Activity Atlas1 is a new component available in Forge2, Cresset’s powerful workbench for ligand design and SAR analysis. Activity Atlas models summarize the SAR for a series into a visual 3D model that informs design decisions and helps prioritize molecules for synthesis. This new method is particularly useful for project teams where there is not enough SAR for a traditional 3D-QSAR approach. In this case study, Activity Atlas was used to analyze the SAR of a series of adenosine A1, adenosine A2a and adenosine A3 antagonists, with the objective to investigate and understand the electrostatic, hydrophobic and shape features underlying receptor selectivity.
Activity Atlas is a probabilistic method of analyzing the Structure-Activity Relationships of a set of aligned compounds as a function of their electrostatic, hydrophobic and shape properties. The method uses a Bayesian approach to take a global view of the data in a qualitative manner. Results are displayed using Forge visualization capabilities to gain a better understanding of the features which underlie the SAR of your set of compounds.
Activity Atlas calculates and displays as 3D visualizations the:
- ‘Activity cliff summary’: what do the activity cliffs tell us about the SAR?
- ‘Average of actives’: what do active molecules have in common?
- ‘Regions explored’: where have I been? For a new molecule, would making it increase our understanding? This analysis also calculates a novelty score for each molecule.
In this case study, the activity cliff summary method in Activity Atlas was used to analyze the SAR of a series of published3 adenosine A1, adenosine A2a and adenosine A3 antagonists, with the objective of understanding the electrostatic, hydrophobic and shape features underlying A2a over A1 and A2a over A3 selectivity.
The data set
The data set of 342 compounds originally published by Dimova and Bajorath3 was downloaded from the supplementary material together with their adenosine A1, A2a and A3 receptors potency values. A subset of 102 tricyclic compounds (see Figure 1) was selected for the Activity Atlas analysis.
- A2a over A1 selectivity = pA2a potency – pA1 potency
- A2a over A3 selectivity = pA2a potency – pA3 potency.
Conformation hunt and alignment of compounds
Cmpd321, Cmpd296 and Cmpd249 (see Figure 1) were chosen as the reference structures to drive the alignment of the full training set of 102 compounds.
A conformation hunt was carried out for Cmpd321 within Forge: an extended low energy conformation was chosen as the initial reference structure to which Cmpd296 and Cmpd249 were aligned by Maximum Common Substructure.
The 102 compounds in the training set were then aligned to the three reference compounds in Figure 1 by Maximum Common Substructure using a ‘very accurate but slow’ set-up for the conformation hunt:
- Max number of conformations: 1000
- RMS cut-off for duplicate conformers: 0.5
- radient cut-off for conformer minimization: 0.1 kcal/mol
- Energy window: 3 kcal/mol.
The use of a 3D similarity metric in Activity Atlas requires (as with 3D-QSAR) the generation of alignments for all compounds and is sensitive to misalignment and alignment noise. For this reason, visual inspection of alignments is always recommended, to ensure that there are no anomalies present. Where the calculated alignment is sub-optimal, manual intervention can be used to improve it. In this case study, the alignment of a few compounds was manually adjusted by flipping the phenyl ring on the phenyl-urea side chain (see Figure 1), to align the ortho and meta substituents in a consistent manner across the whole dataset.
Activity Atlas models are calculated following a probabilistic approach which takes into account the probability that a molecule is correctly aligned, as shown in Figure 3 below, rather than assuming that the top scoring or the selected preferred alignment is the correct alignment.
This is done by associating a weight with each alignment based on its similarity score. Alignments with similarity higher than a certain threshold (which can either be automatically calculated by Forge, or manually defined by the user) are fully trusted. Alignments with similarity lower than the low similarity threshold are not trusted and discarded. Linear scaling is applied to associate a proper weight to alignments which have an intermediate similarity score.
Likewise, a weight is also associated with each molecule based on its activity. Molecules whose activity is higher than a certain threshold (which again can either be automatically calculated by Forge, or manually defined by the user) are considered fully active. Molecules whose activity is lower than the low activity threshold are considered inactive.Read More…