A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

doi:10.1007/s11705-021-2060-z

Front. Chem. Sci. Eng.

2022, Vol. 16

Issue (2) : 152-167 https://doi.org/10.1007/s11705-021-2060-z

RESEARCH ARTICLE

A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

Qilei Liu, Yinke Jiang, Lei Zhang(

), Jian Du

Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China

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Abstract

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemical-based products. The life cycles of chemical products involve the procedures of conceptual product designs, experimental investigations, sustainable manufactures through appropriate chemical processes and waste disposals. During these periods, one of the most important keys is the molecular property prediction models associating molecular structures with product properties. In this paper, a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions, such as activity coefficient, and so forth. The workflow of framework consists of three steps. In the first step, a database is created for collections of basic molecular information; in the second step, quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties (pseudo experimental data), which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step. The whole framework has been carried out within a molecular property prediction toolbox. Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

Keywords molecular property quantum mechanics quantitative structure-property relationship heat of reaction solid-liquid phase equilibrium

Corresponding Author(s): Lei Zhang

Online First Date: 13 July 2021 Issue Date: 10 January 2022

Cite this article:

Qilei Liu,Yinke Jiang,Lei Zhang, et al. A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship[J]. Front. Chem. Sci. Eng., 2022, 16(2): 152-167.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-021-2060-z
https://academic.hep.com.cn/fcse/EN/Y2022/V16/I2/152

Tab.1 Toolboxes developed for QSPR/QSAR methods

Fig.1 The diagrammatic sketch of QM-QSPR framework.

Fig.2 The software architecture of QM-QSPR with its dataflow and workflow.

Fig.3 The workflow of case study 1.

Fig.4 Comparison results between QM calculated values and experimental data for (a)

Δ f H g θ

, (b)

Δ f G g θ

and (c)

S g θ

at 298.15 K of 22 representative compounds (blank means experimental data are unavailable in database).

No.	Compound	CAS number	Calculated $Δ H vap θ$ /(kJ·mol^–1)	Experimental $Δ H vap θ$ /(kJ·mol^–1)	Calculated $Δ G vap θ$ /(kJ·mol^–1)	Experimental $Δ G vap θ$ /(kJ·mol^–1)	Calculated $Δ S vap θ$ /(J·mol^–1·K^–1)	Experimental $Δ S vap θ$ /(J·mol^–1·K^–1)
1	Methane	74-82-8	4.4		–21.8		88.0
2	1-Octene	111-66-0	46.9	40.4	20.7		88.0
3	Ethylene	74-85-1	18.7		–7.5		88.0
4	1-Butyne	107-00-6	34.9		8.6		88.0
5	1-Bromohexane	111-25-1	50.3		24.1		88.0
6	Bromoethane	74-96-4	31.3		5.0		88.0
7	Propanol	71-23-8	48.5	47.5	22.2	8.8	88.0	129.1
8	Ether	60-29-7	34.6	27.4	8.4	–5.6	88.0	170.3
9	Benzaldehyde	100-52-7	63.2		37.0		88.0
10	Acetaldehyde	75-07-0	27.6	26.1	1.3	–5.4	88.0	103.4
11	Acetone	67-64-1	35.1	31.3	8.8		88.0	96.5
12	Methyl ethyl ketone	78-93-3	41.3	19.6	15.0		88.0	100.8
13	Hexanoic acid	142-62-1	72.8		46.5		88.0
14	Acetic acid	64-19-7	53.3	52.2	27.1	16.0	88.0	123.6
15	Ethyl acetate	141-78-6	38.5	35.7	12.3	5.3	88.0	106.1
16	Methyl formate	107-31-3	38.0	28.7	11.8		88.0
17	Propionitrile	107-12-0	38.3		12.0		88.0
18	Acetonitrile	75-05-8	35.0	39.6	8.8	5.4	88.0	93.7
19	Propylamine	107-10-8	44.4	31.3	18.2		88.0
20	Methanethiol	74-93-1	27.2		1.0		88.0
21	Water	7732-18-5	35.1	44.0	8.9	8.5	88.0	118.9
22	Carbon dioxide	124-38-9	18.1	19.8	–8.1	–8.4	88.0	94.4

Tab.2 The calculated values of

Δ H vap θ

Δ G vap θ

Δ S vap θ

at 298.15 K of 22 examples and their corresponding experimental data ^a)

No.	Compound	CAS number	Calculated $Δ f H 1 θ$ /(kJ·mol^–1)	Experimental $Δ f H 1 θ$ /(kJ·mol^–1)	Calculated $Δ f G 1 θ$ /(kJ·mol^–1)	Experimental $Δ f G 1 θ$ /(kJ·mol^–1)	Calculated $S 1 θ$ /(J·mol^–1·K^–1)	Experimental $S 1 θ$ /(J·mol^–1·K^–1)
1	Methane	74-82-8	–78.1		–36.9		118.7
2	1-Octene	111-66-0	–146.2	–121.8	56.3		335.0
3	Ethylene	74-85-1	32.8		69.4		130.9
4	1-Butyne	107-00-6	122.4		174.7		201.2
5	1-Bromohexane	111-25-1	–215.0		–43.3		325.9
6	Bromoethane	74-96-4	–101.2		–42.7		198.4
7	Propanol	71-23-8	–309.5	–303.5	–190.1	–170.6	212.8	193.6
8	Ether	60-29-7	–303.2	–279.4	–155.8	–116.7	245.4	172.4
9	Benzaldehyde	100-52-7	–115.7		–48.8		244.5
10	Acetaldehyde	75-07-0	–192.0	–192.2	–134.6	–127.6	163.5	160.4
11	Acetone	67-64-1	–255.6	–248.3	–170.0		195.8	198.8
12	Methyl ethyl ketone	78-93-3	–288.5	–258.2	–174.0		225.7	239.1
13	Hexanoic acid	142-62-1	–581.9		–390.5		323.4
14	Acetic acid	64-19-7	–465.1	–484.4	–387.3	–390.2	197.6	159.9
15	Ethyl acetate	141-78-6	–496.0	–481.1	–362.9		265.7	256.7
16	Methyl formate	107-31-3	–406.1	–386.1	–327.9		196.4
17	Propionitrile	107-12-0	8.1		72.1		196.4
18	Acetonitrile	75-05-8	32.2	34.4	68.2	86.5	163.4	149.7
19	Propylamine	107-10-8	–125.7	–101.3	10.4		215.1
20	Methanethiol	74-93-1	–54.8		–19.9		165.2
21	Water	7732-18-5	–262.1	–285.8	–224.5	–237.1	106.6	69.9
22	Carbon dioxide	124-38-9	–408.7	–413.3	–386.2	–386.0	126.1	119.4

Tab.3 The calculated values of

Δ f H 1 θ

Δ f G 1 θ

S 1 θ

at 298.15 K of 22 examples and their corresponding experimental data ^a)

Property	$R 2$	MAE
$Δ f H g θ$	0.990	28.1 kJ·mol^–1
$Δ f G g θ$	0.988	27.7 kJ·mol^–1
$S g θ$	0.993	6.7 J·mol^–1·K^–1
$Δ H vap θ$	0.925	4.0 kJ·mol^–1
$Δ G vap θ$	0.925	4.0 kJ·mol^–1
$Δ f H 1 θ$	0.990	28.0 kJ·mol^–1
$Δ f G 1 θ$	0.988	27.7 kJ·mol^–1
$S 1 θ$	0.993	6.7 J·mol^–1·K^–1

Tab.4 The fitting results (

R 2

)/error criterion (MAE) of

Δ f H g θ

Δ f G g θ

S g θ

Δ vap H θ

Δ vap G θ

Δ f H 1 θ

Δ f G 1 θ

and

S 1 θ

between GC predictions and QM predictions

Fig.5 The number of group sets with

Δ f H g θ

Δ f G g θ

and

S g θ

values in the MG1 based GC method and MG1-RDKit based GC method.

Fig.6 The diagrammatic sketch of synthesis pathway for aspirin.

Molecule	CAS number	Experiment $Δ f H g θ$ /(kJ·mol^–1)	Unpackaged hybrid QM $Δ f H g θ$ /(kJ·mol^–1)	MG1-RDKit $Δ f H g θ$ /(kJ·mol^–1)	MG1 $Δ f H g θ$ /(kJ·mol^–1)
Salicylic acid	69-72-7	–494.8	–473.5	–458.4	–469.4
Acetic anhydride	108-24-7	–572.5	–588.3	–587.0	–575.2
Aspirin	50-78-2		–677.1	–667.1
Acetic acid	64-19-7	–432.8	–434.3	–415.9	–432.6

Tab.5 The properties,

Δ f H g θ

(298.15 ? K)

, for each compound in aspirin synthesis pathway obtained from different methods (the experimental data refers to ICAS software [42]; blank means data are unavailable)

Fig.7 Fig. 7 The workflow of case study 2.

Fig.8 Comparisons of solid-liquid phase equilibrium curves of solute ibuprofen and four solvents ((a) 1-propanol, (b) 2-methyl-1-propanol, (c) 3-Methyl-1-butanol and (d) ethyl acetate) obtained from the experiment, QM calculation and MLAC method.

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