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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5580_Библиотеки_им_академика_М_И_Перельмана
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226 H. Satoh et al.
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Basic research
(f) Evaluation of
synthesized molecules
(e) Structure
determination
(a) Molecular design
New drug candidates
Preclinical research
(d) Synthesis
(c) Reaction design
and prediction
Fig. 13.1 Process of basic research at the early stage of drug development. Chemical reaction design
and prediction are important steps in the process for efficient synthesis of designed molecules with
high yield, less or without side products, and reducing the cost and environmental impact
(b) In Silico
evaluation of
designed molecules
Clinical research
Products
artificial intelligence (AI) and ML technology rapidly conquered a wide variety of
domains including natural science. In the last decade, more systems with data-driven
approaches have been developed including RMap project [
Molecular Transformer [
19, 20]systems.
17], AlphaChem [18], and
Key elements that determine the performance of data-driven systems are the choice
of ML method, the quality and quantity of data, and the design of descriptors, i.e.,
how to describe molecules and reactions. Finding good descriptors is an important
part of the challenge.
In chemistry, many descriptors have been developed [21, 22], and software pack-
]. These descriptors can be categorized
ages to calculate them are available [
23–29
into four different types: graph, language, physicochemical parameters (empirical),
and properties from theoretical chemistry (Table
13.1).
The basic method to describe chemical structures is based on graph theory
(Table 13.1a), where atoms and bonds are treated as nodes and edges, respectively.
Several software packages to generate chemical graphs that meet given conditions,
30–39
e.g., a molecular formula and spectral data, have been developed [
]. Such
methods are becoming increasingly crucial to explore the large chemical space.
Language (Table 13.1b) is also generally used to describe chemical information,
including text data in DBs (e.g., reaction condition), chemical language like Chemical
Markup Language (CML) [
whereby oncology and advanced language models, such as Transformer [
applied to chemical problems [
40], and natural language. A new trend is emerging
41], are
20, 42
].
Descriptors based on physicochemical parameters are often calculated with empirical methods (Table
13.1c). They have been intensively developed as primary
descriptors in the quantitative structure–activity relationships (QSAR) study.
13 “Quantum-Chemoinformatics” for Design and Discovery of New … 227
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Fig. 13.2 Chemical reaction design and prediction systems. Several software systems have been
developed since 1960s, starting with (a) rule- or logic-based approaches. (b) Data-driven methods
using knowledgebase or ML models, which are automatically built from DBs, have been mainstream
since the late 1980s. New systems have appeared especially after the second boom started in the
middle of 2010s, as artificial intelligence (AI) and ML technology has been rapidly conquered a
wide variety of domains including natural science in the last decade. Note that this figure does not
cover all of the systems
Nowadays, various methods and software programs to provide these three basic
types of descriptors are available and are widely used. An advantage of these descriptors is their low computational complexity, allowing the easy construction of large
datasets and enabling screening of big data. However, these descriptors may not
be sufficient for certain purposes, and further information beyond what is provided
by these descriptors is necessary. Specific characteristics of three-dimensional (3D)
structures and properties related to electronic structures, to molecular fields or cavities, and to molecules on surfaces, in cages, or in solvents are needed. Dynamic
behaviors of these chemical systems may play an additional important role.
Theoretical chemistry, e.g., molecular mechanics (MM), quantum chemistry
(QC), and molecular dynamics (MD), could be used for these purposes (Table
13.1d).
These techniques can provide essential properties of molecules and reactions based
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Table 13.1 Types of chemical descriptors
Types (a) Graph (b) Language (c) Physicochemical
Contents Chemical
Attribute Abasic
Calculation
speed
graph
method to
describe
chemical
structures in
DBs
Very fa s t Very fas t Fast Slow or very slow
Text data in DBs (e.g.,
reaction condition),
chemical languages,
natural languages
(1) A basic method to
describe chemical
structures and
reactions in
database and
literature
(2) New trends
inspired by new
informatics
methods like
Transformer
parameters
(Empirical)
e.g., σ , π -charge,
polarizability, pKa
(1) Primary
descriptors for
analysis of
property,
activity, and
reactivity
(2) Well-developed
especially in
QSAR
(d) Properties
fromTheoretical
chemistry
e.g., Potential/
Gibbs free energy,
transition states
(TS), HOMO,
LUMO
(1) Essential
properties for
molecular and
reaction
analysis can be
provided
(2) Sufficient
quantity and
quality of data
have been
getting
available
on electronic structures. Some of the properties, such as 3D structures, potential or
free energy, activation energy, and solvent effects cannot be obtained usually with
the three basic methods (Table
13.1a–c). However, they are necessary to discuss
property, activity, and reactivity of molecules in chemical synthesis as well as the
behavior of drugs in vitro and in vivo. Despite its high computational cost, sufficient
quantity and quality of this high-level data is now getting accessible, thanks to enormous efforts by computational and theoretical chemists as well as the advancement
of computer technology.
In this chapter, we focus on reaction design and prediction with the approach
of data-driven chemistry (chemoinformatics) using descriptors based on theoretical chemistry, especially QC and ab initio MD simulations. We call this approach
quantum-chemoinformatics. We will start with a brief review and then introduce two
projects of quantum-cheminformatics: One is named the RMap project, which uses
QC-based chemical reaction route networks for the discovery and design of new
molecules and reactions. The other is related to environmental pollution by drug
molecules, which should be taken into account during molecular design and evaluation (Fig.
13.1a, b) in the process of basic research in drug development. In the
last section, we will describe our recent attempt to accelerate QC-data acquisition
by utilizing a limited amount of experimental data and ML technology.
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13.2 Mini-Review of Quantum-Cheminformatics
In this section, we will give a brief history and the current status of quantumcheminformatics for reaction design. We focus on QC calculation-based parameters and DBs and reaction field analysis methods. Further, we will discuss reaction
pathway networks from QC calculations.
13.2.1 QC Parameters and DBs
Basic information obtained from QC calculations includes geometries and energy
on the potential or Gibbs free energy surfaces, generally at stationary points, i.e.,
minimum or saddle points, corresponding to equilibrium (EQ) and transition states
(TS), respectively. Thereby several parameters related to the electronic structure can
be obtained, including atomic charges, highest occupied molecular orbital (HOMO),
lowest unoccupied molecular orbital (LUMO), one-electron oxidation potential,
43
electronegativity, electron affinity, and chemical hardness [
Many chemical phenomena are usually discussed based on molecular ground
states. However, some properties need excited states to be considered, which have
different reactivity and properties, e.g., in the discussion of absorption and emission
of light. It is one of the advantages of QC methods that they can provide information
not only at the ground states but also at the excited states.
MD simulations, especially QC-based MD simulations, provide insights on the
dynamical behavior of chemical systems, e.g., chemical reactions in solutions, a cage
or a cavity of a protein, or at a surface of materials or biomolecules.
A new development of electronic structure-based descriptors for drug discovery
is discussed in Chapter
Recently, as data-driven methods became more commonly used in chemistry, and
sufficiently accurate QC data can be calculated in an accessible timeframe, the use
of QC methods for data acquisition has become increasingly popular. Large datasets
of geometries and energies at several QC levels and methods have been generated
in several groups [
developed since the early 2000s [17
application of TS DB system to synthetic design studies.
11 of this book.
]. Reaction pathway DBs with TS structures have been also
44–47
, 48, 49]. Chapter
].
12 of this book describes an
13.2.2 Molecular and Reaction Field Analysis
QC results can be used to analyze further characteristics of molecular and reaction
fields. Several approaches have been developed for 3D-QSAR study [
prediction of chemical reactivity or stereoselectivity in chemical reactions [
A typical approach is to place a molecule within a grid and to use a probe placed
50–59] and
60–65
].
230 H. Satoh et al.
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at every point on the grid to measure its properties [50, 51, 55, 63, 64]. Another
approach is to consider the space occupied by a molecule and use a probe on the
molecular surface to measure its properties [
These methods can be applied to reaction prediction and design by combining
them with neural networks [
called sparse modeling [
61, 62, 65] or by using an advanced informatics method
63, 64].
61].
13.2.3 Reaction Pathways
Reaction pathway networks or maps, such as those used to describe metabolism,
are common data used in bioinformatics. But most of reaction data available in
chemistry are single or multi-step reactions, each representing a single pathway
from a starting material to a product. Due to the difficulty of calculating pathway
networks of chemical reactions based on QC, those reaction data were limited to
single pathways [
In the beginning of the 2000s, several new exploration techniques of potentialenergy surfaces (PES) made calculations of chemical reaction pathway networks
more accessible [
find all the stationary points on PES to make up the chemical reaction pathway
networks.
The first project using these QC-based chemical reaction pathway network data
for molecular and reaction design by combining informatics techniques was reported
in 2015, called Maizo-chemistry project, currently called RMap project [
Useful methods to find characteristics of chemical reaction pathway networks were
also proposed, e.g., kinetic analysis to profile the networks [
homology analysis to sketch the landscape of reaction networks based on the height
of each stationary point on PES [
48].
39
]. Using these new techniques, it became possible to automatically
17, 66].
] and persistent
67–69
70].
13.3 RMap Project: QC Reaction Pathway Network
Data-Driven Prediction and Design
The RMap (or “Maizo”-chemistry) project was launched in 2008, and the first paper
was published in 2015 [
discovery based on big data of QC global reaction pathway networks. An overview
of the project is shown in Fig.
includes EQ structures and dissociation channels (DCs), which are connected via
TSs. Each of the stationary points can be related to several properties, e.g., potential
energy, Gibbs free energy, geometries, and atomic charges. The RMap system uses
the Scaled Hypersphere Search of the Anharmonic Downward Distortion Following
(SHS-ADDF) method [
17
]. This project is aimed toward molecular- and reaction
13.3. The reaction pathway DB (RMapDB) [49
71–74
] to obtain these data but is basically designed not to
]
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Fig. 13.3 Overview of the RMap project. The reaction pathway DB (RMapDB) includes EQ structures and dissociation channels (DCs), which are connected via TSs. The project aims to use these
data together with experimental data to find or design new reactions and molecules
focus on specific software. One can use any QC or MD software to obtain these
reaction network data and convert them to the file formats compatible to the RMap
system, e.g., xyz coordinate format. The aim of the project is to use these data
together with experimental data to find or design new reactions and molecules. Appropriate informatics methods are chosen for modeling in the prediction and design if
necessary.
Figure 13.4 shows images of RMapViewer [75], a tool for visualization and analysis of reaction pathway networks, which is a part of the RMap system. A reaction
pathway network called reaction map (RMap) (left) is visualized together with energy
diagrams (right) as shown in both top and bottom views of Fig.
13.4. The RMap data
can be searched with 2D or 3D queries. If you specify two molecules as reactant and
product in the network, the system enumerates all possible pathways between them
and shows the list of pathways in ascending order of the maximum activation energy
along the route, the number of reaction steps, and the maximum TS energy along
the route. The top of the list of the maximum activation energy corresponds to the
minimum energy pathway (MEP).
Figure 13.5 shows an example of the reaction pathway network for aciclovir.
This calculation was carried out by using GRRM (GRRM17, SHS-ADDF, LADD =
10). This setting means that reaction pathways starting from aciclovir by using the
SHS-ADDF method, especially by taking only large-ADD (LADD) into account,
assuming that searching EQs via relatively small TS energies. The obtained reaction
pathway network consists of 9 EQs, 8 TSs, and 1 DC. One of the pathways from
aciclovir (EQ0), which is a proton transfer reaction, is shown in Fig.
13.5. Although
this is a known reaction, the benefit of this calculation is that this type of reactions
can be obtained automatically and this is not a single pathway but part of a reaction
network.
The RMap system was applied to the automatic deduction of conformational
transition networks, which gives QC landscapes of glucose conformers [76]. In the
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Fig. 13.4 Images of RMapViewer, a tool for visualization and analysis of reaction pathway
networks. RMapViewer is a part of the RMap system. A reaction pathway network called reaction map (RMap) (left) is visualized together with an energy diagram (right). RMap data can
be searched with 2D or 3D queries. The system enumerates all possible pathways between two
molecules (reactant and product) and shows the list of pathways respect to the maximum activation
energy along the route, the number of reaction steps, and the maximum TS energy along the route.
The user can find the minimum energy pathway (MEP)
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Fig. 13.5 Example of reaction pathway: hydrogen transfer reaction of aciclovir, antiviral medication. QC level: B3LYP/6-31G(d,p). GRRM: SHS-ADDF, LADD = 10. The obtained reaction
network consists of 9 EQs, 8 TSs, and 1 DC
context of the RMap project, new classes of carbon allotropes and polycyclic aromatic
hydrocarbons were explored [
The RMap system is open source [66] and open data [49] according to the open
science spirits.
39, 77–84].
13.4 Drug Design Considering Degradations
in Environment
As described in Sect.
sizing them is very important (Fig.
early stage of drug development. The prime target is to design molecular structures
that have desired functions, such as pharmacological activity (Fig.
accessibility is another major issue. However, to design an optimal molecule, one
should consider not only these positive properties but also negative properties of
those molecules, like toxicity and persistency in the environment. Since people
have become aware of the serious environmental problems caused by micropollutants, demand to consider the burden on the environmental in chemical development
has steeply increased. Applications of informatics technologies to the prediction of
degradation of micropollutants in wastewater, quantitative structure-bio-degradation
relationships (QSBR), have been discussed [
13.1, the evaluation of molecules before and after synthe-
13.1b, f) in the process of basic research at the
13.6). Synthetic
85
].
234 H. Satoh et al.
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(6) Evaluation of
synthesized molecules
(5) Structure
determination
(4) Synthesis
(3) Reaction design
Fig. 13.6 Degradation in environment is one of the important factors for molecular evaluation in
drug design
(1) Molecular design
(2) In Silico
evaluation of
designed molecules
•
Activity
•
Reactivity
•
Synthetic Accessibility
•
Toxicity
•
Degradation in Environment
Degradation prediction is related to the wide field of reaction prediction. The reac-
tion prediction systems described in Sect.
13.1 are targeting general organic chemical
reactions at the lab bench. In principle, similar approaches can be used to predict
reactions of chemicals in the environment, i.e., chemical degradation in wastewater. However, to address the full complexity of chemical reactions in wastewater,
adjusting the basic strategy is needed. The major problem with degradation processes
in wastewater comes from their much lower degree of controllability and traceability,
leading to lower quality and quantity of chemical degradation information from
wastewater.
One possible approach to overcome the limitation of the quality and quantity of
chemical degradation data can be combining QC methods with classical QSBR. A
benefit of QC approaches is to provide data with a certain level of accuracy based
on theoretical grounds and to give insights about reaction mechanisms.
A pilot study to see the applicability of ab initio QC parameters to degradation in
wastewater has been carried out for specific relevant enzyme systems, laccases, also
called multicopper oxidases (MCOs), which are known as potent monooxygenases,
]. In metatranscriptomic data from
particularly in the case of fungal laccases [
86
activated sludge microbial communities, gene transcripts of bacterial laccases were
found to be rather abundant and their abundances to align with oxidative transformation of micropollutants, e.g., hydroxylations or oxidative N- and O-dealkylations.
Since bacterial laccases typically have a lower reduction potential than fungal ones,
a mediator compound to catalyze the oxidation reaction should be involved in a plausible mechanism (Fig.
was examined for 20 micropollutants (1–20 in Fig.
13.7a). In an experimental study [ 87
13.8) with 2,2-azino-bis-3-athyl-
benzthiazoline-6-sulfonic acid (ABTS) as a mediator (Fig.
(13, 14, 15, 16, 17, 18, 19, and 20 in Fig.
tional study [85
], the possibilities of QC descriptors that can distinguish between the
13.8) were degraded. In the computa-
], degradation reactivity
13.7b), and 8 compounds
13 “Quantum-Chemoinformatics” for Design and Discovery of New … 235
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reactive and non-reactive compound groups were examined, and it was found that a
simple combination of the one-electron oxidation potential and the HOMO–LUMO
energy gap can clearly classify these two groups and construct a model to predict
the reactivity by using linear support vector machine (SVM) (Fig.
13.8). The calcu-
lations were performed at the M062X/6–311+G(2df,2p)//M06L/6–311+G(2df,2p)
level with the SMD solvent model by using the Gaussian 16 program package [
88].
This result is based only on a small dataset. Nonetheless, it is notable that such
simple QC descriptors were able to identify differences in degradability across a
set of structurally highly diverse micropollutants. It indicates applicability of QC
descriptors to a case where properties based on electronic structures play an important
role in the rate-limiting step of the degradation processes.
Fig. 13.7 Degradation of compounds with MCO extracted from wastewater. a Plausible mechanism
of oxidation. The existence of a mediator is expected. b Degradation reactivity was examined for
20 compounds with ABTS as a mediator and 8 compounds were degraded
Fig. 13.8 Simple combination of QC parameters can classify the 20 compounds into reacted and
not-reacted groups. The prediction model for the degradation was constructed by using linear SVM
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