natural compounds such as morphine from opium poppies in the early nineteenth century. Medicinal chemistry drastically changed with the development of aspirin, derived from willow tree extracts, marking the beginning of the synthetic chemistry era. As scientiﬁc knowledge expanded, so did the methodologies, transitioning from serendipitous ﬁndings to targeted chemi cal synthesis and high-throughput screening. In recent decades, the advent of computational techniques has revolutionized drug design, enabling researchers to model molecular interactions (mainly comparing structure-based drug design strategies) and predict biological activity (in special considering ligand-based drug design strategies) with unprecedented precision. This shift towards computational drug design has accelerated the discovery process,

V. G. Maltarollo · R. J. Alves (✉) Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil e-mail: dylancover@gmail.com

E. Shevchenko Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard-Karls-Universität Tübingen, Tübingen, Germany

T. Kronenberger Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard-Karls-Universität Tübingen, Tübingen, Germany

Tübingen Center for Academic Drug Discovery & Development (TüCAD2), Tübingen, Germany

Excellence Cluster “Controlling Microbes to Fight Infections” (CMFI), Tübingen, Germany

Partner-site Tübingen, German Center for Infection Research (DZIF), Tübingen, Germany

School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug Design, Computer-Aided Drug Discovery and Design 3,

https://doi.org/10.1007/978-3-031-76718-0_1

4 V. G. Maltarollo et al.

making it more efﬁcient and allowing for the development of novel therapies with greater speciﬁcity and reduced side effects.

Keywords Computer-Aided drug discovery · Medicinal Chemistry · Drug Design · LBDD · SBDD

1 Historical Perspective on Drug Discovery

and Computer-Aided Drug Discovery (CADD)

The process of drug discovery has drastically changed over the centuries adapting to the available knowledge and new technologies. Many early drugs were discovered through serendipity or empirical observation and a lot of ingenuity from scientists. Since ancient times, humankind searched for something to relieve pain and cure diseases. Among the several commonly used remedies were plant extracts, generally used as beverages. Several of them did work, others were inactive or even caused toxicity. With the scientiﬁc expansion in the nineteenth century, the development of analytical methods for isolation, puriﬁcation, and characterization of pure compounds from plant extracts led to the discovery of several plant-based medicinal compounds. To mention some, morphine (Fig. 1.1) is one of the several alkaloid components of opium, the exudate obtained from Papaver somniferum was isolated by German apothecary Friedrich Sertürner in 1817. This fantastic painkiller is used even today to relieve severe pain [1].

From the extracts of willow tree (Salix spp.), salicin, a plant-derived glycoside used to relieve several types of pain, was isolated in 1826 by a French chemist named Henri Leroux. Chemical hydrolysis and oxidation furnished salicylic acid (Fig. 1.2), which was also a component of the plant. Salicylic acid was eventually used as a

Fig. 1.1 Molecular structure of morphine

Fig. 1.2 Hydrolysis and oxidation of salicin into salicylic acid followed by acetylation into acetylsalicylic acid

1 Echoes from the Past, Visions from the Future: A Journey into... 5

Fig. 1.3 Molecular structures of atoxyl, salvarsan, and neosalvarsan

Fig. 1.4 Molecular

structures of prontosil rubrum and sulphanilamide

painkiller but was replaced by acetylsalicylic acid, a less irritant drug. Acetylsalicylic acid was ﬁrst synthesized by French chemist Charles Frédéric Gerhardt in 1853. Later, Felix Hoffmann, a German chemist working for Bayer company, acetylated salicylic acid to create Aspirin, the trade name of acetylsalicylic acid which is also used nowadays [2]. Thus, acetylsalicylic acid is a synthetic drug inspired by a natural product, sali cin.

Several drugs of synthetic origin were developed in the twe ntieth century due to advances in synthetic routes and analytical techniques. In 1905, the French pharmacist Pierre Jacques Antoine Béchamp synthesized atoxyl (Fig. 1.3, left), the ﬁrst aromatic arsenical compound which displayed trypanocidal activity, although it was too toxic. In 1907, the German chemist Alfred Bertheim synthesized salvarsan (Fig. 1.3, centre). The structure of salvarsan was much later revised, and it was shown that the compound does not have a double bond between arsenic atoms and is composed of a mixture of cyclic species, majorly comprising 3- and 5-membered arsenical ring derivatives (Fig. 1.3, right structures) [3, 4].

Paul Ehrlich, another famous German scientist, is well known as the father of chemotherapy and who coined the term “magic bullet”, meaning a compound that would cure disea se selectively, without causing host toxicity. Bertheim and Paul Ehrlich, inspired by the work of Béchamp, synthesized more than six hundred arsenical compounds, among which salvarsan stood out as an effective drug to treat syphilis, being introduced into the market in 1910. Due to solubility issues, neosalvarsan (Fig. 1.3), a water-soluble derivative, was also prepared.

The dye industry was the origin of the discovery of sulph onamides, an important class of synthetic antibacterial drugs. Gerhard Johannes Paul Domagk, a German physician at that time working for Bayer, from the IG Farben cartel, described, in

6 V. G. Maltarollo et al.

Fig. 1.5 Molecular structures of penicillin and 6-APA

Fig. 1.6 Molecular structures of noradrenaline, adrenaline, and salbutamol

1935, that prontosil rubrum (Fig. 1.4), an azo-dye, although inactive against Strep- tococci in vitro, protected mice from infection. Later, it was discovered by French chemist Ernest Fourneau and colleagues that prontosil is converted in vivo to sulphanilamide (4-aminobenzenesulphonamide, Fig. 1.4), the ﬁrst of a series of sulpha drugs [ 5 ].

The serendipitous discovery of penicillin (benzylpenicillin, Fig. 1.5) by Scottish physician and microbiologist Alexander Fleming in 1929 was a breakthrough in drug discovery. Penicillin saved the lives of thousands of soldiers in World War II. Besides, the production of 6-aminopenicillanic acid (6-APA, Fig. 1.5) from benzylpenicillin allowed for the preparation of several analogues that were resistant to acid hydrolysis and to beta-lactamases produced by penicillin-resistant bacteria. Finally, the discovery of penicillin set a new era in drug discovery, namely, the search for antibiotic compounds from fungi and other microorganisms [5 ].

The develo pment of salbutamol (albuterol, Fig. 1.6), a beta-2 adrenergic agonist (bronchodilator) drug by Scottish pharmacologist David Jack in 1966, was an ingenuous process in a time where the biological function of adrenaline alog with the receptor occupation theory for the drug action was the only guide for researchers to infer the prototypical binding mode. Adrenaline acts on alfa, beta-1, and beta-2 adrenergic receptors inducing vasoconstriction, increase of heart beating, and bronchodilatation, respectively. Noradrenaline, which lacks the N-methyl group, has comparatively lower activity on beta receptors. Thus, it was rationalized that the presence of N-alkyl groups could enhance selectivity for the beta receptors. Replacing the N-methyl group successively with increasingly bulkier groups showed that the tert-butyl group was the best to confer selectivity to the beta-2 receptor. The modiﬁcation of the catechol moiety, by replacement of the 3-hydroxy group for a hydroxymethyl group, makes the compound resistant to the action of the enzyme

1 Echoes from the Past, Visions from the Future: A Journey into... 7

catechol O-methyl transferase (COMT), thus prolonging its action as compared to the catecholamines. Since its launch in 1969, salbutamol has ever since been used in the management of asthma [6, 7].

The history of computer methods applied to drug discovery dates from the early 1960s, marked by the discovery of quantitative structure–activity relationships studies reported by Prof. Corwin Hansch’s research group. Those ligand-based studies were later supported (1970–1980s) by the rise in the number of 3D structures determined by X-ray crystallography. This paved the way for the design of novel structure-based drugs, focusing on human haemoglobin [8]. In that decade, there was an increase from 69 crystallographic structures, in 1980, to 365 in 1989, leveraging an average of 30 new structures deposited per year [9]. In other words, the amount of information regarding structures of proteins was, at the time, modest. The Protein Databank (PDB) [10] was created in 1971 and ﬁrst established in the Brookhaven National Laboratory and the Cambridge Crystallographic Data Center [11]. This initiative has remained a beacon for structural information for more than 50 years.

As the data generated increased and became more available, so did the algorithms to process it. The computer methods also gained interest outside of the academic readership and, in October 1981, Fortune Magazine published the cover of an article entitled “Designing drugs with computers at Merck” calling it “The Next Industrial Revolution” [12]. The increasing number of projects and acceptance of computational methods as essential tools in drug design even led some authors to suggest a change from the term “computer-aided-” to “computer-driven drug desig n ” (CDDD) [13].

Classical concepts, such as Emil Fischer’s “lock-and-key” theory introduced in 1894 [14], inﬂuenced the development of most computational methods to predict protein-ligand binding. The lock-and-key derived computer-aided drug design (CADD) models not only encompass a good geometrical ﬁt but also protein-ligand complementarity due to hydrophobic and polar interactions. Apart from interactions, modern molecular modelling considers both ligands’ and binding sites’ ﬂexibility, binding, and distortion energies, solvation, and entropic effects [15].

Forty years later, the subject of magazines [16], newspapers [17], and even news pieces from prestigious journals, such as Nature Medicine [18], featured the rise of Artiﬁcial Intelligence (AI) applied to drug discovery. This wave was not met without its share of questions, scientists from the 1970s, investigated the actual impact of rational drug design by evaluating whether the incremental potency experimentally observed was related to interactions with the molecular target [19]. Similarly, in 2021, the role of AI-based drug design was also challenged [20] looking speci

ﬁcally at how AI needs to evolve and produce novel data to measure and ensure its actual impact on drug design.

Regardless of the method’s nature itself, current CADD approaches are inserted in almost every step of drug discovery. They became integral to industry and academic research’s backbone, given the brought layer of structural rationality to the hit identiﬁcation and its further development into a lead [21] such as understanding of protein-ligand interactions as well as physicochemical and structural features of bioactive compounds, which are the foundation of medicinal chemistry. CADD

8 V. G. Maltarollo et al.

Fig. 1.7 Methods related to SBDD and LBDD strategies and the interchangeability of information between computational procedures, databases, and experimental testing

offers a wide range of methods and techniques to gain insights into protein-ligand binding and phenotypic activities for providing valuable data to support medicinal chemistry efforts [22–24].

CADD techniques provide insights into atomistic details beyond what can be obtained with the current experimental methods. Additionally, molecular modelling works with large pools of chemical data, allowing great chemical diversity to be reasonably analysed and generating new hypotheses capitalizing on large data sets. The research and development process is steadily modernized through the rapid adoption of technologies and techniques that have the potential to drastically improve the drug development pipeline. Despite the speed at which these approaches are emerging, a comprehensive understanding of their applicability and limitations is to be achieved [25].

For instance, CADD can be used as part of major drug design strategies in combination with experimental methods. Of course, the main purpose of computational methods is not to replace experimentation but to support multidisciplinary teams with rational and faster strategies. Considering this, they are often used in parallel or followed by chemical series synthesis and evaluation as well as structural determination using X-ray crystallography. In this sense, the drug design strategies could be classiﬁed as structure-based drug design (SBDD) or ligand-based drug design (LBDD) depending on the available information of the studied system [26, 27]. The molecular design process could comprise a single or several methods in the pipeline dependi ng on the amount of available information (Fig. 1.7).

Despite the many computational methods to be mentioned in the next session and this book, one of the main key points is the development of reliable force ﬁelds. A

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