logP

The logP value, representing a compound's partition coefficient between octanol and water, is a critical parameter in computer-aided drug design (CADD) and various molecular applications due to its direct correlation with a compound`s lipophilicity.

Note

Lipophilicity is a key factor influencing a molecule`s absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, which are essential in drug discovery.

Interpretation

Here`s how the significance of the logP value can be interpreted in these contexts.

Drug Absorption and permeability

In the context of drug absorption and permeability, the lipophilicity of a compound, as indicated by its logP value, plays a pivotal role. A compound must strike a balance in lipophilicity to effectively permeate cell membranes and achieve optimal oral bioavailability; typically, a logP value between 2 and 5 is associated with favorable absorption characteristics.

This balance is crucial for general drug absorption and for penetration of the blood-brain barrier (BBB). Specifically, drugs designed to act on the central nervous system (CNS) require a higher degree of lipophilicity to cross the BBB and reach their target site within the brain. Thus, a suitable logP value is indispensable in developing CNS drugs, ensuring they possess the necessary characteristics to cross the BBB effectively and exhibit the desired therapeutic action once they reach the central nervous system.

Solubility and Formulation

The relationship between a compound's logP value and its aqueous solubility is inversely proportional, with high logP values often signaling poor water solubility. This presents significant challenges in the formulation and delivery of drugs, as a balance must be struck between lipophilicity for effective cellular absorption and aqueous solubility for systemic availability. Understanding and optimizing the logP value is thus critical in developing drug formulations that achieve this balance.

Moreover, the logP value can inform the choice of formulation strategies. Specifically, it can guide the selection of appropriate excipients and delivery systems that enhance the solubility of lipophilic drugs. This, in turn, improves the bioavailability of these drugs, ensuring that they can be effectively absorbed into the bloodstream and reach their intended targets within the body.

Toxicity and Side Effects

In drug development, accurately predicting and managing the toxicity and side effects of potential pharmaceutical compounds is of paramount importance. Compounds characterized by very high logP values pose a particular concern, as they may preferentially accumulate in lipid-rich tissues, potentially leading to adverse toxicity levels. This underscores the importance of closely monitoring and optimizing the logP values throughout the drug design process to mitigate such risks effectively.

Furthermore, a nuanced understanding of how a compound's logP value influences its interactions with biological targets enables scientists to modify the drug's chemical structure judiciously. Such strategic modifications aim to minimize unwanted interactions that could result in side effects, thereby enhancing the drug`s therapeutic index. This dual focus on minimizing toxicity while maximizing therapeutic efficacy exemplifies the critical role of logP in developing safer, more effective drugs.

Method

MolModa computes the octanol−water partition coefficient (logP) for molecules using the method described by Wildman and Crippen ¹.

Atom classification

The approach relies on classifying atoms into one of 68 predefined atom types, each associated with a specific contribution to the molecule's logP value. Atoms are classified based on their immediate chemical environment, considering the neighboring atoms' type, aromaticity, and bonding. This classification reduces ambiguity and ensures a consistent assignment of atom types across different molecules.

Atom Type Descriptions and Contributions

type	descriptions	SMARTS	logP \(\alpha_i\)
C1	1°, 2° aliphatic	`[CH4]`, `[CH3]C`, `[CH2](C)C`	0.1441
C2	3°, 4° aliphatic	`[CH](C)(C)C`, `[C](C)(C)(C)C`	0.0000
C3	1°, 2°	`[CH3][(N,O,P,S,F,Cl,Br,I)]`, `[CH3][A#N]`, `[CH2X4][A#N]`, `[CH3][#15]`, `[CH2X4][#15]`, `[CH3][#16]`, `[CH2X4][#16]`, `[CH3][#53]`, `[CH2X4][#53]`, `!([CH2X4]a)`	-0.2035
	heteroatom	`[CH2X4][(N,O,P,S,F,Cl,Br,I)]`
C4	3°, 4°	`[CH1X4][(N,O,P,S,F,Cl,Br,I)]`, `[CHX4][A#N]`, `[CH0X4][A#N]`, `[CHX4][#15]`, `[CH0X4][#15]`, `[CHX4][#16]`, `[CH0X4][#16]`, `[CHX4][#53]`, `!([CH0X4][#53])`, `!([CHX4]a)` or `!([CH0X4]a)`	-0.2051
	heteroatom	`[CH0X4][(N,O,P,S,F,Cl,Br,I)]`
C5	C = heteroatom	`[C] = [A#X]`	-0.2783
C6	C = C aliphatic	`[CH2] = C`, `[CH1](=C)A`, `[CH0](=C)(A)A`,`[C](=C)=C`	0.1551
C7	acetylene, nitrile	`[CX2]#A`	0.00170
C8	1° aromatic carbon	`[CH3]c`	0.08452
C9	1° aromatic heteroatom	`[CH3][a#X]`	-0.1444
C10	2° aromatic	`[CH2X4]a`	-0.0516
C11	3° aromatic	`[CHX4]a`	0.1193
C12	4° aromatic	`[CH0X4]a`	-0.0967
C13	aromatic heteroatom	`[cH0]−[!(C,N,O,S,F,Cl,Br,I)]`, `[c][#5]`, `[c][#14]`, `[c][#15]`, `[c][#33]`, `[c][#34]`, `[c][#50]`, `[c][#80]`	-0.5443
C14	aromatic halide	`[c][#9]`	0.0000
C15	aromatic halide	`[c][#17]`	0.2450
C16	aromatic halide	`[c][#35]`	0.1980
C17	aromatic halide	`[c][#53]`	0.0000
C18	aromatic	`[cH]`	0.1581
C19	aromatic bridgehead	`[c](:a)(:a):a`	0.2955
C20	4° aromatic	`[c](:a)(:a)-a`	0.2713
C21	4° aromatic	`[c](:a)(:a)-C`	0.1360
C22	4° aromatic	`[c](:a)(:a)-N`	0.4619
C23	4° aromatic	`[c](:a)(:a)-O`	0.5437
C24	4° aromatic	`[c](:a)(:a)-S`	0.1893
C25	4° aromatic	`[c](:a)(:a) = C`, `[c](:a)(:a) = N`, `[c](:a)(:a) = O`	-0.8186
C26	C = C aromatic	`[C](=C)(a)A`, `[C](=C)(c)a`, `[CH](= C)a`, `[C] = c`	0.2640
C27	aliphatic heteroatom	`[CX4][!(C,N,O,P,S,F,Cl,Br,I)]`, `[CX4][#X]`, `!([CX4][#N])`, `[CX4][#16]`, `[CX4][#15]`, `[CX4][#53]`	0.2148
CS	carbon supplemental	`[#6]` not matching any basic C type	0.08129
H1	hydrocarbon	`[#1][#6]`, `[#1][#1]`	0.1230
H2	alcohol	`[#1]O[CX4]`, `[#1]Oc`, `[#1]O[!(C,N,O,S)]`, `[#1][!(C,N,O)]`, `[#1]O[CX4]`, `[#1]Oc`, `[#1]O[#1]`, `[#1]O[#5]`, `[#1]O[#14]`, `[#1]O[#15]`, `[#1]O[#33]`, `[#1]O[#50]`, `[#1][#5]`, `[#1][#14]`, `[#1][#15]`, `[#1][#16]`, `[#1][#50]`	-0.2677
H3	amine	`[#1][#7]`, `[#1]O[#7]`	0.2142
H4	acid	`[#1]OC = [#6]`, `[#1]OC = [#7]`, `[#1]OC = O`, `[#1]OC = S`, `[#1]OO`, `[#1]OS`	0.2980
HS	hydrogen supplemental	`[#1]` not matching any basic H type	0.1125
N1	1° amine	`[NH2+0]A`	-1.0190
N2	2° amine	`[NH+0](A)A`	-0.7096
N3	1° aromatic amine	`[NH2+0]a`	-1.0270
N4	2° aromatic amine	`[NH+0](A)a`, `[NH+0](a)a`	-0.5188
N5	imine	`[NH+0] = A`, `[NH+0] = a`	0.08387
N6	substituted imine	`[N+0](=A)A`, `[N+0](=A)a`, `[N+0](=a)A`, `[N+0](=a)a`	0.1836
N7	3° amine	`[N+0](A)(A)A`	-0.3187
N8	3° aromatic amine	`[N+0](a)(A)A`, `[N+0](a)(a)A`, `[N+0](a)(a)a`	-0.4458
N9	nitrile	`[N+0]#A`	0.01508
N10	protonated amine	`[NH3+]`, `[NH2+]`, `[NH+*]`	-1.950
N11	unprotonated aromatic	`[n+0]`	-0.3239
N12	protonated aromatic	`[n+*]`	-1.119
N13	4° amine	`[NH0+](A)(A)(A)A`, `[NH0+](=A)(A)A`, `[NH0+](=A)(A)a`, `[NH0+](=[#6])=[#7]`	-0.3396
N14	other ionized nitrogen	`[N+]#A`, `[N−]`, `[N+](=[N−])=N`	0.2887
NS	nitrogen supplemental	`[#7]` not matching any basic N type	-0.4806
O1	aromatic	`[o]`	0.1552
O2	alcohol	`[OH]`, `[OH2]`	-0.2893
O3	aliphatic ether	`[O](C)C`, `[O](C)[A#X]`, `[O]([A#X])[A#X]`	-0.0684
O4	aromatic ether	`[O](A)a`,`[O](a)a`	-0.4195
O5	oxide	`[O]=[#8]`, `[O]=[#7]`, `[OX1−*][#7]`	0.0335
O6	oxide	`[OX1−*][#16]`	-0.3339
O7	oxide	`[OX1−][!(N,S)]`, `[OX1−][#15]`, `[OX1−][#33]`, `[OX1−][#43]`, `[OX1−*][#53]`	-1.189
O8	aromatic carbonyl	`[O]=c`	0.1788
O9	carbonyl aliphatic	`[O]=[CH]C`, `[O]=C(C)C`, `[O]=C(C)[A#X]`,`[O]=[CH]N`, `[O]=[CH]O`,`[O]=[CH2]`, `[O]=[CX2]=O`	-0.1526
O10	carbonyl aromatic	`[O]=[CH]c`, `[O]=C(C)c`, `[O]=C(c)c`, `[O]=C(c)[a#X]`, `[O]=C(c)[A#X]`, `[O]=C(C)[a#X]`	0.1129
O11	carbonyl heteroatom	`[O]=C([A#X])[A#X]`, `[O]=C([A#X])[a#X]`, `[O]=C([a#X])[a#X]`	0.4833
O12	acid	`[O−1]C(=O)`	-1.326
OS	oxygen supplemental	`[#8]` not matching any basic O type	-0.1188
F	fluorine	`[#9−0]`	0.4202
Cl	chlorine	`[#17−0]`	0.6895
Br	bromine	`[#35−0]`	0.8456
I	iodine	`[#53−0]`	0.8857
Hal	ionic halogens	`[#9−]`, `[#17−]`, `[#35−]`, `[#53−]`, `[#53+*]`	-2.996
P	phosphorous	`[#15]`	0.8612
S1	aliphatic	`[S−0]`	0.6482
S2	ionic sulfur	`[S−]`, `[S+]`	-0.0024
S3	aromatic	`[s]`	0.6237
Me1	`B`, `Si`, `Ga`, `Ge`, `As`, `Se`, `Sn`, `Te`, `Pb`, `Ne`, `Ar`, `Kr`, `Xe`, `Rn`		-0.3808
Me2	`Fe`, `Cu`, `Zn`, `Tc`, `Cd`, `Pt`, A`u,`Hg`		-0.0025

These SMARTS strings are based on v1988.10 Molecular Operating Environment (MOE).

Reproduced from Wildman and Crippen ¹.

Calculation

The logP of a molecule is calculated as the sum of the contributions of its constituent atoms, allowing for the estimation of its lipophilicity. The logP value of a molecule is calculated using

\[ \log \, P = \sum_{i} n_{i} \alpha_{i} \]

where

\(n_{i}\) is the number of atoms of type \(i\) present in the molecule, and
\(\alpha_{i}\) is the contribution to logP for atom type \(i\).

Warning

Please be aware that the logP value of a molecule is dependent on its protonation state. When you predict the logP value for a molecule and subsequently change its protonation state, the logP value will also change accordingly. This is evident in the Atom Classification table above, where there are specific contributions for hydrogens.

It is essential to consider the protonation state of your molecule when interpreting and comparing logP values. The same molecule can have different logP values depending on its protonation state under specific pH conditions. Remember this when using predicted logP values to make decisions or comparisons, especially if you have modified your molecule's protonation state.

Wildman, S. A., & Crippen, G. M. (1999). Prediction of physicochemical parameters by atomic contributions. Journal of chemical information and computer sciences, 39(5), 868-873. DOI: 10.1021/ci990307l ↩↩