Prediction of Log<i>P</i>with Property‐Based Methods

Tetko, Igor V.; Poda, Gennadiy

doi:10.1002/9783527621286.ch15

Cited by 16 publications

(16 citation statements)

References 78 publications

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“…The application of log P prediction software tools for rational drug design in pharmaceutical industry deserves a comprehensive validity check on large databases that cover a broad chemical space for drug‐like as well as lead‐like structures. A number of evaluations of calculated versus experimental log P values have appeared in the literature 154–161. Most of these studies suffer from small size, limited chemical diversity, and/or inclusion of only a few log P prediction software tools.…”

Section: Comparison Of Log P Prediction 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

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572

474

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Section: Comparison Of Log P Prediction 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

Self Cite

572

474

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“…To be meaningful, such a comparison requires that third parties run multiple models side‐by‐side on a common test set, preferably one composed of compounds not ‘seen’ during training by any of the models being evaluated. Several such independent analyses have been published for the more commonly predicted physicochemical properties,48–52 though not, unfortunately, for all properties that are relevant to modern herbicide discovery. Regardless of general conclusions about the relative performance of different programs, it is a good idea to analyze compounds from one's own corporate database to assess the reliability and range of applicability of any given model.…”

Section: Things That Have Changedmentioning

confidence: 99%

A perspective on the role of quantitative structure–activity and structure–property relationships in herbicide discovery

Clark

2012

Pest Management Science

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“…Only a selection of methods are highlighted here, since an excellent extensive review was recently published [34]. Only a selection of methods are highlighted here, since an excellent extensive review was recently published [34].…”

Section: Prediction Methods Based On 3-d Molecular Structurementioning

confidence: 99%

“…This section presents only an outline of the principal in silico methods since this subject was recently reviewed in detail [33,34]. These different methods can be divided in two main classes according to the level of description of molecular structure, namely 2D fragmental methods which cut the molecule in typical atomic or multiatomic fragments possessing their own lipophilicity coefficients and 3D global methods which code explicitly the principal intermolecular interactions potential of a 3D molecule.…”

Section: 21mentioning

confidence: 99%

“…Several attempts were performed to determine the accuracy of in silico prediction tools developed for lipophilicity (for a recent review, see [34]). The main factor limiting the accuracy of all predictive methods is the training sets used to generate the models, in terms of population and quality of the experimental data they contain.…”

Section: General Comments On the Prediction Of Log P Octmentioning

confidence: 99%

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

Martel

Gasparik

Carrupt

2009

Hit and Lead Profiling

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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 ...

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Prediction of LogPwith Property‐Based Methods

Cited by 16 publications

References 78 publications

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

A perspective on the role of quantitative structure–activity and structure–property relationships in herbicide discovery

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

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