EXPLORATION OF STRUCTURE–ACTIVITY RELATIONSHIPS (SARs) AND TRANSFER OF KEY ELEMENTS IN LEAD OPTIMIZATION

Matter, Hans; Güssregen, Stefan; Schmidt, Friedemann; Heßler, Gerhard; Naumann, Thorsten; Baringhaus, Karl‐Heinz

doi:10.1002/9781118742785.ch10

Cited by 1 publication

(2 citation statements)

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“…Lead optimization is a key activity in medicinal chemistry for progressing chemical series toward clinical development candidates. Knowledge-based decisions to guide these activities require attention to biological, pharmacokinetic, and physicochemical data as drivers for optimization . The effect of changes in these properties can often be rationalized using structure–activity relationship (SAR) analysis .…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge-based decisions to guide these activities require attention to biological, pharmacokinetic, and physicochemical data as drivers for optimization. 1 The effect of changes in these properties can often be rationalized using structure−activity relationship (SAR) analysis. 2 Variations in overall biological and physicochemical profiles are typically correlated to structural differences of scaffolds, compound pairs (matched molecular pairs, MMPs) 3−5 and chemical series (congeneric series with several MMPs and activity trends).…”

Section: ■ Introductionmentioning

confidence: 99%

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Using Graph Databases to Investigate Trends in Structure–Activity Relationship Networks

Matter

Buning

Stefanescu

et al. 2020

J. Chem. Inf. Model.

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Mining the steadily increasing amount of chemical and biological data is a key challenge in drug discovery. Graph databases offer viable alternatives for capturing interrelationships between molecules and for generating novel insights for design. In a graph database, molecules and their properties are mapped to nodes, while relationships are described by edges. Here, we introduce a graph database for navigation in chemical space, analogue searching, and structure–activity relationship (SAR) analysis. We illustrate this concept using hERG channel inhibitors from ChEMBL to extract SAR knowledge. This graph database is built using different relationships, namely 2D-fingerprint similarity, matched molecular pairs, topomer distances, and structure–activity landscape indices (SALI). Typical applications include retrieving analogues linked by single or multiple edge paths to the query compound as well as detection of nonadditive SAR features. Finally, we identify triplets of linked molecules for clustering. The speed of searching and analysis allows the user to interactively navigate the database and to address complex questions in real-time.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%