1
|
Shen J, Harada Y, Nakamura S. Prediction on Air-Nasal Mucus Partition Coefficients of Odor Compounds. ACS OMEGA 2024; 9:31328-31334. [PMID: 39072110 PMCID: PMC11270682 DOI: 10.1021/acsomega.3c07722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/22/2024] [Accepted: 06/25/2024] [Indexed: 07/30/2024]
Abstract
The air-nasal mucus partition coefficient is a crucial property among all of the interaction mechanisms between odor molecules and olfactory receptors, since this property contributes to our sense of smell. Due to the complexity of the mucus composition, in vivo determination of the air-mucus partition coefficient is a technical challenge. A predictable model of the air-mucus partition coefficient can provide valuable insights into the chemical properties that govern olfactory perception and can help design desired odorants. In this study, we propose a novel model based on the deep-layer neural network (DNN) algorithm to predict the air-mucus partition coefficients for a range of odor compounds. The molecular surface charge density (σ-profile) calculated from the COnductor like Screening MOdel for Real Solvents (COSMO-RS) thermodynamic package was adapted as descriptors of structural features of odor molecules. The results revealed that the air-mucus partition coefficients are highly correlated to the σ-profile of the studied compounds. The information obtained from the study provided interpretable results, which not only help in identifying the molecular features that contribute to the air-mucus partition coefficient of odorants but also aid in the design of compounds with the desired odor properties.
Collapse
Affiliation(s)
- Junwei Shen
- Laboratory for Data Sciences,
Priority Organization for Innovation and Excellence, Kumamoto University, 2-39-1, Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
| | - Yuki Harada
- Laboratory for Data Sciences,
Priority Organization for Innovation and Excellence, Kumamoto University, 2-39-1, Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
| | - Shinichiro Nakamura
- Laboratory for Data Sciences,
Priority Organization for Innovation and Excellence, Kumamoto University, 2-39-1, Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
| |
Collapse
|
2
|
Song Z, Chen J, Cheng J, Chen G, Qi Z. Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications. Chem Rev 2024; 124:248-317. [PMID: 38108629 DOI: 10.1021/acs.chemrev.3c00223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.
Collapse
Affiliation(s)
- Zhen Song
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jiahui Chen
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jie Cheng
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guzhong Chen
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhiwen Qi
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| |
Collapse
|
3
|
Application of atomic electrostatic potential descriptors for predicting the eco-toxicity of ionic liquids towards leukemia rat cell line. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
4
|
Zhang L, Mao H, Zhuang Y, Wang L, Liu L, Dong Y, Du J, Xie W, Yuan Z. Odor prediction and aroma mixture design using machine learning model and molecular surface charge density profiles. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116947] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|
5
|
Nordness O, Kelkar P, Lyu Y, Baldea M, Stadtherr MA, Brennecke JF. Predicting thermophysical properties of dialkylimidazolium ionic liquids from sigma profiles. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
6
|
Kang X, Lv Z, Zhao Y, Chen Z. A QSPR model for estimating Henry's law constant of H2S in ionic liquids by ELM algorithm. CHEMOSPHERE 2021; 269:128743. [PMID: 33139046 DOI: 10.1016/j.chemosphere.2020.128743] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/13/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
Ionic liquids (ILs) as green solvents have been studied in the application of gas sweetening. However, it is a huge challenge to obtain all the experimental values because of the high costs and generated chemical wastes. This study pioneered a quantitative structure-property relationship (QSPR) model for estimating Henry's law constant (HLC) of H2S in ILs. A dataset consisting of the HLC data of H2S for 22 ILs within a wide range of temperature (298.15-363.15 K) were collected from published reports. The electrostatic potential surface area (SEP) and molecular volume of these ILs were calculated and used as input descriptors together with temperature. The extreme learning machine (ELM) algorithm was employed for nonlinear modelling. Results showed that the determination coefficient (R2) of the training set, test set and total set were 0.9996, 0.9989,0.9994, respectively, while the average absolute relative deviation (AARD%) of them were 1.3383, 2,4820 and 1.5820, respectively. The statistical parameters for the measurement of the model exhibited the great reliability, stability, and predictive power of the ELM model. The Applicability Domain (AD) of the ELM model is also investigated. It proves that the majority of ILs in the training and test sets are located in the model's AD and verifies the reliability of the model. The proposed model is potentially applicable to guide the application of ILs for gas sweetening.
Collapse
Affiliation(s)
- Xuejing Kang
- The Key Laboratory of Biotechnology for Medicinal Plants of Jiangsu Province, School of Life, Jiangsu Normal University, Shanghai Road 101, 221116, Xuzhou, China; Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 16500, Prague 6, Czech Republic
| | - Zuopeng Lv
- The Key Laboratory of Biotechnology for Medicinal Plants of Jiangsu Province, School of Life, Jiangsu Normal University, Shanghai Road 101, 221116, Xuzhou, China
| | - Yongsheng Zhao
- Department of Chemical Engineering, University of California, Santa Barbara, CA, 93106-5080, United States.
| | - Zhongbing Chen
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 16500, Prague 6, Czech Republic.
| |
Collapse
|
7
|
Regression Diagnostics with Predicted Residuals of Linear Model with Improved Singular Value Classification Applied to Forecast the Hydrodynamic Efficiency of Wave Energy Converters. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11072990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In the preliminary stages of design of the oscillating water column (OWC) type of wave energy converters (WECs), we need a reliable cost- and time-effective method to predict the hydrodynamic efficiency as a function of the design parameters. One of the cheapest approaches is to create a multiple linear regression (MLR) model using an existing data set. The problem with this approach is that the reliability of the MLR predictions depend on the validity of the regression assumptions, which are either rarely tested or tested using sub-optimal procedures. We offer a series of novel methods for assumption diagnostics that we apply in our case study for MLR prediction of the hydrodynamics efficiency of OWC WECs. Namely, we propose: a novel procedure for reliable identification of the zero singular values of a matrix; a modified algorithm for stepwise regression; a modified algorithm to detect heteroskedasticity and identify statistically significant but practically insignificant heteroscedasticity in the original model; a novel test of the validity of the nullity assumption; a modified Jarque–Bera Monte Carlo error normality test. In our case study, the deviations from the assumptions of the classical normal linear regression model were fully diagnosed and dealt with. The newly proposed algorithms based on improved singular value decomposition (SVD) of the design matrix and on predicted residuals were successfully tested with a new family of goodness-of-fit measures. We empirically investigated the correct placement of an elaborate outlier detection procedure in the overall diagnostic sequence. As a result, we constructed a reliable MLR model to predict the hydrodynamic efficiency in the preliminary stages of design. MLR is a useful tool at the preliminary stages of design and can produce highly reliable and time-effective predictions of the OWC WEC performance provided that the constructing and diagnostic procedures are modified to reflect the latest advances in statistics. The main advantage of MLR models compared to other modern black box models is that their assumptions are known and can be tested in practice, which increases the reliability of the model predictions.
Collapse
|
8
|
Dai Z, Chen Y, Liu C, Lu X, Liu Y, Ji X. Prediction and verification of heat capacities for pure ionic liquids. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.10.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
9
|
Kang X, Chen Z, Zhao Y. Assessing the ecotoxicity of ionic liquids on Vibrio fischeri using electrostatic potential descriptors. JOURNAL OF HAZARDOUS MATERIALS 2020; 397:122761. [PMID: 32388091 DOI: 10.1016/j.jhazmat.2020.122761] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 06/11/2023]
Abstract
Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (SEP) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R2), the average absolute error (AAE%) and the root-mean-square error (RMSE) of the developed model are 0.9272, 0.2101 and 0.3262 for the entire set, respectively. The proposed approach based on the high R2 and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri.
Collapse
Affiliation(s)
- Xuejing Kang
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences, Prague 16521, Prague 6, Czech Republic
| | - Zhongbing Chen
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences, Prague 16521, Prague 6, Czech Republic.
| | - Yongsheng Zhao
- Department of Chemical Engineering, University of California, Santa Barbara, CA, 93106-5080, USA.
| |
Collapse
|
10
|
Kang X, Lv Z, Chen Z, Zhao Y. Prediction of ammonia absorption in ionic liquids based on extreme learning machine modelling and a novel molecular descriptor S EP. ENVIRONMENTAL RESEARCH 2020; 189:109951. [PMID: 32777637 DOI: 10.1016/j.envres.2020.109951] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/07/2020] [Accepted: 07/12/2020] [Indexed: 06/11/2023]
Abstract
The large amounts of ammonia emissions generated from industrial production have caused serious environmental pollution problems, such as soil acidification, eutrophication, the formation of fine particles and changes in the global greenhouse balance, and also greatly endanger human health. At present, effectively reducing ammonia emissions or recovering ammonia is still a huge challenge. Ionic liquids (ILs) as a new class of green solvent have been introduced for ammonia absorption with great potential, but a huge number on combination systems of ILs lead to the difficulty of measuring the ammonia solubility in all ILs by experiments (e.g., danger and cost). Hereby, this study proposed a novel approach for estimating the ammonia solubility in different ILs. A predictive model was developed based on the novel Algorithm - extreme learning machine (ELM) and the molecular descriptors of electrostatic potential surface areas (SEP) as input parameters. Besides, 502 data points of ammonia solubility in 17 ILs were gathered with a wide range of pressure and temperature. For the total set, the determination coefficient (R2) and the average absolute relative deviation (AARD) of the developed model were 0.9937 and 2.95%, respectively. The regression plots revealed good consistency between predictive and experimental data points. Results show the good performance and reliability of the developed model, indicating that the proposed approach can be potentially applied for screening reasonable ILs to absorb ammonia from chemical industry processes.
Collapse
Affiliation(s)
- Xuejing Kang
- The Key Laboratory of Biotechnology for Medicinal Plants of Jiangsu Province, Jiangsu Normal University, Shanghai Road 101, 221116, Xuzhou, China; Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 16500, Prague 6, Czech Republic
| | - Zuopeng Lv
- The Key Laboratory of Biotechnology for Medicinal Plants of Jiangsu Province, Jiangsu Normal University, Shanghai Road 101, 221116, Xuzhou, China
| | - Zhongbing Chen
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 16500, Prague 6, Czech Republic.
| | - Yongsheng Zhao
- Department of Chemical Engineering, University of California, Santa Barbara, CA, 93106-5080, USA.
| |
Collapse
|