Estimation of partition coefficients of organic compounds: local database modeling with uniform-length structure descriptors

Junghans, Martin; Pretsch, Ernö

doi:10.1007/s002160050541

Cited by 15 publications

(11 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This strategy promises enhanced prediction accuracy. Several other approaches such as TLOGP,46 KowWIN,47 SLIPPER,48, 49 ALOGPS50 and XLOGP351 apply a similar methodology to estimate the value of query compounds using the lipophilicity of their nearest neighbors.…”

Section: Substructure‐based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds

Mannhold

Poda

Ostermann³

et al. 2009

Journal of Pharmaceutical Sciences

581

474

View full text Add to dashboard Cite

Section: Substructure‐based Methodsmentioning

confidence: 99%

“…Junghans and Pretsch46 used topological descriptors of uniform length136 representing n ‐dimensional vectors. The vectors showed the occurrence of atom pairs in relative distances of 1 to n bonds.…”

Section: Property‐based Methodsmentioning

confidence: 99%

Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds

Mannhold

Poda

Ostermann³

et al. 2009

Journal of Pharmaceutical Sciences

581

474

View full text Add to dashboard Cite

“…For example, MLOGP method [52] which considered atomic contributions to differentiate hydrophobic and hydrophilic atoms and thirteen descriptors such as intramolecular hydrogen bonds abilities and proximity effects, TLOGP which used uniform-length molecular descriptors generated from 3D structures [53]. For example, MLOGP method [52] which considered atomic contributions to differentiate hydrophobic and hydrophilic atoms and thirteen descriptors such as intramolecular hydrogen bonds abilities and proximity effects, TLOGP which used uniform-length molecular descriptors generated from 3D structures [53].…”

Section: Prediction Methods Based On 3-d Molecular Structurementioning

confidence: 99%

In Silico Tools and In Vitro HTS Approaches to Determine Lipophilicity During the Drug Discovery Process

Martel

Gasparik

Carrupt

2009

Hit and Lead Profiling

View full text Add to dashboard Cite

Advances in rational drug design and combinatorial chemistry increased drastically the number of newly promising active compounds. As inappropriate pharmacokinetics (PK) was recognized as one of the major factors leading to the withdrawal of new chemical entities (NCEs) from drug development [1,2], efficient in silico and in vitro models were developed to replace in vivo studies for absorption, distribution, metabolism, excretion (ADME) and toxicity (T) in the early phases of drug research [3]. The large number of approaches offering suitable estimates of ADMET properties in the early stages of drug discovery has been extensively reviewed [4][5][6][7][8][9][10][11]. These approaches used largely (Q)SAR models based on physicochemical parameters such as solubility, ionization and lipophilicity. Thus these fundamental physicochemical properties have to be estimated early in drug discovery programs to filter out unsuitable compounds and to rank validated hits in order to select the most promising compounds according to their pharmacodynamic and pharmacokinetic profiles. As the paradigms of hit-like [12,13], lead-like [13][14][15][16] or drug-like properties [17][18][19][20][21][22][23][24] are governing the research, considerable efforts are still carried out to develop efficient virtual and experimental filters to reduce large chemical databases to smaller collections of compounds with suitable PK properties for an in vivo high therapeutic potency [25].Among the physicochemical properties involved in ADMET profiling, lipophilicity has a place of choice as the major contributor to solubility, membrane permeation and protein binding [26,27], metabolism and the ability to reach and to bind the targeted receptors [28]. This predominant position is well illustrated by numerous papers devoted to this topic (for recent reviews, see [6,11,29,30]). To overcome the limitations of the time-consuming traditional methods such as the shake-flask, potentiometry (for ionizable compounds) and cyclic voltammetry (for ions only), a number of in silico and experimental high-throughput techniques Hit and Lead Profiling. Edited by Bernard Faller and Laszlo Urban j91 emerged. The scope of this chapter is to review these HTS methods for log P and log D determination in the 1-octanol/water system and in other solvent/water systems useful in the early stages of drug discovery. 5.2Virtual Filtering: In Silico Prediction of log P and log D Among the large number of existing lipophilicity parameters [31], the descriptor frequently estimated by in silico methods is the partition coefficient of a solute between 1-octanol and water, expressed as log P oct [32]. However, lipophilicity determination in different solvent systems, such as alkane/water system, proved its utility in (Q)SAR studies and therefore some predictive methods also emerged in this field. Many publically available databases include numerous experimental values collected through the literature; the quality of the experimental data represents the cornerstone of most of the models ...

show abstract

“…However, finding the optimal three-dimensional structure of the molecule is a time consuming task, which limits the ability of these methods to handle large numbers of molecules in a reasonable amount of time. Most molecular-property based methods do not use all available molecular descriptors, but limit themselves to a small subset of descriptors, which have already been proven to be effective like E-state indices [24, 25] by ALOGPS [19, 20] and VLOGP [26], topological descriptors by TLOGP [27] and molecular size and H-bonding descriptors by QLOGP [17]. As a result, these models provide little indication in the identification of specific molecular properties that affect the partition coefficient.…”

Section: Introductionmentioning

confidence: 99%

A non-linear structure–property model for octanol–water partition coefficient

Yerramsetty

Neely

Gasem

2012

Fluid Phase Equilibria

View full text Add to dashboard Cite

Octanol-water partition coefficient (Kow) is an important thermodynamic property used to characterize the partitioning of solutes between an aqueous and organic phase and has importance in such areas as pharmacology, pharmacokinetics, pharmacodynamics, chemical production and environmental toxicology. We present a non-linear quantitative structure-property relationship model for determining Kow values of new molecules in silico. A total of 823 descriptors were generated for 11,308 molecules whose Kow values are reported in the PhysProp dataset by Syracuse Research. Optimum network architecture and its associated inputs were identified using a wrapper-based feature selection algorithm that combines differential evolution and artificial neural networks. A network architecture of 50-33-35-1 resulted in the least root-mean squared error (RMSE) in the training set. Further, to improve on single-network predictions, a neural network ensemble was developed by combining five networks that have the same architecture and inputs but differ in layer weights. The ensemble predicted the Kow values with RMSE of 0.28 and 0.38 for the training set and internal validation set, respectively. The ensemble performed reasonably well on an external dataset when compared with other popular Kow models in the literature.

show abstract

Estimation of partition coefficients of organic compounds: local database modeling with uniform-length structure descriptors

Cited by 15 publications

References 1 publication

Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds

Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds

In Silico Tools and In Vitro HTS Approaches to Determine Lipophilicity During the Drug Discovery Process

A non-linear structure–property model for octanol–water partition coefficient

Contact Info

Product

Resources

About