SAR Maps:  A New SAR Visualization Technique for Medicinal Chemists

Agrafiotis, Dimitris; Shemanarev, Maxim; Connolly, Peter J.; Farnum, Michael; Lobanov, Victor S.

doi:10.1021/jm070845m

Cited by 69 publications

(73 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[5] The SAR Index (SARI) developed by Peltason and Bajorath is a method to quantify the topology of SAR landscapes. [6] The SARI term correlates 2D structural similarity with compound potency and characterizes the landscape of data sets into SAR categories: continuous, discontinuous, and heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

2011

View full text Add to dashboard Cite

The role of activity cliffs in drug discovery projects is certainly two-edged: on the one hand, they often lead to the failure of QSAR modeling techniques; on the other, they are highly valuable for identifying key aspects of SARs. In the presence of activity cliffs the results of purely ligand-based QSAR approaches often remain puzzling, and the resulting models have limited predictive power. Herein we present a new approach for the identification of structure-based activity cliffs (ISAC). It uses the valuable information of activity cliffs in a structure-based design scenario by analyzing interaction energies of protein-ligand complexes. Using the relative frequency at which a protein atom is involved in activity cliff events, we introduce a novel visualization of hot spots in the active site of a protein. The ISAC approach supports the medicinal chemist in elucidating the key interacting atoms of the binding site and facilitates the development of pharmacophore hypotheses. The hot spot visualization can be applied to small data sets in early project phases as well as in the lead optimization process. Based on the ISAC approach, we developed a method to derive target-specific scoring functions and pharmacophore constraints, which were validated on independent external data sets in virtual screening experiments. The activity-cliff-based approach shows an improved enrichment over the generic empirical scoring function for various protein targets in the validation set.

show abstract

Section: Introductionmentioning

confidence: 99%

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

2011

View full text Add to dashboard Cite

show abstract

“…The approach makes it possible to identify undersampled regions and highlight key substitution patterns which determine the SAR of a compound series. An alternative way to visualize SARs in analogous series with a common scaffold is offered by the "SAR maps" invented by Agrafiotis et al [65] In a "SAR map", each series is rendered as a rectangular matrix of cells, each representing a unique combination of substituents (i.e., a unique compound). Color-coding the cells by their potency easily identifies SAR patterns.…”

Section: Navigation In Graph-based Chemical Spacementioning

confidence: 99%

Chemoinformatics as a Theoretical Chemistry Discipline

Varnek

Baskin

2011

Molecular Informatics

101

View full text Add to dashboard Cite

Here, chemoinformatics is considered as a theoretical chemistry discipline complementary to quantum chemistry and force-field molecular modeling. These three fields are compared with respect to molecular representation, inference mechanisms, basic concepts and application areas. A chemical space, a fundamental concept of chemoinformatics, is considered with respect to complex relations between chemical objects (graphs or descriptor vectors). Statistical Learning Theory, one of the main mathematical approaches in structure-property modeling, is briefly reviewed. Links between chemoinformatics and its "sister" fields - machine learning, chemometrics and bioinformatics are discussed.

show abstract

“…Original compounds are then assigned to corresponding MCSs and represented as a second node category, i.e., compound nodes colored by potency. MCS-based visualization methods have also been introduced to organize individual compound series and elucidate SAR patterns [6][7][8]. In addition to network representations, tree-like structures have been designed to graphically organize compound series and study SAR trends in chemical neighborhoods [8,9].…”

Section: Introductionmentioning

confidence: 99%

Visualization of multi-property landscapes for compound selection and optimization

Léon

Kayastha

Dimova

et al. 2015

J Comput Aided Mol Des

View full text Add to dashboard Cite

Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.

show abstract

SAR Maps: A New SAR Visualization Technique for Medicinal Chemists

Cited by 69 publications

References 23 publications

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

Chemoinformatics as a Theoretical Chemistry Discipline

Visualization of multi-property landscapes for compound selection and optimization

Contact Info

Product

Resources

About