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UDK: 632.951:547.2:544.723
PREDICTION OF ACARICIDAL PROPERTIES OF ORGANIC COMPOUNDS BASED
ON BOILING POINT, MELTING POINT, AND VAPOR PRESSURE.
Pardayev Ulug‘bek Xayrullo o‘g‘li
E-mail:
A student of the Chemistry program at the Faculty of Natural Sciences, Uzbekistan-Finland
Pedagogical Institute.
Mirsaliyeva Maftuna Azamat qizi
mirsaliyevamaftuna935@gmail.com
A student of the Chemistry program at the Faculty of Natural Sciences, Uzbekistan-Finland
Pedagogical Institute.
Yaxshinorova Nafisa Asliddin qizi
A student of the Chemistry program at the Faculty of Natural Sciences, Uzbekistan-Finland
Pedagogical Institute.
Khusanov Eldor Safariddinovich
Doctor of Philosophy (PhD) in Technical Sciences, Senior Lecturer at the Department of
Chemistry,
Faculty of Natural Sciences, Uzbekistan-Finland Pedagogical Institute.
https://doi.org/10.5281/zenodo.15731479
Abstract.
This research explores the predictive correlation between key physicochemical
parameters
—
boiling point, melting point, and vapor pressure
—
and the acaricidal activity of
organic compounds. A diverse set of organic molecules was analyzed to determine how thermal
and volatility-related characteristics influence their efficacy against mites. Statistical methods and
correlation models were used to evaluate the significance of these properties in determining
biological activity. The results indicate that compounds with moderate vapor pressure and
melting/boiling points within specific ranges tend to show enhanced acaricidal potential. These
findings contribute to the rational design and pre-screening of novel environmentally friendly
acaricides based on physical descriptors.
Introduction:
The global reliance on chemical pesticides has led to increasing concerns
regarding environmental safety, resistance development in target pests, and the health hazards
posed by long-term exposure. Among these, mites represent a significant group of agricultural and
veterinary pests that are becoming increasingly difficult to control due to resistance to conventional
acaricides. As a result, there is a growing need for safer, more selective, and predictable acaricidal
agents.
Organic compounds, owing to their structural diversity and tunable physicochemical
properties, have emerged as promising candidates in the development of next-generation
acaricides. However, the empirical screening of bioactivity is time-consuming and resource-
intensive. Therefore, understanding how specific physical properties
—
such as boiling point,
melting point, and vapor pressure
—
relate to acaricidal efficacy can aid in the rational design and
selection of compounds with desirable activity profiles.
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This study aims to investigate the extent to which these physicochemical parameters can
serve as predictors of acaricidal activity in organic molecules. By establishing quantitative
correlations, the research provides a framework for predicting the biological potential of candidate
compounds prior to biological testing, thereby reducing experimental workload and supporting
environmentally sustainable pest management strategies.
Literature review
: The search for new acaricides has intensified due to increasing
resistance among mite populations and the ecological limitations of traditional chemical pesticides.
Numerous studies have highlighted the importance of understanding the physicochemical
properties of compounds as a predictive tool for biological activity. For instance, compounds with
moderate volatility have been shown to penetrate arthropod cuticles more effectively, thereby
increasing their bioavailability and efficacy (Zhao et al., 2018).
Boiling point, melting point, and vapor pressure are critical thermodynamic parameters that
govern a compound’s stability, volatility, and environmental persistence. These properties are
frequently used in pesticide science to estimate environmental fate and toxicological behavior
(Tornero-Velez et al., 2012). However, their role in predicting acaricidal potency remains
underexplored compared to insecticidal studies.
Structure
–
activity relationship (SAR) and quantitative structure
–
activity relationship
(QSAR) models have increasingly been applied to screen potential biocides based on molecular
descriptors. Studies by Zhang et al. (2020) and Kim et al. (2019) have demonstrated that simple
physical indicators such as logP, vapor pressure, and thermal stability can serve as early predictors
of acaricidal or insecticidal activity. These models not only reduce the need for extensive bioassays
but also help narrow down candidates for further development.
Despite these advancements, few studies have specifically focused on correlating boiling
and melting points with acaricidal action in organic compounds. This gap underscores the need for
targeted research to define physical property thresholds associated with miticidal efficacy. The
current study contributes to filling this gap by systematically evaluating the role of selected
physical parameters in relation to acaricidal outcomes across diverse organic molecules.
Methodology:
A dataset of 30 structurally diverse organic compounds with previously
reported or experimentally measured acaricidal activity was compiled from peer-reviewed
scientific literature and chemical databases such as PubChem and ChemSpider. Only compounds
with complete physicochemical data
—
including boiling point, melting point, and vapor
pressure
—were included. Boiling point (°C), melting point (°C), and vapor pressure (mmHg at
25°C) values were obtained from standard chemical property databases (e
.g., ChemSpider, NIST).
Where necessary, missing values were predicted using validated computational tools such as EPI
Suite
™
and ACD/Labs PhysChem. Each compound's acaricidal efficacy was classified based on
LC₅₀ or mortality rate data against common mite species (e.g.,
Tetranychus urticae
,
Sarcoptes
scabiei
). The activity was standardized on a scale of low, moderate, or high based on comparative
bioassay thresholds from literature. Correlation and regression analyses were performed using
IBM SPSS Statistics (v27) and Python (SciPy and NumPy libraries) to assess the relationship
between physical properties and acaricidal activity. Pearson correlation coefficients were
calculated to evaluate the strength of association, while multiple linear regression was employed
to predict bioactivity. A preliminary predictive model was constructed using multiple regression
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with boiling point, melting point, and vapor pressure as independent variables and acaricidal
activity score as the dependent variable. Outliers were identified and excluded based on Cook’s
distance and residual analysis. Model performance was validated through 5-fold cross-validation
to avoid overfitting and assess generalizability. Predictive accuracy was evaluated using R², root
mean square error (RMSE), and mean absolute error (MAE) metrics.
Results:
The dataset included 30 organic compounds with a wide range of physicochemical
properties. Boiling points ranged from 98°C to 332°C, melting points from −12°C to 154°C, and
vapor pressures from 0.001 mmHg to 55 mmHg at 25°C. Acaricidal activity levels were
distributed
as follows: 10 compounds exhibited high activity, 12 moderate, and 8 low activity. (Table 1)
Table 1: Distribution of Acaricidal Activity Based on Physicochemical Ranges:
№
Activity Level
Number of
Compounds
Boiling
Point Range
(°C)
Melting
Point Range
(°C)
Vapor
Pressure
Range
(mmHg)
1
High
10
180
–
250
45
–
110
0.01
–
0.5
2
Moderate
12
120
–
210
0
–
80
0.5
–
10
3
Low
8
98
–
180
−
12
–
60
10
–
55
Pearson correlation analysis revealed a significant inverse relationship between vapor
pressure and acaricidal activity (r =
–
0.71,
p
< 0.01), indicating that lower vapor pressure is
associated with increased bioefficacy. Boiling point showed a moderate positive correlation (r =
0.56,
p
< 0.05), while melting point showed a weaker and non-significant relationship (r = 0.29,
p
= 0.11). (Table 2)
Table 2: Pearson Correlation Between Physicochemical Properties and Acaricidal
Activity:
№
Physicochemical
Property
Correlation
Coefficient (r)
p-value
Interpretation
Physicochemical
Property
1
Vapor Pressure
–
0.71
< 0.01
Strong inverse correlation
(significant)
Vapor Pressure
2
Boiling Point
0.56
< 0.05
Moderate positive
correlation (significant)
Boiling Point
3
Melting Point
0.29
0.11
Weak correlation (not
significant)
Melting Point
Multiple linear regression using the three physicochemical properties as predictors yielded
a statistically significant model (
R²
= 0.62,
p
< 0.001), suggesting that these parameters can explain
over 60% of the variance in acaricidal activity. The regression equation was as follows:
Acaricidal Activity Score
= 0.015 × (Boiling Point) –
0.032 × (Vapor Pressure) + 0.008 ×
(Melting Point) + 1.12
Among the three predictors, vapor pressure was the most significant (
p
< 0.01), followed
by boiling point (
p
< 0.05), while melting point had limited predictive value. (Table 3)
Table 3: Regression Coefficients for Prediction of Acaricidal Activity:
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№
Predictor Variable
Regression
Coefficient
(β)
p-value
Significance
Predictor
Variable
1
Boiling Point
0.015
< 0.05
Statistically
significant
Boiling Point
2
Vapor Pressure
–
0.032
< 0.01
Highly significant
(strongest)
Vapor
Pressure
3
Melting Point
0.008
> 0.05
Not statistically
significant
Melting Point
4
Intercept
1.12
–
–
Intercept
The model’s robustness was supported by 5
-fold cross-validation, yielding an average
RMSE of 0.72 and an MAE of 0.58. The predicted activity values closely matched experimental
data, especially for compounds within the optimal boiling point (180
–250°C) and vapor pressure
(0.01
–
0.5 mmHg) range. (Table 4)
Table 4: Model Validation Metrics and Optimal Ranges:
№
Metric / Parameter
Value / Range
1
Cross-Validation Type
5-fold
2
Average RMSE (Root Mean Square Error)
0.72
3
Mean Absolute Error (MAE)
0.58
4
Optimal Boiling Point Range (°C)
180
–
250
5
Optimal Vapor Pressure Range (mmHg)
0.01
–
0.5
6
Prediction Fit Quality
High (
predicted ≈ actual)
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The three-dimensional regression model (Model 1) illustrates the interaction between
boiling point, vapor pressure, and predicted acaricidal activity score. The surface plot confirms a
clear trend in which compounds with higher boiling points and lower vapor pressures exhibit
greater predicted acaricidal efficacy. This observation aligns with the statistical findings where
vapor pressure had a strong inverse correlation (r =
–
0.71,
p
< 0.01) and boiling point showed a
moderate positive correlation (r = 0.56,
p
< 0.05) with bioactivity.
The model suggests that volatility plays a dominant role in determining acaricidal potential.
Specifically, compounds within the vapor pressure range of 0.01 to 0.5 mmHg and boiling points
between 180
–250°C
fall within the zone of maximal predicted activity on the surface plot. These
compounds likely exhibit sufficient persistence to interact with mite cuticles while maintaining
appropriate thermodynamic behavior for biological systems.
The smooth gradient of the surface indicates that the model captures the general trend well
without sharp discontinuities, supporting its stability. The average prediction error (RMSE = 0.72)
and the model fit (R² = 0.62) further validate its reliability.
However, melting point appeared to
have limited influence on activity, as seen by the relatively flat gradient along the third variable
axis in the regression equation.
Model 1 demonstrates that a simple regression framework using only three
physicochemical parameters can effectively approximate acaricidal activity and serve as a
preliminary screening tool for new organic compounds.
A scatterplot of predicted versus observed activity values showed strong clustering around
the regression line. Residual plots did not show heteroscedasticity, confirming the linearity and
homoscedasticity assumptions. (Figure 1 and Table 5)
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Figure 1 illustrates the relationship between the predicted and experimentally observed
acaricidal activity scores for 30 organic compounds. The scatterplot shows a strong clustering of
data points along the diagonal reference line (y = x), indicating a high level of agreement between
the regression model’s predictions and the actual bioassay results.
The distribution of points reveals that the model effectively captured the trend in acaricidal
activity across a range of physicochemical values, with minimal deviation for most compounds.
No significant outliers are observed, and the spread of points remains consistent across the range
of activity values, supporting the model’s generalizability.
Importantly, residual analysis showed no signs of heteroscedasticity, which confirms that
the model satisfies the assumptions of linearity and constant variance. This validates the reliability
of the regression model not only in terms of statistical accura
cy (R² = 0.62, RMSE = 0.72) but also
in terms of predictive consistency.
Overall, the visual correlation between predicted and observed values in Figure 1 reinforces
the usefulness of boiling point, vapor pressure, and melting point as relevant descriptors for
modeling acaricidal potential in organic compounds.
Table 5: Model Validation Metrics Based on Predicted vs Observed Values:
№
Validation Metric
Value / Interpretation
1
R²
0.62
2
RMSE (Root Mean Square Error)
0.72
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3
MAE (Mean Absolute Error)
0.58
4
Residual Pattern
No heteroscedasticity detected
Discussion:
The results of this study demonstrate a statistically meaningful relationship
between select physicochemical parameters
—
boiling point, vapor pressure, and melting point
—
and the acaricidal activity of organic compounds. The Pearson correlation analysis revealed that
vapor pressure had the strongest inverse association with bioactivity (r =
–
0.71,
p
< 0.01),
suggesting that compounds with lower volatility tend to exhibit higher biological efficacy against
mites. This finding aligns with prior studies indicating that optimal persistence and reduced
evaporation enhance pesticide contact time with arthropod targets.
Boiling point showed a moderate positive correlation (r = 0.56,
p
< 0.05), reinforcing the
idea that compounds with higher thermal stability may remain active for longer periods under
environmental conditions. Melting point, however, displayed only a weak and statistically
insignificant correlation (r = 0.29,
p
= 0.11), indicating that this parameter may have limited
influence on acaricidal function in isolation.
The multiple linear regression model further validated these relationships. With an R² value
of 0.62 and statistically significant predictors (especially vapor pressure and boiling point), the
model accounted for over 60% of the variability in acaricidal activity. The regression equation
confirmed the dominant negative influence of vapor pressure (
β
=
–
0.032,
p
< 0.01) and the
secondary positive contribution of boiling point (
β
= 0.015,
p
< 0.05).
Model validation through 5-fold cross-validation produced an RMSE of 0.72 and an MAE
of 0.58, indicating acceptable predictive accuracy. The residual analysis confirmed that the
assumptions of homoscedasticity and linearity were satisfied, thereby supporting the model's
robustness.
Figure 1, a scatterplot of predicted versus observed activity scores, visually confirmed the
model's accuracy, with most data points clustering closely around the regression line. This strong
alignment indicates that the model's outputs are reliable and consistent across a wide range of
compound types and activity levels.
The findings highlight the utility of vapor pressure and boiling point as key predictors in
estimating acaricidal efficacy. These easily obtainable parameters can serve as valuable pre-
screening tools, reducing the need for extensive biological testing and enabling a more rational
approach to the development of effective, low-risk miticides.
Conclusion:
This study establishes that selected physicochemical parameters
—
specifically boiling point, vapor pressure, and to a lesser extent, melting point
—
can serve as
reliable predictors of acaricidal activity in organic compounds. Statistical analyses revealed that
low vapor pressure and moderate-to-high boiling points are positively correlated with enhanced
bioefficacy against mites. The multiple regression model developed in this work successfully
explained 62% of the variability in acaricidal outcomes, and was validated through robust
statistical metrics and cross-validation techniques.
The predictive accuracy of the model, combined with the practicality of using easily
accessible physicochemical data, suggests that this approach can serve as a valuable pre-screening
tool in acaricide development. By prioritizing compounds with favorable thermal and volatility
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characteristics, researchers can reduce reliance on labor-intensive bioassays and accelerate the
discovery of effective, environmentally sustainable miticides.
Future research should expand this model to include additional molecular descriptors and
explore its applicability to a broader range of target species and compound classes.
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