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HirokoSatoh
KimitoFunatsu
HiroshiYamamotoEditors
Drug Development
Supported
byInformatics
Drug Development Supported by Informatics
Hiroko Satoh · Kimito Funatsu · Hiroshi Yamamoto
Editors
Drug Development
Supported by Informatics
Editors
Hiroko Satoh
Department of Chemistry
University of Zurich (UZH)
Zurich, Switzerland
Research Organization of Information
and Systems (ROIS)
Tokyo, Japan
Hiroshi Yamamoto
pirika.com LLC
Yokohama, Japan
Kimito Funatsu
Data Science Center
Nara Institute of Science and Technology
Ikoma, Nara, Japan
ISBN 978-981-97-4827-3 ISBN 978-981-97-4828-0 (eBook)
https://doi.org/10.1007/978-981-97-4828-0
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Singapore Pte Ltd. 2024
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Preface
https://t.me/med1917
Today, the world faces a range of global healthcare issues. New virus outbreaks are
always a potential threat, as the recent pandemic is still fresh in our memory. The
geographical distribution of infectious diseases is impacted by climate change. The
prevalence of antibiotic-resistant bacteria is becoming a growing concern. Animal
experiments, which are needed to develop better and safer drugs and therapies, are
becoming subject to more strict regulations, with the trend toward zero animal testing.
Regulations are becoming tight also for chemicals that are used in labs or industries
for developing new drugs. Chemical compounds including drugs can contribute to
micropollutants, which have been recognized as a serious environmental problem.
These diverse issues are becoming additional concerns for those involved in drug
development.
The pharmaceutical industry and market are facing also fundamental issues, e.g.,
access to medicine. On the one hand, patients can be at risk of running out of essential
medication, a reason being a decrease in the number of generic drugmakers, caused by
the falling prices of off-patent drugs. On the other hand, patients may not have access
to certain treatments, such as gene therapy, due to their exorbitant cost resulting from
high expenses for drug development.
In the last decade, information and computer technology, especially artificial intelligence (AI) and machine learning (ML) technology, has made enormous progress
and rapidly conquered a wide variety of domains including natural science. They
are assumed to have high potential to help to find solutions for our global problems. Predating the current trend, the field of pharmaceutical science has a long
history of using informatics technology. That history dates back to the nineteenth
century, when the idea of quantitative structure-activity relationships (QSAR) study
for drug discovery was formulated. Nevertheless, to cope with the growing global
complex problems, the field of pharmaceutical science must continuously integrate
advanced technology and explore new potentials of interdisciplinary cooperation. It
is important to constantly update the information and knowledge on both, methods
and domains.
However, the range of technology and applications related to pharmaceutical
and healthcare is so huge that it is a challenge to overview this expanding field.
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Furthermore, when trying to adjust informatics methods to the problems of a specific
field, one may encounter difficulties because of a lack of information explaining how
to apply them to individual problems. Choosing appropriate methods and parameters
out of many options can be also challenging. For those in the field of each domain, it
is difficult to find informatics experts who could help them apply the technology to
their problems. For those in the field of informatics, it can be difficult to find good
applications to test their methods.
Therefore, we started this book project entitled “Drug Development Supported
Informatics”, aiming to provide an overview of the state-of-the-art of informatics
by
and its applications in drug development for a wide spectrum of readers from learners
to professional scientists in academia and industry. This book focuses on the basic
research stage of drug development, with contributions from experts at the forefront
of these fields. We would like to express our deepest gratitude to all the chapterauthors for their valuable contributions.
This book describes several informatics methods for regression, classification,
clustering,
e.g., convolutional neural network (CNN), recurrent neural network (RNN), graph
neural network (GNN) and generative adversarial network (GAN) with different
types of learning methods, including self-supervised learning, unsupervised learning,
transfer learning and reinforcement learning. It discusses also advanced language
models, data assimilation and image processing, which are extensively used in current
molecular and material science.
Regarding application targets, this book covers all steps in the basic research
phase,
tion of designed molecules, reaction and synthetic design, and analysis and evaluation of synthesized molecules. The topics extend to biomaterials design, pharmaceutical formulation and drug delivery. Interdisciplinary collaborations between
advanced microscopies and informatics to “see” a better view of biomolecules or
living cells are also presented. From the view of applied informatics, the content of
this book is related to chemoinformatics, bioinformatics, materials informatics and
measurement/metrology informatics.
Chapter 1 provides a concise overview of AI trends in drug discovery research.
This
this chapter.
Chapter 2 offers tutorials specially designed for beginners. It demonstrates a
step-by-step
anesthetics.
The topic of Chapters 3 and 4 is the use of Bayesian inference to determine
area of the chemical space to explore to achieve an efficient inverse modeling,
an
a kind of generative model for molecular design. Chapter
molecular
1960s and describes the concept of the inverse modeling using Bayesian inference,
which was proposed by the chapter-authors in 2010. Chapter
methods
desired properties (inverse modeling) and describes their implementation of Bayesian
prediction and generation. It treats several types of neural networks,
i.e., prediction of property and activity, molecular design, in silico evalua-
chapter is a good guide to the following chapters. We recommend starting with
process of using informatics methods to design candidates of inhaled
3 focuses on an inverse
modeling. It reviews the history of inverse modeling in chemistry since the
4 discusses modeling
to predict properties (forward modeling) and to design molecules with
Preface vii
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inference for an inverse modeling. This chapter illustrates how these models work
even with limited data as showing this chapter-author’s successful applications to
materials design.
In Chapters 5 and 6, GNN and its applications to molecular design are presented.
GNN can be used with several learning methods, and these chapters highlighted reinforcement learning (RL) for efficient search for desirable drugs. Chapter
detailed theoretical background of GNN and RL as well as descriptors used for molecular modeling. Chapter
molecules with desired properties from subgraphs using GNN-RL. GNN is currently
one of the most popular methods in molecular informatics. These sections will be a
good guide for people who are interested in applying these authors’ methods or in
simply trying GNN in their research.
Chapters 7 and 8 provide insightful reviews and perspectives on language models
for molecular discovery. These chapters are presented by pioneering and worldleading groups, who have successfully applied modern language models to molecular
science, achieving top-level scores in applications, such as synthetic design and
reaction prediction.
Chapter 9 presents innovative approaches based on drug-protein-genome interaction networks for drug discovery and drug repositioning. These methods are expected
to be used for development of drugs with less side effects and discovery of new efficacy of existing drugs, as well as for drug discovery from properties of diseases in
an inverse way.
Chapter 10 discusses a fundamental issue, the selection of molecular descriptors
for ligand-based drug discovery. This chapter focuses on the necessity of threedimensional (3D) descriptors, i.e., conformational properties of ligand molecules,
compared to 2D-descriptors, i.e., 2D-structure environment by means of circular
atom neighborhoods. It is shown how the 2D and 3D properties influence the results
of activity prediction and molecular screening.
Chapters 11, 12 and 13 discuss the use of quantum chemical data for molecular and
reaction design. Chapter
with their applications to clustering and screening of drug molecules. Chapter
demonstrates a project for synthetic design using a transition state (TS) database. The
TS database, where TS motifs are associated with name reactions, is successfully
applied to the evaluation of synthetic routes proposed by a rule-based synthetic
design system. Chapter
which is data-driven chemistry using descriptors on the basis of theoretical chemistry,
especially quantum chemistry and ab initio molecular dynamics simulations. Some
projects of this chapter’s authors, including a study for faster and accurate quantum
chemical data acquisition, are also presented.
Chapter 14 presents a groundbreaking strategy for toxicity prediction. The key
idea is introducing an original three-layered model, which integrates essential components for toxicological manifestation and evaluation, i.e., chemical structures, pharmacokinetics, in vitro test data and in vivo effects. The details of the strategy, system
architecture and prediction flow are demonstrated.
6 demonstrates this chapter-authors’ method to generate
11 proposed new descriptors based on electronic structures
13 gives a short review about quantum-chemoinformatics,
5 describes
12
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Chapters 15 and 16 present topics on measurement/metrology informatics. In
, the authors obtained information on single biomolecules with very high-
Chapter
spatiotemporal resolution by integrating high-speed atomic force microscopy (HSAFM) and biomolecular dynamics simulations with data assimilation techniques.
Their recent attempt to apply HS-AFM to cell-biology is also described. Chapter
presents another advanced instrument, lattice light-sheet microscopy. This type of
high-spatiotemporal resolution live cell imaging enables visualization of cellular and
intracellular dynamics. This chapter describes strategies to overcome several challenges to extract maximum information from the big data obtained in the measurement by combining informatics techniques, such as image processing and deep
learning.
biomaterial design is to deal with the complex interactions between biomolecules,
cells and biomaterials. In this chapter, the author demonstrates the prediction of
protein adsorption onto monolayers and polymer films by training neural networks
with self-assembled monolayers’ molecular structure data.
Chapter
technology for process control in pharmaceutical continuous production processes.
Near infrared spectroscopy plays an important role in real-time monitoring
throughout the production process from raw materials to the final products. This
chapter presents an overview of this approach. Chapter
review on Hansen Solubility Parameter (HSP) method for drug development. HSP
gives a solubility index applicable from macroscopic matter (e.g., living organs and
materials) to small molecules ( e.g., drug molecules). This chapter demonstrates how
to use HSP in several scenarios in drug development, including isolation of target
compounds, pharmaceutical formulation and drug delivery.
all his energies to make use of informatics, especially chemoinformatics, at his
company’s production site. His colleagues regularly told him, “We hope for your
success, but we are not counting on you”. Their words conveyed confidence and
pride in their skills and experience, which did not require the assistance of such
technology. But at the same time, they feared that new technology might take over
their work in the future. This fear from the twentieth century is now becoming a
reality. We have to find a solution to how to work in this time when new innovative
technologies are rapidly emerging.
review all of them in one book. Even so, we believe that this book is a unique volume
that integrates diverse informatics and drug development techniques together with
theoretical details and many applications. We hope that this book will be of help
to readers to explore new opportunities for collaboration between pharmaceutical
science and informatics as an independent scientist who can handle the advanced
informatics technology.
15
16
Chapter 17 provides topics of biomaterial informatics. One of the challenges in
Chapters 18 and 19 discuss pharmaceutical formulation and drug delivery.
18 describes Process Analytical Technology (PAT), which is an advanced
19 provides a thorough
One of the editors spent more than 25 years in chemical industry. He has devoted
The topics treated in these fields are so wide ranging that it is impossible to
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AI and ML are tools, which can be a great help for your science. If this book gives
a clue to unlocking your future, there is no pleasure greater for us.
Zurich, Switzerland and Tokyo, Japan
Ikoma, Nara, Japan
Yokohama, Japan
February, 2024
Hiroko Satoh
Kimito Funatsu
Hiroshi Yamamoto
Contents
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1 The AI Trends in Chemical Space for Drug Discovery ............ 1
Takuto Koyama and Yasushi Okuno
2 Screening Methods for Drugs Using Chemoinformatics
Methods for Beginners ......................................... 9
Hiroshi Yamamoto
3 Data-Driven Molecular Structure Generation for Inverse
QSPR/QSAR Problem ......................................... 47
Tomoyuki Miyao and Kimito Funatsu
4 Materials Informatics with Limited Data ........................ 61
Ryo Yoshida
5 Primer on Graph Machine Learning ............................ 87
Masatsugu Yamada and Mahito Sugiyama
6 Subgraph-Based Molecular Graph Generation ................... 103
Masatsugu Yamada and Mahito Sugiyama
7 Language Models in Molecular Discovery ....................... 121
Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan,
Teodoro Laino, and Jannis Born
8 Transformers and Large Language Models for Chemistry
and Drug Discovery ........................................... 143
Andres M. Bran and Philippe Schwaller
9 Drug Discovery and Drug Repositioning Using Computational
Methods ...................................................... 165
Yoshihiro Yamanishi
10 Two- and Three-Dimensional Molecular Representations
in Ligand-Based Approaches ................................... 175
Tomoyuki Miyao and Kimito Funatsu
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