Formation of Activity Cliffs Is Accompanied by Systematic Increases in Ligand Efficiency from Lowly to Highly Potent Compounds

Léon, Antonio de la Vega de; Bajorath, Jürgen

doi:10.1208/s12248-014-9567-x

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Third, SARI score was calculated for NNs as the raw structure–activity relationship (SAR) discontinuity score component of the SAR Index to quantify potency variations among NNs. For SARI score calculations, the Tanimoto similarity threshold for NNs was set to 0.6 and 0.4 for MACCS and ECFP4, respectively …”

Section: Methodsmentioning

confidence: 99%

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling

Miyao

Funatsu

Bajorath

2018

J. Chem. Inf. Model.

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Support vector regression (SVR) is a premier approach for the prediction of compound potency. Given the conceptual link between support vector machine (SVM) and SVR modeling, SVR is capable of accounting for continuous and discontinuous structure−activity relationships (SARs) in potency prediction, which further extends the classical quantitative SAR (QSAR) paradigm. In the context of virtual compound screening, compound potency prediction can be applied to identify the most potent compounds that are available or enrich database selection sets with potent compounds. To these ends, we have evaluated new potency prediction strategies. Conventional (direct) potency prediction using SVR was compared to two-stage SVM-SVR modeling and potency prediction using SVR models trained in the presence of active and inactive compounds, a previously unconsidered approach. The latter models were found to maximize the recall of potent compounds but were least accurate in predicting high potency values. For this purpose, direct SVR predictions were preferred. However, the best balance between accurate potency predictions and enrichment of potent compounds in database selection sets was achieved by combined SVM-SVR modeling. Taken together, our findings further extend current approaches for compound potency prediction in virtual compound screening.

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Section: Methodsmentioning

confidence: 99%

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling

Miyao

Funatsu

Bajorath

2018

J. Chem. Inf. Model.

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“…To further qualify 3D-cliff information, LE and LLE values were also calculated for compounds forming individual cliffs. If activity cliffs are a consequence of specific interactions and contributions to binding, rather than only increasing size or hydrophobicity of ligands, cliff formation should be accompanied by increases in LE and LLE, as has been shown previously (16). Of course, there are also specific lipophilic interactions that determine activity cliffs, which must be taken into consideration when judging about LLE values.…”

Section: Principles Of Hot Spot Analysismentioning

confidence: 73%

Identification of Interaction Hot Spots in Structures of Drug Targets on the Basis of Three‐Dimensional Activity Cliff Information

Furtmann

Gütschow

et al. 2015

Chem Biol Drug Des

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Activity cliffs are defined as pairs or groups of structurally similar or analogous compounds that share the same specific activity but have large differences in potency. Although activity cliffs are mostly studied in medicinal chemistry at the level of molecular graphs, they can also be assessed by comparing compound binding modes. If such three-dimensional activity cliffs (3D-cliffs) are studied on the basis of X-ray complex structures, experimental ligand-target interaction details can be taken into account. Rapid growth in the number of 3D-cliffs that can be derived from X-ray complex structures has made it possible to identify targets for which a substantial body of 3D-cliff information is available. Activity cliffs are typically studied to identify structure-activity relationship determinants and aid in compound optimization. However, 3D-cliff information can also be used to search for interaction hot spots and key residues, as reported herein. For six of seven drug targets for which more than 20 3D-cliffs were available, series of 3D-cliffs were identified that were consistently involved in interactions with different hot spots. These 3D-cliffs often encoded chemical modifications resulting in interactions that were characteristic of highly potent compounds but absent in weakly potent ones, thus providing information for structure-based design.

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“…However, whether or not systematic trends might be detectable was an open question. The analysis revealed that the formation of 99.1% of all activity cliffs across different targets was accompanied by consistent increases in LE values between lowly and highly potent cliff partners, with, on average, ∆LE = 6.25 14 . For activity cliffs defined on the basis of calculated (molecular fingerprint-based) similarity values, comparable observations were made 14 .…”

Section: Ligand Efficiency and Lipophilic Efficiencymentioning

confidence: 96%

“…In a recent analysis 14 , 18,208 activity cliffs were extracted from more than 41,000 unique ChEMBL 15 (release 15) compounds with activity against the current spectrum of human targets. Then, differences in LE between lowly and highly potent cliff partners were systematically assessed.…”

Section: Ligand Efficiency and Lipophilic Efficiencymentioning

confidence: 99%

“…Because LE and LipE are important and widely applied measures for compound optimization in drug discovery, it makes sense to also consider them in the context of activity cliff formation, given their immediate relevance for SAR analysis. In a recent analysis 14 , 18,208 activity cliffs were extracted from more than 41,000 unique ChEMBL 15 (release 15) compounds with activity against the current spectrum of human targets. Then, differences in LE between lowly and highly potent cliff partners were systematically assessed.…”

Section: Ligand Efficiency and Lipophilic Efficiencymentioning

confidence: 99%

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Advancing the activity cliff concept, part II

et al. 2014

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We present a follow up contribution to further complement a previous commentary on the activity cliff concept and recent advances in activity cliff research. Activity cliffs have originally been defined as pairs of structurally similar compounds that display a large difference in potency against a given target. For medicinal chemistry, activity cliffs are of high interest because structure-activity relationship (SAR) determinants can often be deduced from them. Herein, we present up-to-date results of systematic analyses of the ligand efficiency and lipophilic efficiency relationships between activity cliff-forming compounds, which further increase their attractiveness for the practice of medicinal chemistry. In addition, we summarize the results of a new analysis of coordinated activity cliffs and clusters they form. Taken together, these findings considerably add to our evaluation and current understanding of the activity cliff concept. The results should be viewed in light of the previous commentary article.

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Formation of Activity Cliffs Is Accompanied by Systematic Increases in Ligand Efficiency from Lowly to Highly Potent Compounds

Cited by 5 publications

References 17 publications

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling

Identification of Interaction Hot Spots in Structures of Drug Targets on the Basis of Three‐Dimensional Activity Cliff Information

Advancing the activity cliff concept, part II

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