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Besides Egan’s guidelines, the presented principles thus far identify molecular
weight as one of the crucial indicators of drug-likeness. Veber (GSK) provided evi-
dence that low molecular weight or tiny size are not prerequisites for oral bioavail-
ability. To assure target selectivity and efficacy, he also underlined the probability of
developing a large molecular weight molecule. He proposed that, regardless of their
molecular weight, polarity and molecular flexibility might substantially impact a
drug’s bioavailability. They came to the conclusion that a molecule’s bioavailability
would be much improved by lowering the number of rotatable bonds and PSA. They
ultimately developed the following set of guidelines to explain their definition of
drug-likeness: ≤10 rotatable bonds and PSA ≤ 140 Å
2
(or ≤12 HBD and HBA) [20].
In order to enhance the drug candidate’s affinity and selectivity, lipophilicity and
molecular weight are frequently enhanced throughout the drug development process.
Therefore, maintaining drug-likeness (i.e., RO5 compliance) throughout hit and lead
optimization is frequently challenging. Therefore, it has been suggested that screening
libraries used to find hits should be biased towards reduced molecular weight and lip-
ophilicity, making it simpler for medicinal chemists to produce optimized drug devel-
opment candidates that are also drug-like. As a result, the rule of three (RO3) has been
incorporated to the Ro5 to define compounds that resemble lead. A compound that
complies with the RO3 is one that has log P (octanol–water partition coefficient < 3),
molecular weight (<300 Da), hydrogen bond donors, hydrogen bond acceptors and ro-
tatable bonds (<3) [21].
Oprea and colleagues found that 70% of the compounds in both drug-like data-
bases (ACD) and nondrug-like databases (MDDR, current patents fast-alert, CMC, PDR,
and NCE) adhere to the following rules: 0 ≤ HBD ≤ 2, 2 ≤ HBA ≤ 9; 2 ≤ RTB (number of
rotatable bonds) ≤ 8, and 1 ≤ RNG (number of rings) ≤ 4 [22].
The REOS (rapid elimination of swill) program was created at Vertex with a simi-
lar goal in mind as the drug-likeness rules/filters. In contrast to Lipinski’ sRo5,the
drug-likeness criteria used by REOS comprises six rules: MW (200 ~ 500), log P (5 ~ 5),
number of hydrogen bond donors (0–5), number of hydrogen bond acceptors (0 ~ 10),
number of formal charge (2 ~ 2), and number of rotatable bonds (0 ~ 8). Additionally,
REOS gives customers access to more than 200 functional group filters that let them
exclude compounds that include reactive, toxic, and undesirable fragments [23].
Hopkins and colleagues published the quantitative estimate of drug-likeness (QED),
a concept of desirability, in 2012. The QED index was developed by fitting the distribu-
tions of eight characteristics from 771 commercially available oral medications, includ-
ing MW, A log P, HBA, HBD, polar surface area (PSA), RTB, number of aromatic rings
(AROM), and number of warnings for unfavorable substructures (warnings). Since the
inflexible cut-offs are replaced with a distinctive continuous index (0.5–1.0), the QED
approach is more adaptable than the majority of property-based drug-likeness rules/fil-
ters [11]. A summary of a few important drug-likeness rules has been displayed in
Table 3.1.
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3.3 Physicochemical properties employed
as descriptors by Lipinski
3.3.1 Molecular weight
Lipinski’s Ro5 requires that a molecule’s molecular mass be less than 500 Da for it to
be considered a small-molecule therapy. The molecular weight of medications is ris-
ing with time, according to retrospective research. The molecular weight may not
need to be constrained by these rules if other physicochemical features like lipophilic-
ity are under control, but an increase in the number of heavy atoms in the molecule
might have an influence on how well the ligand binds to it [24].
Table 3.1: Summary of a few important drug-likeness rules.
S. no. Drug-likeness rule Criteria
Lipinski []MW≤
C log P ≤
HBA ≤
HBD ≤
Ghose [] ≤ MW ≤
–. ≤ W log P ≤ .
≤ MR ≤
≤ atoms ≤
Egan [] W log P ≤ .
TPSA ≤ .
Muegge [, ] ≤ MW ≤
– ≤ X log P ≤
TPSA ≤
NR ≤
NC >
NHA >
RB ≤
HBA ≤
HBD ≤
Veber []RB≤
TPSA ≤
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3.3.2 Lipophilicity
A physicochemical feature called lipophilicity, which describes a molecule’scapacityto
partition into octanol or water, is frequently thought to be extremely important to the
rate of absorption. The logarithm of the drug’s partitioning into the organic phase to
that into the aqueous phase, or log P, is used to determine lipophilicity. There are many
ways to measure this feature physically, but there are also many ways to compute log
P, each of which has benefits and drawbacks. For instance, the C log P technique calcu-
lates a molecule’s lipophilicity by adding the log P values of the constituents that make
up the molecule [25].
These fragmented values, which also contain correction factors for electronic and
steric effects, were produced via least-squares fitting to a training set. For molecules
with typical drug-like functional groups and fragments that are closely connected to the
training set, the C log P approach performs well. The atomic contributions of each atom
in the molecule are used in an atomic-based prediction of log P (A log P, M log P), which
is also fitted to a training set with empirically confirmed partition coefficients. Lipinski
compared the use of the fragment-based C log P computation to that of the atomic-
based M log P parameter during the formulation of the Ro5. The rule-based Moriguchi
technique always gave an answer, even at a compromise of accuracy, whereas the
C log P method offered very accurate results in classes of compounds where all the frag-
ments of a particular chemical were specified inside the training set. As a result, the
more precise C log P is often utilized within a group of related compounds (produced
by a medicinal chemistry optimization program), while the broader M log P calculation
was used to analyze the lipophilicity of vast collections [26, 27].
The simple counting of lipophilic (all carbons and halogens with a multiplier fac-
tor to normalize their contributions) and hydrophilic (all nitrogen and oxygen atoms)
atoms forms the basis of the Moriguchi method’s determination of log P. Eleven cor-
rection variables are used by the M origuchi approach, including four that describe
hydrophobicity and seven that indicate lipophilicity [28]. The correction factors that
describe hydrophobicity are:
1. UB (no. of unsaturated bonds excluding those in nitro groups);
2. AMP (the correction factor for amphoteric compounds): an α amino acid structure
adds 1.0 to the AMP parameter, while each aminobenzoic acid and each pyridine
carboxylic acid adds 0.5;
3. RNG: has the value of 1.0 if the compound has any rings aside from benzene-
based with heteroaromatic, or hydrocarbon rings;
4. QN (no. of quaternary nitrogen atoms).
The seven correction factors that describe lipophilicity are:
1. PRX (a proximity correction factor for nitrogen and oxygen atoms that are topologi-
cally close to one another): (1) addition of 2.0 for each two atoms directly connected
and also for each two atoms connected via a carbon, sulfur, or phosphorus atom;
3 The utilization of descriptors in convoluted Lipinski’s rule of five 45
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(2) addition of 1.0 unless one of the two bonds connecting the two atoms is a double
bond; (3) addition of an extra 1.0 for each carboxamide group, and for each sulfon-
amide group 2.0.
2. HB: 1.0 if any structural characteristics exist that may result in an internal hydro-
gen bond.
3. POL (no. of carbon atoms associated to two or more heteroatoms that are likewise
joined to an aromatic ring by a single bond or the number of heteroatoms con-
nected to an aromatic ring by only single bond).
4. ALK: 1.0 if the molecule only has one double bond and the atoms of carbon and
hydrogen.
5. NO
2
(no. of nitro groups).
6. NCS: 1.0 for each isothiocyanate group and 0.5 for each thiocyanate group.
7. BLM: 1.0 if the molecule has a beta lactam ring.
3.3.3 Hydrogen bond donors
Many hydrogen bond donor groups in a molecule might lessen a molecule’scapacityto
penetrate a membrane bilayer in addition to high molecular weight and lipophilicity.
In contrast to the lipophilic environment seen in a biological membrane, compounds
with a lot of hydrogen bond donors will partition into a highly hydrogen-bonding sol-
vent (like water). By simply accounting for the N–H and O–H bonds in a molecule, the
same can assess the functional groups’ capacity for hydrogen bonding [29].
3.3.4 Hydrogen bond acceptors
Hydrogen bond acceptors influence permeability by reacting favorably with a highly
hydrogen-bonding solvent, such as water. Once more, despite the fact that hydrogen-
bonding characteristics can be calculated, Lipinski and colleagues found that merely
adding the molecules’ nitrogen and oxygen atom counts acts as a decent proxy for
correlating to oral bioavailability [30].
Various descriptors play a major role in different drug-likeness filters and are
shown in Table 3.2.
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Table 3.2: Descriptors involved in drug-likeness rules.
Descriptor Range Description
Molecular weight < (Lipinski) The total atomic weights of the atoms in a molecule are
measured by its molecular weight. Lipinski’s rule of five
requires that a small-molecule therapy has a molecular mass
of less than Da.The molecular weight range for a small
molecule established by the Ghose drug-likeness filter is
– Da [, ].
C log P (calculated
octanol–water partition
coefficient)
– One of the most crucial molecule characteristics is log P,which
has a big impact on a lot of ADMET-related variables and
overall compound “quality” [].
C log D
.
(calculated
octanol–water
distribution coefficient at
pH.)
– Considering ionizable groups in compounds, log D values
between and are more likely to avoid problems brought
on by either high or low lipophilicity (poor absorption, renal
clearance, toxicity, high metabolic clearance, hERG inhibition,
and promiscuity) [].
HBA (number of H-bond
acceptor atoms)
≤ The total no. of –NH and –OH bonds (from Lipinski’s rules)
[]. For leads (RO) ≤ [].
HBD (number of H-bond
donor atoms)
≤ The total no. of =N– or –O– atoms (from Lipinski’s rules) [].
For leads (RO) ≤ [].
TPSA (topological polar
surface area)
≤ Å
An approximate two-dimensional representation of the
surface sum of all polar molecules’ atoms, chiefly oxygen and
nitrogen, together with any hydrogen atoms that are bound
to those atoms [].
nRotB (number of
rotatable bonds)
≤ Any single non-ring bond joined to a non-hydrogen atom
that is not terminal. Since amide C–N bonds have a high
barrier to rotation, they are not counted [].
nAr (number of aromatic
rings)
< remove or
replace
carboaromatic
rings
Later clinical stages and commercialized drugs often have
lower mean aromatic ring counts than compounds currently
in development. It is not just a result of changes in size or
lipophilicity that an increase in the number of aromatic rings
decreases solubility while enhancing protein binding, CYP
inhibition, and hERG inhibition. ADMET developability is
markedly improved by lowering the proportion of
carboaromatic rings and substituting heteroaromatic or
aliphatic rings in their place [].
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3.4 Applicability of Lipinski’s rule
The Ro5 is broken by 8.2% of oral medicines released since 1950, breaking two or more
rules. Some examples of drugs that violate the Ro5 are shown in Figure 3.2. Although
the area is not fully understood, potential “compensating” characteristics include natu-
ral product (NP) origin, complexity, such as high chirality and sp
3
carbon fraction (e.g.,
in antiviral agents), low aromatic ring count, macrocyclic structures, intramolecular hy-
drogen bonding, and transporter-mediated permeation. If the goal has a high therapeu-
tic value, no other leads exist, and the portfolio is not substantially dominated by such
initiatives, pursuing “exception space” becomes more justifiable. However, property-
based inhibitor optimization has been shown to be effective against even extremely dif-
ficult “lipophilic” targets, such as the cholesteryl ester transfer protein [37].
Total 64 orally active NCEs were introduced between 1994 an d 1997, and 14 of
them (21.9%) had one or more descriptions that were not in the Ro5. Comparatively,
154 NCEs that were administered orally between 2013 and 2019 were found, and 60 of
them had at least one physicochemical description outside the Ro5 (39.9%) [38].
In some circumstances, these criteria can be improved to take on challenging ob-
jectives like protein– protein interaction (PPI). Only 30% of PPI inhibitors fulfil the
standard Ro5, according to the study. The number of PPI inhibitors quadrupled when
these restrictions were reduced and compounds were permitted to violate only one
criterion, as 64% of the compounds had a relaxed drug-like behavior. At other times,
other natural transporters may use drug molecules as substrates. As a result, these
substances frequently violate the standard guidelines for drugs (e.g., antibiotics, anti-
fungals, vitamins, and cardiac glycosides). On the other hand, a variety of harmful
substances cling to the Ro5. As a result, the database preparation should further use
additional filtration methods that address various components of the drug-likeness
criterion [39].
Table 3.2 (continued)
Descriptor Range Description
Fsp (fraction of carbon
atoms that are sp
-
hybridized)
As a stand-in for three-dimensionality, Fsp, or the proportion
of carbon atoms with sp
hybridization (also called the
aliphatic indicator), is a D descriptor. It is represented as a
number between and .HigherFsp levels appear to be
associated with substances that are more soluble, have lower
melting temperatures, are less promiscuous, bind to fewer
proteins, and inhibit CYP less. The relationship between
nAr and Fsp is adverse (r = −.)[].
48 Mohit Motiwale et al.
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3.5 Beyond the rule of five
The findings of a research support the “beyond the Ro5” notion by demonstrating that
thechoiceofdrug-likecompoundsisnowdetermined by a balance between their
physicochemical features rather than by a set of predefined criteria. Since the param-
eters now suggested do not apply to all molecules, traditional prodrugs are created to
increase medication absorption without significantly altering the drug’s structural
makeup. Considering Lipinski’s rules, the molecular modifications in the prodrug de-
sign can act in a two-way manner by allowing a drug to be adjusted to some parame-
ters, such as Crippen log P and HBD number, while also allowing extrapolation of
other parameters, such as MW and HBA number. As a result, certain prodrugs, which
violate the rules demonstrate that the dynamic balance between the physicochemical
qualities is the key to effective oral absorption, superior absorption than drugs that
are inside the drug-like chemical space. Additionally, there are several examples of
orally active drugs that do not adhere to the drug-likeness rules, indicating that early
stages of drug design screening of compounds inside the drug-like chemical space
may result in the exclusion of molecules of interest. To include orally active medi-
cines and prodrugs, the chemical space might be extended to Crippen log P ≤ 5.88,
MW ≤ 600 Da, HBA ≤ 12, HBD ≤ 5, TPSA < 204 Å
2
, and the number of atoms < 76 [40].
Figure 3.2: Some examples of drugs that violate Lipinski’s rule. Violated descriptors are shown
in red color.
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In general, organic compounds with a ring of at least 12 heavy atoms are referred
to as macrocycles. Before 1980, there was a growing general interest in macrocycles
across various scientific disciplines; this interest then sharply increased beginning
1990. However, 30–40% of drugs and clinical candidates are orally bioavailable and
typically stay in the “beyond the Ro5” chemical space where macrocycles typically re-
side. HBD ≤ 7 in conjunction with either MW < 1,000 Da or C log P > 2.5 are simple bi-
descriptor models [41].
Karami et al. carried out a thorough parameter distribution study on the molecu-
lar characteristics of ophthalmic drugs. The foundation for physicochemical parame-
ter limitations was laid by investigating molecular thermodynamic properties of
approved ophthalmic drugs influencing corneal permeability and topical ophthalmic
medication delivery. These par ameters included free energy of partitioning (ΔG
o/w
)
calculated based on thermodynamic free energy equation, distribution coef ficient at
physiological pH (C log D, pH 7.4), topological PSA (TPSA), and aqueous solubility (S
int
,
S
pH
7.4) with boundaries of C log D,pH7.4≤ 4.0, TPSA ≤ 250 Å
2
, ΔG
o/w
≤ 20 kJ/mol
(4.8 kcal/mol), and solubility (S
int
and S
pH
7.4) ≥ 1 μM, respectively. The computation of
variations in the free energy of partitioning, (G
o/w
), as a gauge of incremental gains in
corneal permeability for congeneric series, was made simpler by the theoretical free
energy of partitioning model. For topical ophthalmic medicines, the parameter limita-
tions are suggested as “rules of thumb” to evaluate risks in developability [42].
In 1999, Lipinski established a set of guidelines (RoCNS) that can be used to find
medications with strong CNS penetration and gastrointestinal absorption. RoCNS is
generated from a group of 1,500 approved drugs that are both orally accessible and
brain-penetrable. According to the study, physicochemical parameters often have a
lower tolerated range for CNS penetration than those allowed for non-CNS purposes
[43]. The values for RoCNS’ physicochemical characteristics are as follows:
Molecular weight ≤ 400
No. of H-bond acceptors ≤ 7
No. of H-bond donors ≤ 3
C log P ≤ 5
3.6 Various useful databases in drug-likeness
To evaluate molecular PK and toxicity, molecular ADMET-associated properties have
been employed in addition to conventional physicoche mical property-based drug-
likeness criteria and scores. Examples include drug-induced liver injury (DILI), human
intestinal absorption (HIA), blood-brain barrier (BBB) permeability, inhibition of the cy-
tochrome P450 enzyme (CYP), serving as a P-gp substrate or inhibitor, cardiotoxicity,
and cytotoxicity. Such information can be found in databases that enable drug-likeness
research: chemical structures, physicochemical characteristics, ADMET-related data,
50 Mohit Motiwale et al.
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drug-likeness attributes (rules and scores), and drug and drug-like sets [44]. The infor-
mation about such databases has been included in Table 3.3.
Table 3.3: List of databases including the information associated with ADMET, physicochemical
characteristics and drug-likeness attributes of small molecules.
Database Features References
DrugBank – Includes 2,500 authorized small-molecule
medications and over 6,000 investigational
compounds.
– Offers both qualitative and quantitative drug-
likeness prediction findings for each molecule,
including the bioavailability score, Lipinski’ s Ro5,
the Ghose filter, Veber’s rule, and the MDDR-like
rule.
[, ]
ChEMBL – Includes 2,500 approved drugs and over 1 million
drug-like compounds.
– Offers molecular QED results.
– Serves as both training sets for drug-likeness
modeling and testing sets for evaluating the
model’s propensity to recognize compounds that
are similar to drugs.
[]
Pharmacokinetics
knowledge base (PKKB)
– Includes 10,000 experimental ADMET assessments
for 1,685 different drugs.
– Users may search and explore ADMET data.
– Includes information for nearly 760,000 chemical
substances associated with:
– physicochemical properties;
– in vivo toxicities;
– ADME properties, such as fraction unbound
in human plasma, volume of distribution;
– PK half-life, can be found on the US
Environmental Protection Agency’s web-
based CompTox chemistry dashboard.
[, ]
BindingDB – Assay findings can be gathered to create
quantitative structure–activity relationships
(QSAR) and other theoretical models for drug-
likeness prediction by employing keywords (e.g.,
“HepG2” are cells connected with cytotoxicity;
“CYP450” is an enzyme related to metabolism).
[]
ZINC – Includes a library of 727,842 molecules.
– Maximally compiles molecules with drug-like
properties.
[, ]
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In summary, drug-likeness research has advanced significantly with the enormous in-
crease of chemical compound databases. DrugBank, ZINC, and ChEMBL all offer drug
and drug-like sets. Each compound’s drug-likeness characteristics are provided by
DrugBank and ChEMBL. AdmetSAR, DrugBank, and PKKB have more ADMET data
than other databases do. The in vitro data from BindingDB can supplement ChEMBL
because it primarily focuses on the interactions of compounds with potential target
proteins. These databases are useful for providing top-notch and current materials,
especially for AI modeling. Although many ADMET endpoints are still lacking, it is still
vital to increase the amount of ADMET data that is accessible to the general public.
How to effectively extract this substantial quantity of pertinent data from the litera-
ture and databases and transform it into useable inform ation for developing drug-
likeness prediction models presents a significant problem.
3.7 Online tools for screening new chemical entities
for “rule of five”
Chemicals’ ADMET characteristics must be evaluated experimentally, which is expen-
sive and time-consuming. The development of online technology as a substitute strat-
egy is encouraging. Several techniques have been implemented into web servers to
reliably and precisely estimate a molecule’s drug-likeness. Artificial intelligence (AI)
methods, for instance, can routinely forecast ADMET endpoints. Molecular drug-
likeness may be thoroughly assessed using molecular property-based drug-likeness
principles and scores. In addition to making it simple to exclude compounds with un-
desirable PK profiles, drug-likeness assessment services also assist scientists in coordi-
nating and optimizing the interdependent descriptors [52–55].
3.7.1 SCFBio, Lipinski filter
SCFBio, a center for supercomputing facility in the field of bioinformatics and compu-
tational biology at the Indian Institute of Delhi has launched the L ipinski filters for
distinguishing drug-like and nondrug-like molecules. It predicts a high probability of
success or failure due to drug-likeness for molecules complying with two or more of
the Lipinski’s Ro5 (http://www.scfbio-iit d.res.in/software/drugdesign/lipinski.jsp#an
cho rtag) [56].
52 Mohit Motiwale et al.
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