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13.5 Faster and Accurate QC-Data Acquisition
It should be worth further investigating larger sets of data based on the promising first
results on the QC-based classification and prediction of degradation of micropollutants in wastewater described in Sect.
achieve this. Firstly, the computational cost is rather high at the QC level used in the
study (M062X/6–311+G(2df,2p)//M06L/6–311+G(2df,2p) with SMD). One needs
to explore a relevant method to calculate the QC descriptors with sufficient accuracy
and practically acceptable computational cost. Secondly, as described in Sect.
because of their lower degree of controllability and traceability, the quality and quantity of experimental data for chemical degradation in wastewater are limited. Therefore, the scaling up of the experimental and computational data cannot be achieved
without employing specific strategies necessary to overcome this limitation.
There are several possibilities for informatics technologies to improve the quality
and quantity of experimental data. One possibility is measurement informatics (MI),
where informatics technologies are applied to various measurement techniques, e.g.,
spectroscopy [
raw data, increasing image resolution, improving signal-to-noise (S/N) ratio, and
building models directly from raw data. Some examples of MI are introduced in
Chapters
The situation of computational cost for QC-data acquisition has greatly improved
thanks to advances in computer technology and method development, including
informatics approaches. For example, ML-based force fields, initiated by Behler
and Parrinello [
of structures while maintaining the same level of accuracy. The use of informatics
has become increasingly common also for generating descriptors with high accuracy
from lower levels of calculations.
These approaches can be used to enrich the quality and quantity of descriptors for predicting degradation in wastewater. Here, we introduce another potential
approach for scaling up the computational data with limited experimental data for
the degradation.
89–91]. The aims of MI include extracting more information from
15 and 16 of this book.
92, 93], paved the way for enormously accelerating computations
13.4. However, there are several challenges to
13.4,
13.5.1 Strategy to Build Large Dataset
Figure 13.9 shows the strategy to build a large dataset of QC parameters for the
prediction of degradation reactivity in wastewater. Dataset ➀ consists of high QC
level (Level 1) and experimental data. Dataset ➁ consists of only calculated data at
the same QC level used to obtain ➀. In the first step, the degradation reactivity of
dataset ➁ is predicted using SVM in the same scheme as shown in Fig.
The second step searches for a lower QC level that provides parameters with
sufficient accuracy using datasets ➀ and ➁. Using this 2nd highest level (Level 2),
QC calculations are carried out for larger dataset (➀, ➁, and ➂).
13.8.
13 “Quantum-Chemoinformatics” for Design and Discovery of New … 237
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Fig. 13.9 Strategy to build a large dataset of QC parameters for prediction of degradation reactivity
in wastewater
The third step builds a ML model to predict the parameters with the accuracy
of Level 2 from the calculations at a further lower QC level (Level 3) and basic
molecular descriptors. The ML model is used to produce a larger dataset ➃, which
can be used for screening molecules in terms of degradation reactivity.
This strategy assumes that the prediction model built at the first step correctly
works. This means the determination of appropriate QC parameters at the first step
is essential to give an accurate prediction for successful screening using the large
dataset ➃. The sample numbers of molecules are shown in Fig.
13.9. Generally, the
reliability of predictions increases with the number and diversity of molecules in the
dataset ➀.
13.5.2 Results
After examining the correlations between several QC levels and the highest QC
level of M062X/6-311+G(2df,2p)//M06L/6-311+G(2df,2p) with SMD (Level 1)
regarding one-electron oxidation potential and HOMO-LUMO gap parameters using
167 molecules, it was found that M062X/6-31+G(d,p)//BLYP/3-21G(d) with SMD
(Level 2) shows a sufficiently good correlation with those at Level 1.
Figure 13.10a plots the two parameters at Level 1, where the molecules are classified into reactive (red) and non-reactive (blue) based on the SVM hyperplane for 20
molecules with experimental data shown in Fig.
13.8. Figure 13.10b plots the same
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parameters at Level 2, where its SVM hyperplane was calculated by using the classification of reactive (red) and non-reactive (blue) indicators shown in Fig.
13.10a.
There was a good correlation between Level 1 and Level 2 for both one-electron
2
oxidation potential (R
13.10d). The calculation speed at Level 2 is c.a. 4.2 times faster than that at
Fig.
= 0.907, Fig. 13.10c) and HOMO-LUMO gap (R2 = 0.971,
Level 1.
Two approaches were tested to train the ML models to predict the parameters with the accuracy of Level 2 from Level 3: direct prediction of the parameter values (Fig.
13.11b). Regarding the ML models, the Random Forest (RF) method gave better
(Fig.
13.11a) and prediction of delta values Δ(Level 3–Level 2)
results compared to Gradient Boosting and AdaBoost (Adaptive Boosting) methods.
M062X/6-31+G(d,p)//PM7 with SMD (for energy calculations) was adopted as Level
3 by examining several DFT (density function theory) levels and basis sets. PaDELdescriptors [
ular descriptors. The prediction based on the delta values (Fig.
correlations to the classification using Level 1 (Fig.
plots of Fig.
26] and Weisfeiler-Lehman (WL) Kernel [94–96] were used as molec-
13.11b) shows better
13.10a) as demonstrated in the
13.11. The calculation speed at Level 3 with RF is c.a. 9.8 times faster
than that at Level 1.
Fig. 13.10 Results from the search of lower QC levels that can be used to calculate parameters
of interest. a Classification of 167 molecules as reactive (red) or non-reactive (blue) by using the
linear SVM model trained with calculated and experimental data of 20 molecules (Fig.
Level 1: M062X/6-311+G(2df,2p)//M06L/6-311+G(2df,2p) with SMD solvent model. b Plot the
two parameters of 167 molecules at Level 2: M062X/6-31+G(d,p)//BLYP/3-21G(d) with SMD
solvent model. The molecules are classified as reactive (red) or non-reactive (blue) according to the
results of (a). This dataset was used to train the SVM model. c Correlation between Level 1 and
Level 2 regarding one-electron oxidation potential. The R
Level 1 and Level 2 regarding HOMO-LUMO gap. The R
13.8)at
2
value is 0.907. d Correlation between
2
value is 0.971
13 “Quantum-Chemoinformatics” for Design and Discovery of New … 239
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Fig. 13.11 Results of prediction of parameters from lower level calculations using RF model.
a Direct prediction. b Prediction of delta values (Δ(Level 3 – Level 2)). The molecules in plots (a)
and (b) are colored according to the classification as reactive (red) or non-reactive (blue) according
to the results of Fig.
as molecular descriptors
13.10a. The border lines are from linear SVM. We used PaDEL and WL kernel
13.6 Conclusions and Remarks
We presented a historical overview together with some works in the field of quantumcheminformatics, the data-driven chemistry utilizing QC parameters. We focused
especially on the design and prediction of chemical reactions, which is one of the
key processes of the basic research for drug development.
Chemistry has a long history of working with AI and ML, dating back to the 1960s
30, 31], i.e., to the research fields called chemometrics and chemoinformatics. Since
[
the early days of this data-driven chemistry field, researchers have been advancing
it by developing descriptors for chemical information and modeling methods for the
prediction and design of molecular structures and chemical reactions, aiming not only
at efficient design and prediction but also at automatic synthesis with robotics technology. The accumulation of these research and development forms the foundation
of modern data-driven chemistry.
QC, which is at the heart of theoretical chemistry, has made important contributions to the development of chemistry. It is often used to obtain rational explanations
of experimental phenomena. It can also be used to predict chemical structures and
their properties, even for molecules that are difficult or impossible to access, such as
those at TS or found in interstellar space. The basic mathematical equations that can
describe chemical phenomena were discovered in quantum mechanics (QM) about
a century ago. However, applying QM to solve chemical problems and establish QC
required significant effort, as chemical phenomena are generally too complex to be
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described by a straightforward application of these equations. Thanks to the great
efforts of researchers and the development of computer technology, QC and other
methods of theoretical chemistry, such as MD and MM, are now widely used in the
field of chemistry.
The field of data-driven chemistry has been a minor discipline for a long time.
Many people were unaware of the significance of this field and most of the people
in the QC field ignored data-driven chemistry. Only few people realized the effectiveness of combining data-driven chemistry with QC. They developed the field to a
stage that enabled the current rapid growth.
In recent years, as AI and ML technologies have rapidly conquered a wide variety
of domains including natural science, data-driven chemistry has finally stepped into
the spotlight, and its applications have been increased dramatically. Even those in the
field of theoretical chemistry have turned their attention to this area. As the number of
interested researchers increases, the diversity of ideas and their applications to more
diverse topics will increase. This phenomenon is expected to have a synergistic effect
and we will experience significant growth of data-driven chemistry in the future.
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