
| | - Jibo Wang
- ChemExplorer Co. Ltd., 965 Halei Road, Shanghai 201203, People’s Republic of China, and Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285
| | - Yue Webster
- ChemExplorer Co. Ltd., 965 Halei Road, Shanghai 201203, People’s Republic of China, and Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285
| | - Ian A. Watson
- ChemExplorer Co. Ltd., 965 Halei Road, Shanghai 201203, People’s Republic of China, and Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285
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25
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26
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Chen HF. In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression. Chem Biol Drug Des 2009; 74:142-7. [PMID: 19549084 DOI: 10.1111/j.1747-0285.2009.00840.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.
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Affiliation(s)
- Hai-Feng Chen
- College of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China.
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27
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Three-class classification models of logS and logP derived by using GA–CG–SVM approach. Mol Divers 2009; 13:261-8. [DOI: 10.1007/s11030-009-9108-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2008] [Accepted: 01/09/2009] [Indexed: 10/21/2022]
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28
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CHEN L, HUANG Y, XIE W, CAO J, NI C, SHEN Z, LI X, ZHANG Y, SHEN T, YU F, LIU B, YUAN L, YAO J. Synthesis, QSAR Study and Optimization of Propiophenone Oxime Derivatives. CHINESE J CHEM 2009. [DOI: 10.1002/cjoc.200990023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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29
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Liao Q, Yao J, Yuan S. Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines. Mol Divers 2007; 11:59-72. [PMID: 17440826 DOI: 10.1007/s11030-007-9057-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2006] [Accepted: 02/06/2007] [Indexed: 01/04/2023]
Abstract
The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure-toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL's toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.
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Affiliation(s)
- Quan Liao
- Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 354, Fenglin Road, Shanghai 200032, China
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