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11 Electronic-Structure Informatics for Drug Development 205
https://t.me/med1917
on E-ESI. The applicability of this informatics method has been rationalized by
showing its relationship with drug activity parameters.
In addition to comments on the plausible ESI methodologies, the detailed explanation of the E-ESI descriptors has been presented. The common feature of the
suggested descriptors is that each has a background justifiable with theories in chemistry and physics. Although there is still arbitrariness in the choices of descriptors,
the factors discussed in quantum chemical analyses are considered reasonable as
descriptors for “explainable” machine learning models.
Further challenges in the electron-level descriptor development for drug discovery
would be autonomic definitions of essentially important machine learning descriptors. It will be done not through only versatile applications of multi-scale computer
simulations, but through the development of emerging methods for data mining. For
these approaches, both ideas on practical computation and theoretical methods are
demanded.
It is noted that although domain-specific information in ESI is appreciably
different from the others, it should be appropriately and skillfully coupled with
other data-centric sciences. There are a variety of concepts, practical ideas, computational methods, and techniques developed so far in chemoinformatics, bioinformatics, materials informatics, and computer sciences developing machine learning
and artificial intelligence (AI). There are no needs to develop independent informatics approaches. Ongoing efforts to develop compound methods for ESI are being
made in our laboratory, and the results will be published in future.
Acknowledgements The author expresses sincere thanks to Prof. Johann Gasteiger and Prof.
Hiroko Satoh for their careful reading and suggestions on the present manuscript.
Technical Note This manuscript was originally written by the present author and has been linguistically updated by referring to the suggestions for rephrasing from ChatGPT4 of OpenAI [
Grammarly [
Japanese language, which is the native language of the present author. After machine-suggested
modifications, the final draft was completed under the responsibility of the present author. This note
is added to indicate that this is the first trial for the author to publish a paper through these man–
machine interactions (MMI). MMI would be a key even in sciences and technologies of molecules
and materials. Emerging tools and applications of productive MMI are highly expected for (even
unexpected) development of functional molecules and materials.
]. Using the DeepL translator [44], the contents have been checked even in the
43
42
]and
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Chapter 12
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Data-Driven Chemistry for Developing
Organic Synthesis Routes for Functional
Chemicals
Kenji Hori, Shohei Majima, and Toru Yamaguchi
12.1 Introduction
In recent years, drug and material design have been using chemoinformatics to create
new compounds with medicinal or useful properties [
synthetic routes for these compounds is conducted in the following order: (i) synthetic
chemists create several synthesis routes based on their experience and intuition, (ii)
these routes are verified and obtained the target through experiments involving trial
and error, (iii) the reaction conditions are optimized on the basis of a plausible reaction
mechanism.
There were also many attempts to create synthetic routes using computers. Corey
has developed LHASA, an organic synthesis route design system (SRDS), along
with the concept of the retrosynthesis [
progress through the efforts of Gasteiger in Germany [
and Funatsu in Japan [
ered that computers could not handle a variety of compounds and that only synthetic
chemists could solve the problem. For synthesis targets required multi-step reactions,
6, 7]. Although these enthusiastic efforts, it was often consid-
3]. Since the 1980s, SRDSs made remarkable
1, 2]. The development of
4], Jorgensen in the U.S. [5],
K. Hori (
R&D Center of Functional Materials, Transition State Technology Co. Ltd, Ube 755-0097, Japan
e-mail: kenji@tstcl.jp
Faculty of Engineering, Yamaguchi University, Ube 755-8611, Japan
National Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, Japan
S. Majima
Core Technology Department, Shionogi Pharma Co., Ltd, Amagasaki 660-0813, Japan
e-mail: shohei.majima@shionogi.co.jp
T. Yamaguchi
Division of Computational Chemistry, Transition State Technology Co. Ltd, Ube 755-0097, Japan
e-mail: tor@tstcl.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
H. Satoh et al. (eds.), Drug Development Supported by Informatics,
https://doi.org/10.1007/978-981-97-4828-0_12
B
)
209
210 K. Hori et al.
https://t.me/med1917
+
+
+
Database
(i) Creation of New
Synthesis routes using
AIPHOS/TOSP
Fig. 12.1 Overview of data-driven synthesis route development
(ii) Digital verification using transition
state database
(iii) Ordering the routes
+
(iv) High throughput
verification of synthesis routes
SRDS routes often diverge since they do not take into account their synthetic possibility. Therefore, the software has not always been accepted by synthetic organic
chemists.
It has been recently developed SYNTHIA™ [8] on the basis of many literature
data and AiZyntFinder [
9], ASKCOS [10] using machine learning of big data (AI-
SRDS). The possibility of the target synthesis relies only on literature descriptions so
that there are no guarantees whether or not the target is obtained. Therefore, trial-anderror approaches are required. This is not much different from the old fashion route
developments. Recently, synthetic robots will help to reduce experimental efforts
11].
[
While there have been many attempts to use computers for developing synthesis
routes, their potentials have to still be determined by synthetic chemists. We have
proposed to change this situation by introducing a method that combines chemoinformatics and computational chemistry. This method evaluates synthesis routes of
target compounds in the order shown in Fig.
12.1.
i. Creation of new synthesis routes using AIPHOS/TOSP [12]. TOSP creates
synthesis routes using transforms, i.e., knowledge-based Information of formation and/or cleavage positions of bonds, types of substituents, etc., involved
in name reactions. Therefore, we think that AIPHOS/TOSP has a potential to
create synthetic routes that have not been considered before.
ii. Evaluation of synthesis routes by quantum chemical (QC) calculations utilizing
the database (TSDB/QMRDB [
verification is called the digital screening [
iii. Adoption of a small number of routes with high potential for synthesis and their
ranking on the basis of availabilities of reagents and/or experimental easiness,
according to the results of the digital screenings.
iv. Experiments to confirm whether or not the target compound can be synthesized
according to the order of synthetic routes ranked.
This procedure, called the data-driven synthetic route development, can significantly shorten the period for synthesis route developments. The introducing data
13], see below) that we have developed. This
14].
12 Data-Driven Chemistry for Developing Organic Synthesis Routes … 211
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science and theoretical chemistry into the synthesis route development has the potential to fundamentally change the way of organic synthetic chemistry. The new procedure improves the weaknesses of the traditional method, which requires a lot of
trial-and-error experiments. In this paper, we describe the details of (ii) and some
results of research on (iv) in a NEDO project named “Development of Synthesis
Process Design Technology” launched in 2022, joining one of the NEDO national
projects on flow synthesis [
for developing functional chemicals using process informatics, which organically
combines computational chemistry, chemoinformatics, and experimental chemistry.
15]. The project aims to significantly shorten the period
12.2 Digital Screenings Using QC Calculations
12.2.1 TOSP as the Standard SRDS of the NEDO Project
As TOSP creates synthesis routes using knowledge from name reactions, it does
not depend on big data from chemical journals. The NEDO project is developing
AIst-syn (Unpublished result), another SRDS that shares the same concept as TOSP.
The mechanism of the synthesis route from TOSP is easily analyzed using QMRDB/
TSDB contents. However, TOSP also cannot escape from the same route divergence
problem as other SRDSs, either. In order to use SRDS for the synthesis route development, it is necessary to evaluate their synthetic possibility and to eliminate the
divergence problem.
Computational chemistry is a useful tool for understanding known reactions and
has elucidated reaction mechanisms in detail, including the transition state (TS)
structures. This property implies that QC calculations can confirm whether the target
can be synthesized by new routes that have not previously been examined. This
property can be successfully exploited to develop new synthetic routes for target
compounds by combining SRDS and QC calculations.
12.2.2 Effects of Digital Screenings
The digital screening is effective in avoiding the divergence problem since it verifies
synthetic routes in the reverse order of experiments. In order to explain the reason,
let us consider an example shown in Fig.
Obtaining Precursors 1 and 2 are essential to ascertain whether or not Route D
produces the target compound. Precursor 1 may be synthesized using one of Routes
D1a-D1c and Precursor 2 using Routes D2a-D2e. It may not be necessary to try
them all. For the attempts to synthesize the target using other routes such as Route
A, we have to synthesize the precursor needed for the route. The effort for the route
is wasted if the route does not provide the target.
12.2.
212 K. Hori et al.
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Route A1
Route A2
Route A
Route B
Target
Route C
Route D
+
Precursor 2
Fig. 12.2 Effect of digital screening on synthesis route evaluations
Route A3
Precursor 1
Route D1a
Route D1b
Route D1c
Route D2a
Route D2b
Route D2c
Route D2d
Route D2e
Verification of synthesis routes using the digital screening is performed in the
reverse order of the experiments, i.e., at the beginning, Route A~D are evaluated. If
the digital screening resulted in judging that Routes A–C do not work but Route D
is capable of synthesizing the target compound, there is no need to validate routes
A1–A3. The digital screenings result in significantly reducing the number of QC
calculations.
If the digital screenings evaluate that Route D1a synthesizes Precursor 1 and
Routes D2a and D2e produce Precursor 2, we can use the synthesis routes indicated
in red. Therefore, it is likely that only four experiments of Routes D1a, D2a, D2e,
and D are enough to synthesize the target.
As demonstrated in this example, the digital screening reduces the number of
actual experimental efforts to be performed very much. In addition, the calculated
activation free energy allows to roughly set the reaction temperature. The calculation
of solvent effects provides insight into the solvent to be used in experiments. The
16, 17
NEDO project employed the QM/MC/FEP method [
], which can estimate
solvent effects with a high degree of accuracy.
In the data-driven synthesis route development, retrievals of TSDB/QMRDB are
not used for predictions but for making initials of new calculations for proposed
synthesis routes. The digital screenings have to be always performed to obtain new
TSs for evaluating possibilities of synthesis routes. This is very different from predictions the AI-SRDSs make through models of machine learning with big data. Therefore, the digital screening predicts the possibility for synthesizing the target with
high probabilities.
12 Data-Driven Chemistry for Developing Organic Synthesis Routes … 213
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12.3 Development of TSDB/QMRDB and Technologies
for Digital Screenings
12.3.1 TS Motif, the Key to Reducing Computational Times
In organic chemistry, reactions proceeding under the same reaction mechanism, for
example, Diels–Alder reaction, Ene reaction, and so on, are grouped together as
name reactions. Our experience in analyzing many of those reactions revealed that TS
structures of the same name reaction are similar to each other even though substituents
of substrates differ greatly.
As an example, a reaction is discussed in which an amide is formed through the
condensation reaction of a carboxylic acid and an amine. This reaction forming an
amide consists of two elementary reactions, one is the reaction of the condensation
reagent and two molecules of carboxylic acid to form an intermediate, and the other
is amide formation through the reaction of the intermediate.
The lower left in Fig. 12.3 displays the TS structure (TS_S) of the first elementary reaction with simple substituents (R1 = R3 = CH3), where it takes a sixmembered ring consisting of two oxygen, carbon, nitrogen, and two hydrogen atoms.
18
The intrinsic reaction coordinate (IRC) [
relay is involved in this reaction mechanism. The O1-H and C-O2 distances in this
structure were calculated to be 1.698 and 2.281 Å, respectively. The six-membered
ring in TS_S is retained in TS_C with complex substituents (R1 = 2-CF
= C
). The corresponding distances (1.803 and 2.096 Å) of TS_S are not signif-
6H11
icantly different from those in TS_C. The characteristic geometry, in which bond
formation and/or dissociation occur is called a “TS motif”. This example indicates
the similarity of the TS motifs. In some cases, TS motifs of different name reactions
are similar if they proceed under a similar reaction mechanism.
] calculations indicated that the proton
,R3
3C6H5
Fig. 12.3 Similarity of TS motifs
214 K. Hori et al.
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12.3.2 How to Optimize a New TS Structure Using the TS
Motif
The purpose of the digital screenings is to verify new synthesis routes for the target
to be feasible. However, it has to be emphasized that the possibility of synthesis
routes does not determine on the basis of TSDB/QMRD database searches but assess
through new TS calculations and the activation barrier heights. Therefore, we adopted
a new method which utilize a TS motif to create an initial structure for optimizing
the TS for a given synthesis route. The present method is called the TS motif method,
which follows the steps below.
i. The substituents are given at the corresponding positions in the TS motif. In the
example in Fig.
reagent are substituted with R1 = 2-CF
initial structure for the TS optimization.
ii. The TS optimization is performed using the initial structure with the fixed TS
motif within the red circle (the partial geometry optimization), followed by the
TS optimization without fixed parameters.
The optimized TS structure is used for performing IRC calculations, followed
iii.
by structure optimizations of reactants and products. This will be confirmed that
the obtained TS connects the target compound with the reactant.
We have to complete these calculations within a few hours to one day, the period
that experimental chemists without patience can tolerate. As will be discussed later,
we are constructing QMRDB, a database gathering TS motifs. We developed a
computer cloud system handling the database (Fig.
search, specifically performing the TS motif method. This program creates inputs for
the Gaussian program [
manages the conformational analysis using the Conflex program [
preliminary data of reaction analyses to QMRDB. We made a web-based manual
describing how to use the system and the TS motif method.
Even an organic chemist without experiences in computational chemistry learned
how to use the system within a week. Furthermore, he has completed digital screenings for the four-step synthesis routes of a drug for only two months as will be
given later. Once he is proficient in the system, a similar digital screening could be
completed in less than two weeks. This means that synthetic organic chemists can
complete the digital screenings of reactions for the target before s tarting experiments.
12.3, the methyl groups of the simple acid and the condensation
and R3 = C6H
3C6H5
12.4) and a program, named TS
19
], submits jobs to and downloads from the cloud system,
to create the
11
20], and registers
12.3.3 Problems in the TS Motif Method
The digital screening is a tool to compare whether one synthetic route is superior to
others. The magnitude of activation free energies is one of the factors to determine
which reaction is optimal. Therefore, in data-driven synthetic route development, it is
12 Data-Driven Chemistry for Developing Organic Synthesis Routes … 215
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WindowsTerminal
Web browser
Search resemble reactions
i Structure
Fig. 12.4 TSDB cloud system
Reaction Search
TS Coordinates
TS Motif Method
Conformation search
TSDB Cloud system
TSDB
QMRDB
necessary to find the most stable TS structure in the reaction concerned. However, the
TS motif method is unlikely to locate the most stable TS conformation for compounds
with many substituents [
21].
The simple TS motif of butadiene + ethylene was applied to optimize the TS
for the reaction of N-methylpenta-2,4-dienamide + acrolein, and the resulting TS_
0 is showninFig.
12.5. IRC calculations confirmed t hat the TS connects the reac-
tant with the product, 6-formyl-N-methylcyclohex-2-ene-1-carboxamide. The other
conformers are derived from TS_0. TS_2, 3, 4, and 5 were calculated to be less stable
by 0.3 to 5.7 kcal/mol than TS_1, the most stable and more stable conformation by
3.2 kcal/mol than TS_0.
Another example is a TS conformation analysis of a relatively large transition
metal complex in Scheme
shown in Fig.
12.6, this is a large calculation consisting of 555 basis functions. Even
12.1 [22]. Since the molecule has seven benzene rings
for such a large molecule, the calculations including TS search and its conformation
TS_0 TS_1 TS_2 TS_3 TS_4 TS_5
0.0 -3.2 -2.9 0.6 0.3 2.5
Fig. 12.5 Results of TS conformational analysis for Diels–Alder reaction
Gaussian 09 program
B3LYP/6-31(d)
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