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2 Molecular Databases 29
Fig. 2.3 Nomenclature and identification of compounds in a database, for example, captopril
Free-access online tools that allow the user to draw a chemical structure and
convert it into either SMILES, InChI, InChIKey, or SMARTS can be found
(Fig. 2.3), such as PubChem Sketcher [119]. Additionally, there are also tools that
automatically recognize the chemical structures inside a .pdf file or image and
transform into a SMILES string. Notwithstanding a manual inspection of the output
molecules is required to detect possible mistakes in the output structures [120]. Molecules can be represented according to thei r 2D or 3D coordinates, and the two most
commonly formats used are .mol2 and .sdf. These files can store the atomic
coordinates of each atom of a molecule and the information of the connectivity
among the atoms. Moreover, both formats allow the storage of physicochemical
information of the molecule, while the .mol2 format allows the storage of the
atomical charges of every atom [121, 122].
SMILES (Simplified Molecular-Input Line-Entry System) uses graph theory to
represent structures. Each atom is represented by its symbol in the periodic table,
brackets are used to indicate branching points, and numerical labels denote ring
junctions. The basic grammar of SMILES also includes isot opic information, configuration on double bonds, and chirality, also known as isomeric SMILES. Later,
IUPAC (International Union of Pure and Applied Chemistry) developed its own
linear notation for representing chemical structures, InChIKey (International Union
of Pure and Applied Chemistry Key), based on the InChI identifier. A compound 2D
structure image can be generated using MarvinSketch [123].
30 D. Q. de Azevedo et al.
Moreover, there are available open-source molecular DBs that can be used during
drug design approaches (Tables 2.3 and 2.4 in this chapter). Once the database is
already built and curated (see Sect. 2 of this chapter), or obtained either open source
or commercial, it is possible to proceed to determine helpful information for the drug
design process, with free-access online tools. One can cite NERDD [124], which
offers access tools for drug discovery. NERDD hosts tools for predicting the sites of
metabolism (FAME) [125 ] and metabolites (GLORY) [126] of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit
Dexter) [127], and for identifying natural products and natural product derivatives in
large compound collections (NP-Scout) [128]. NERDD also has a tool that predicts
if a small organic compound is an inhibitor of different human CYP450 isoforms
(CYPlebrity) and another that classifies molecules into skin sensitizers and
nonsensitizers [129]. These online tools also allow the prediction of the absorption,
distribution, metabolism, excretion, and toxicity (ADMET) profile of a single or a
list of molecules, such as ADMETlab [130] and PhaKinPro [131], among at least
18 free web servers capable of predicting ADMET properties, which have been
discussed elsewhere [132].
Additionally, the synth etic accessibility score is another helpful metric that allows
to determine the synthetic feasibility of given a molecule. With the synthetic
accessibility score, it is possible for a user to select from a DB those compounds
with the highest synthetic feasibility [133]. SYBA [134] and GASA [135] are two
different synthetic accessibility scores that can be determined with free-access online
tools. In addition, retrosynthetic planning can also be useful, as in AiZynthFinder,
which recursively breaks down a molecule to its purchasable precursors [136].
Furthermore, one can also capture structural information from molecules. Two
common molecular fingerprints employed to capture structural information are the
Molecular ACCess System (MACCS) keys-166 bits [137] and the Extended Connectivity Finger print (ECFP6) [138]. MACCS keys and ECFP6 fingerprints encode
the structure of the molecules in strings made of bits, that is, every molecule is
represented with a string made by one and zeros. From the fingerprints, the structural
similarity among the molecules can be measured using the Tanimoto coef ficient
(Tc) [139]. Herein, if two molecules share similar structures, then they will likely
have similar bioactivities [140 ]. Thus, a molecule with a certain known biological
activity can be identified using Tc calculations in a molecular database with similar
compound’s structure possibly having a similar bioactivity to the matched molecule.
These fingerprints can be calculated, for example, by both CDK [141] or RDKit
[142] nodes of the free-available software KNIME [51]. RDKit also can be used in
the Python programming language. Last, another useful and free computational tool
to calculate the fingerprints is PaDEL [
143].
2 Molecular Databases 31
6 Chemoinformatics and Computational Tools
for Supporting and Filtering Potential Drug Bioactive
Compounds from Databases
The SMILES strings (see Sect. 5 in this chapter) avail able from the molecular DBs
can also be used to determine physicochemical properties of interest, such as SlogP
[144], molecular weight (MW), topological polar surface area (TPSA) [145], rotatable bonds (Rb), hydrogen bond acceptors (HBA), and hydrogen bond donors
(HBD). The calculation of the physicochemical properties can be performed
using the freely available software DataWarrior [146], KNIME [51] or using Python,
employing the CDK [141 ] and RDKit [142] nodes (KNIME) or RDKit package (Python). One could argue that out of these three options, DataWarrior is the
most user-friendly, as the list of SMILES strings can be imported into the program
just with a copy/paste keyboard shortcut. Once the physicochemical properties have
been calculated, the compounds can be filtered to a chosen numerical range for each
property. There are some drug-likeness guidance parameters, also called rules of
thumb by some authors, which are suggestions that help to choose the potential druglike compounds such as Lipinski’s rule of 5 (Ro5) [147, 148], Veber’s rules [149],
GlaxoSmithKline’s (GSK) 4/400 rule [150], and Pfizer 3/75 rule [151] (Table 2.5).
Compliance with either Lipinski’s, Veber’s, or GSK rules is commonly associated
with good oral bioavailability. These suggestions or rules of thumb are based on the
premise that certain physicochemical properties are directly associated with one or
more parameters of the ADMET profile. Thus, if a group of molecules has a known
ADMET profile, it is expected that other molecules woul d have a similar ADMET
profile whenever they share similar physicochemical properties. Usually, these rules
are helpful in filtering compounds that are expected to have a desirable ADMET
profile [152]. Nonetheless, one should consider that the ADMET profile of a
compound is influenced by many other aspects and not only by some physicochemical properties. Therefore, the fulfillment of one or more of these rules of thumb does
not necessarily assure obtaining the expected desirable value of a parameter of the
ADMET profile [153].
7 Databases in Drug Design and Discovery: Applicability,
Challenges, and Successful Cases
The field of drug design and discovery still faces low efficacy, off-target delivery, as
well as the time spent, and high cost associated [154]. In this scenario, the increase in
biological and chemical data, including in vitro, in vivo, clinical studies, genomics
studies, proteomics studies, metabolomics studies, gene ontology studies, and
molecular pathway data, benefits from different data repositories that have been
developed. For instance, ChemSpider, ChEMBL, ZINC, BindingDB, and PubChem
are the essential DBs for compound synthesis and screening in the drug design and
32 D. Q. de Azevedo et al.
Number of
rotatable
bonds References
Topological
polar surface
area
Sum hydrogen bond
acceptors and donors
Number of
hydrogen bond
donors
Number of
hydrogen bond
acceptors
Molecular
weight LogP
Table 2.5 Rules of thumb or guides associated with drug-likeness
≤ 500 ≤ 5 ≤ 10 ≤ 5[147, 148]
Lipinski’s rule of
<400 <4 [150]
GlaxoSmithKline
Veber’s rules ≤ 12 ≤ 140 ≤ 10 [149]
5
4/400 rule
Pfizer 3/75 rule >3 < 75 [151]
2 Molecular Databases 33
discovery process. ZINC database was the most preferred DB in virtual screening
studies with an average use of 31.2% from 2015 to 2022 [133], selecting compounds
according to their pharmacological and physicochemical properties of pharmaceutical interest [155].
Among the new drug design strategies, the search for information and computational tools is the most widely used due to its great ease of access and extremely low
cost. A DB displays various techniques and information from, for example, in silico
studies, such as molecular docking, molecular dynamics simulation, and
VS. Therefore, a given DB is designed to include the information necessary for
the first stage of drug discovery, including molecular structures, physicochemical
properties, molecular properties of ligands, and their drug-likeness properties [156].
VS is an approach that benefits from virtual DBs containing a large number of
compounds. VS allows the identification of novel hits as well as the prioritization of
compounds for in vitro testing, resulting in a significant reduction in costs and
attrition rates [154]. This pre-selection is done by virtually predicting the biological
activity of interest using different structure-based drug design and/or ligand-based
drug design (SBDD and/or LBDD) approaches. SBDD was the most prominently
used type of VS and it accounted for an average of 57.6%, from 2015 to 2022
[133]. In the early stages of drug design, in silico studies represent a relevant strategy
for reducing costs in this process [28]. DrugBank is one free-access website
containing information on potential drugs and their targets that is usually accessed
for such approaches. Drugbank is a bioinformatics and chemoinformatics resource
that contains detailed information of drugs and their biological targets (29.785
targets). It contains a detailed description of these targets, such as their type,
organism in which they occur, pharmacological function, and specific and general
functions [156].
Despite successful drug discovery approaches accessing different DBs, it has
been reported that between 0.1% and 3.4% of the chemical structures in chemical
DBs are incorrect [157], including errors in molecular DBs regarding compounds’
bioactivity data [158]. An error in the structure representation of the molecules can
lead to inaccurate QSAR predictions, erroneous hazard and risk assessments, and
wrong decisions in the early drug discovery process. In molecular DBs, it has been
identified fundamental errors in stereochemistry, valency issues, and charge imbalances [157]. Unfortunately, still in 2023, an analysis identified a subst antial number
of errors in the identifiers and chemical structures in DBs, such as PubChem,
CompTox Chemicals Dashboard, and European Chemicals Agency (ECHA). Further, the absence of these errors is not guaranteed even in government-funded DBs
[159]. Therefore, it is important to identify and correct those wrong entries and
structures in the molecular DBs.
There have been efforts with the aim of reverting the problem, such as an opensource chemical structure curation pipeline that was published and utilized successfully to standardize the nearly two million compounds in the ChEMBL DB [160]. In
addition, compounds with critical issues were also identified so that they could be
prioritized for manual curation [161]. The solution to these challenges requires
coordinated strategies and a strong finan cial commitment by the government,
34 D. Q. de Azevedo et al.
funders, and institutions. It is estimated that the current global costs of maintaining
public biomedical data repositories are under $300 million annually [162]. To
maintain this, it is important to have a collaboration from universities, pharm aceutical industry, and institutions with the government to raise awareness about the
importance of the situation with the capacity to maintain curate and create
chemical DBs.
8 Perspectives
Based on research about DBs commonly used in drug discovery, we highlight the
relevance of these libraries in this process. Natural product DBs, for example, are
becoming promising accelerators in the development of drugs from bioactive compounds for various therapeutic areas such as cancer, viral, and neglected diseases.
There is a growing trend toward such libraries, i.e., repositories of information on
compounds with specific pharmacological activities. Currently, and in the near
future, these virtual libraries could also enrich the medically relevant chemical
space for inhibitors and potential drug candidates. In addition, the incorporation of
chemoinformatics tools, the automation of curation processes, and the allo cation of
financial and human resources for the maintenance of DBs will contribute significantly to the development of drug design discovery and development projects.
9 Conclusion
The process of developing new bioactive compounds into drugs is long and complex. Scientific and technological advances at the interface of chemistry and biology
have created remarkable opportunities and challenges for research and development.
Computational simulations have been playing an important role in reducing costs
and accelerating the drug design and discovery processes. These advantages,
together with the large number of chemi cal entities available in DBs, represent a
vast area of research for new potential drugs. The importance of DBs in new drug
discovery projects is continuously increasing and is a centerpiece in pharmaceutical
companies as well as in academic and government research centers. Notwithstanding, the quality of the compound DBs is crucial to properly fulfill their role as drug
design tools. Therefore, the curation and maintenance of high-quality data is essential. Compounds discovered in existing DBs have already led to the development of
drugs in clinical use to treat different diseases. This shows the feasibility of accessing
different DBs that can continue to contribute to the field of drug design, discovery,
and development.
2 Molecular Databases 35
Questions to answer when planning an experiment using databases
What computational approaches are being used to accelerate drug discovery
What is a compound database?
What is the relevance of a compound database in drug discovery?
What are the greatest practical challenges to developing compound repositories?
What are the major steps to construct a compound database?
What are the largest compound databases annotated with biological activity?
What is data curation?
Mention examples of software used for data curation.
What are the major deficiencies or shortcomings in databases, particularly in the public domain?
Provide examples of at least five natural product databases publicly available.
What are the main linear notations to store the information of chemical structures in compound
databases?
What are the main rules of thumb or empirical rules used in drug discovery projects?
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