Biologically Relevant Chemical Space Navigator: From Patent and Structure–Activity Relationship Analysis to Library Acquisition and Design

A new and versatile visualization tool, based on a descriptor accounting for ligand–receptor interactions (LiRIf), is introduced for guiding medicinal chemists in analyzing the R-groups from a congeneric series. Analysis is performed in a reference-independent scenario where the whole biologically relevant chemical space (BRCS) is represented. Using a real project-based data set, we show the impact of this tool on four key navigation strategies for the drug discovery process. First, this navigator analyzes competitors’ patents, including a comparison of patents coverage and the identification of the most frequent fragments. Second, the tool analyzes the structure–activity relationship (SAR) leading to the representation of reference-independent activity landscapes that enable the identification not only of critical ligand–receptor interactions (LRI) and substructural features but also of activity cliffs. Third, this navigator enables comparison of libraries, thus selecting commercially available molecules that complement unexplored spaces or areas of interest. Finally, this tool also enables the design of new analogues, which is based on reaction types and the exploration purpose (focused or diverse), selecting the most appropriate reagents

Using Novel Descriptor Accounting for Ligand–Receptor Interactions To Define and Visually Explore Biologically Relevant Chemical Space

The definition and pragmatic implementation of biologically relevant chemical space is critical in addressing navigation strategies in the overlapping regions where chemistry and therapeutically relevant targets reside and, therefore, also key to performing an efficient drug discovery project. Here, we describe the development and implementation of a simple and robust method for representing biologically relevant chemical space as a general reference according to current knowledge, independently of any reference space, and analyzing chemical structures accordingly. Underlying our method is the generation of a novel descriptor (LiRIf) that converts structural information into a one-dimensional string accounting for the plausible ligand–receptor interactions as well as for topological information. Capitalizing on ligand–receptor interactions as a descriptor enables the clustering, profiling, and comparison of libraries of compounds from a chemical biology and medicinal chemistry perspective. In addition, as a case study, R-groups analysis is performed to identify the most populated ligand–receptor interactions according to different target families (GPCR, kinases, etc.), as well as to evaluate the coverage of biologically relevant chemical space by structures annotated in different databases (ChEMBL, Glida, etc.)

Biologically Relevant Chemical Space Navigator: From Patent and Structure–Activity Relationship Analysis to Library Acquisition and Design

A new and versatile visualization tool, based on a descriptor accounting for ligand–receptor interactions (LiRIf), is introduced for guiding medicinal chemists in analyzing the R-groups from a congeneric series. Analysis is performed in a reference-independent scenario where the whole biologically relevant chemical space (BRCS) is represented. Using a real project-based data set, we show the impact of this tool on four key navigation strategies for the drug discovery process. First, this navigator analyzes competitors’ patents, including a comparison of patents coverage and the identification of the most frequent fragments. Second, the tool analyzes the structure–activity relationship (SAR) leading to the representation of reference-independent activity landscapes that enable the identification not only of critical ligand–receptor interactions (LRI) and substructural features but also of activity cliffs. Third, this navigator enables comparison of libraries, thus selecting commercially available molecules that complement unexplored spaces or areas of interest. Finally, this tool also enables the design of new analogues, which is based on reaction types and the exploration purpose (focused or diverse), selecting the most appropriate reagents

Using Novel Descriptor Accounting for Ligand–Receptor Interactions To Define and Visually Explore Biologically Relevant Chemical Space

The definition and pragmatic implementation of biologically relevant chemical space is critical in addressing navigation strategies in the overlapping regions where chemistry and therapeutically relevant targets reside and, therefore, also key to performing an efficient drug discovery project. Here, we describe the development and implementation of a simple and robust method for representing biologically relevant chemical space as a general reference according to current knowledge, independently of any reference space, and analyzing chemical structures accordingly. Underlying our method is the generation of a novel descriptor (LiRIf) that converts structural information into a one-dimensional string accounting for the plausible ligand–receptor interactions as well as for topological information. Capitalizing on ligand–receptor interactions as a descriptor enables the clustering, profiling, and comparison of libraries of compounds from a chemical biology and medicinal chemistry perspective. In addition, as a case study, R-groups analysis is performed to identify the most populated ligand–receptor interactions according to different target families (GPCR, kinases, etc.), as well as to evaluate the coverage of biologically relevant chemical space by structures annotated in different databases (ChEMBL, Glida, etc.)

Biologically Relevant Chemical Space Navigator: From Patent and Structure–Activity Relationship Analysis to Library Acquisition and Design

A new and versatile visualization tool, based on a descriptor accounting for ligand–receptor interactions (LiRIf), is introduced for guiding medicinal chemists in analyzing the R-groups from a congeneric series. Analysis is performed in a reference-independent scenario where the whole biologically relevant chemical space (BRCS) is represented. Using a real project-based data set, we show the impact of this tool on four key navigation strategies for the drug discovery process. First, this navigator analyzes competitors’ patents, including a comparison of patents coverage and the identification of the most frequent fragments. Second, the tool analyzes the structure–activity relationship (SAR) leading to the representation of reference-independent activity landscapes that enable the identification not only of critical ligand–receptor interactions (LRI) and substructural features but also of activity cliffs. Third, this navigator enables comparison of libraries, thus selecting commercially available molecules that complement unexplored spaces or areas of interest. Finally, this tool also enables the design of new analogues, which is based on reaction types and the exploration purpose (focused or diverse), selecting the most appropriate reagents

MOESM1 of Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Additional file 1. Additional figures and tables

MOESM2 of Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Additional file 2. Sequence alignment

Novel Scaffold Fingerprint (SFP): Applications in Scaffold Hopping and Scaffold-Based Selection of Diverse Compounds

A novel 2D <u>S</u>caffold <u>F</u>inger<u>P</u>rint (SFP) for mining ring fragments is presented. The rings are described not only by their topology, shape, and pharmacophoric features (hydrogen-bond acceptors and donors, their relative locations, sp3 carbons, and chirality) but also by the position and nature of their growing vectors because they play a critical role from the drug discovery perspective. SFP can be used (i) to identify alternative chemotypes to a reference ring either in a visual mode or by running quantitative similarity searches and (ii) in chemotype-based diversity selections. Two retrospective case studies focused on melanin concentrating hormone 1-receptor antagonists (MCH-R1) and phosphodiesterase-5 inhibitors (PDE5) demonstrate the capability of this method for identifying novel structurally different and synthetically accessible chemotypes. Good enrichment factor (155 and 219) and recall values (46% and 73%) are found within the first 100 ranked hits (0.3% of screened database). Our 2D SFP descriptor outperforms well-validated current gold-standard 2D fingerprints (ECFP_6) and 3D approaches based on shape and electrostatic similarity. Scaffold-based selection of diverse compounds has a critical impact on corporate library design and compound acquisitions; thus, a novel strategy is introduced that uses diverse scaffold selections using this SFP descriptor combined with R-group selection at the different substitution sites. Both approaches are available as part of an interactive web-based application that requires minimal input and no computational knowledge by medicinal chemists

Novel Scaffold Fingerprint (SFP): Applications in Scaffold Hopping and Scaffold-Based Selection of Diverse Compounds

A novel 2D <u>S</u>caffold <u>F</u>inger<u>P</u>rint (SFP) for mining ring fragments is presented. The rings are described not only by their topology, shape, and pharmacophoric features (hydrogen-bond acceptors and donors, their relative locations, sp3 carbons, and chirality) but also by the position and nature of their growing vectors because they play a critical role from the drug discovery perspective. SFP can be used (i) to identify alternative chemotypes to a reference ring either in a visual mode or by running quantitative similarity searches and (ii) in chemotype-based diversity selections. Two retrospective case studies focused on melanin concentrating hormone 1-receptor antagonists (MCH-R1) and phosphodiesterase-5 inhibitors (PDE5) demonstrate the capability of this method for identifying novel structurally different and synthetically accessible chemotypes. Good enrichment factor (155 and 219) and recall values (46% and 73%) are found within the first 100 ranked hits (0.3% of screened database). Our 2D SFP descriptor outperforms well-validated current gold-standard 2D fingerprints (ECFP_6) and 3D approaches based on shape and electrostatic similarity. Scaffold-based selection of diverse compounds has a critical impact on corporate library design and compound acquisitions; thus, a novel strategy is introduced that uses diverse scaffold selections using this SFP descriptor combined with R-group selection at the different substitution sites. Both approaches are available as part of an interactive web-based application that requires minimal input and no computational knowledge by medicinal chemists

Novel Scaffold Fingerprint (SFP): Applications in Scaffold Hopping and Scaffold-Based Selection of Diverse Compounds

A novel 2D <u>S</u>caffold <u>F</u>inger<u>P</u>rint (SFP) for mining ring fragments is presented. The rings are described not only by their topology, shape, and pharmacophoric features (hydrogen-bond acceptors and donors, their relative locations, sp3 carbons, and chirality) but also by the position and nature of their growing vectors because they play a critical role from the drug discovery perspective. SFP can be used (i) to identify alternative chemotypes to a reference ring either in a visual mode or by running quantitative similarity searches and (ii) in chemotype-based diversity selections. Two retrospective case studies focused on melanin concentrating hormone 1-receptor antagonists (MCH-R1) and phosphodiesterase-5 inhibitors (PDE5) demonstrate the capability of this method for identifying novel structurally different and synthetically accessible chemotypes. Good enrichment factor (155 and 219) and recall values (46% and 73%) are found within the first 100 ranked hits (0.3% of screened database). Our 2D SFP descriptor outperforms well-validated current gold-standard 2D fingerprints (ECFP_6) and 3D approaches based on shape and electrostatic similarity. Scaffold-based selection of diverse compounds has a critical impact on corporate library design and compound acquisitions; thus, a novel strategy is introduced that uses diverse scaffold selections using this SFP descriptor combined with R-group selection at the different substitution sites. Both approaches are available as part of an interactive web-based application that requires minimal input and no computational knowledge by medicinal chemists