What can Torch do for you that TorchLite can't?


TorchLite is the powerful freeware 3D molecule viewer, editor and design tool from Cresset. However, there are situations in which modeling with TorchLite is simply not enough and you need to access the full power of Torch. This blog post highlights some of the features which make Torch a powerful molecular design tool for medicinal and synthetic chemists.


You can see several interesting applications of TorchLite in our case studies and web clips. With TorchLite, you can view the results of ligand-based or structure-based virtual screening, understand the shape and electrostatic character of active molecules and design new molecules to match their pattern. But what are the differences between TorchLite and its big brother Torch? When should you start using Torch?

In this blog, I highlight some of the additional features available in Torch, but not in TorchLite, with examples of their application.

SAR analysis in TorchLite

The web clip Visualizing field changes to understand SAR shows how to quickly investigate the SAR of a small dataset of NaV1.7 inhibitors using TorchLite. Structures were manually sketched using the built-in 3D molecule editor, quickly minimized and saved in the Molecules table and NaV1.7 activity data manually entered. This works nicely for this small dataset, however, for larger compound sets manual editing and data entry is slow and open to human error. Also, manual editing and minimization in TorchLite cannot replace a full exploration of the conformational space of compounds, which ensures that diverse, low energy conformations are considered in the SAR analysis. Finally, while alignment is straightforward for the simple changes carried out in the web clip, a robust method for sensibly aligning the compounds is required when more complex structural changes are made.

This is the most important difference between the two packages: conformational exploration and alignment can be carried out in Torch (and Forge), but not in TorchLite.

SAR analysis in Torch

In Torch, molecules are aligned to one or more reference molecules using fixed conformations, which can be imported into Torch or calculated on the fly by the application.

Suitable reference molecules are highly active molecules, preferably in their bioactive (protein bound) conformation. This is usually either experimentally observed (when crystallographic information is available), or derived from a docking experiment or pharmacophore modeling (these methods are also available in Lead Finder and Field Templater, respectively).

Using a ‘Normal’ alignment, the conformation ensemble for each molecule in the data set is aligned to the reference molecule in two stages. In the first stage the field points around a molecule are used to generate an initial alignment. In the second stage the initial alignment is optimized to get the best possible similarity score. In this stage, it is possible for Torch to use an excluded volume, typically derived from the protein crystal structure, that defines a region of space around the reference molecule that acts as a constraint on the alignments.

Torch offers an additional method for automated molecular alignment. Using the Maximum Common Substructure (MCS) approach each ligand is initially fitted to the reference molecule using a common-substructure algorithm and then additional groups are the fitted using the best match of field points and shape. This substructure alignment can be regarded as a ligand-centric view of the match to the reference where the use of the field points alone is akin to a protein-centric view of the alignment.

Each method has their advantages:

  • Field points give an unbiased view of alignment with a score that can be used in, for example, virtual screening
  • The substructure approach highlights the differences between molecules that lie in the same series making them easier to interpret, particularly when using ligand-centric computational techniques such as the activity cliff analyses in Activity Miner and Activity Atlas, as in the example below.

Using alignment in SAR studies

In the case study Activity Atlas analysis of sodium channel antagonists. Part I: SAR of the right-hand side phenyl ring a dataset of 62 pyrrolopyrimidine NaV1.7 antagonists was downloaded from CheMBL, conformationally explored in Forge and aligned by MCS to the chosen reference compound.

Figure 1. The reference compound used to align the NaV1.7 data set.
The SAR of the data set was then analyzed using Activity Atlas, a probabilistic method of analyzing the SAR of a set of aligned compounds as a function of their electrostatic, hydrophobic and shape properties, available in Forge.

A more simple workflow can be implemented in Torch to quickly and effectively explore the SAR on the right-hand side phenyl ring (Figure 1) using Activity Miner, an optional module of Torch (included in Forge).

The ‘Substructure’ filter in Torch was used to select a subset of 17 compounds from the original data set which have the same scaffold and left-hand side substituent as Cmpd 1, but vary on the right-hand side phenyl, following the workflow shown in Figure 2.

Figure 2. Filter by substructure in Torch.
The lowest energy conformation of Cmpd 1 (one of the most active compounds in the data set) was then chosen as a reference structure, following an ‘accurate but slow’ (Max number of conformations: 200; RMS cut-off for duplicate conformers: 0.5; Gradient cut-off for conformer minimization: 0.1 kcal/mol; Energy window: 3 kcal/mol) conformation hunt within Torch. This was used to align the 17 compounds by Maximum Common Substructure, using again an ‘accurate but slow’ set-up for the conformation hunt.

The SAR of the right-hand substituted compounds can then be explored using the activity view maps calculated and displayed by Activity Miner.

The activity view shows a focus compound surrounded by its nearest neighbors according to the chosen similarity metric (Figure 3). In this view the height of each wedge corresponds to the ‘distance’ between the pair: a smaller wedge reflects very similar compounds.

Figure 3. Activity view map for Nav1.7 pIC50, showing the detailed SAR of the phenyl ring.
The color of the wedge reflects the direction the activity is going: red means the activity is decreasing; green means the activity is increasing between the pair.

The shading echoes the disparity, which relates to how steep the activity cliff is. The result is a focused view of the SAR around a chosen compound.

Figure 3 also shows the activity view around the unsubstituted phenyl (pIC50 6.6). This view clearly shows that para substitution is always detrimental for NaV1.7 activity: ortho substitution is beneficial, especially with a small halogen like Fluorine; and meta substitution is also in general beneficial. Ortho, ortho substitution, instead, is less tolerated.

Design of new molecules using Torch

One of the major advantages of field based alignment is that it is agnostic to the chemical series that is being aligned. This can be used to aid in the design of new compounds in Torch by aligning diverse actives to a common reference and then transferring key functional groups across series. In this example, I use the crystal structure of HDT, a potent Cyclin-Dependent Kinase inhibitor, bound to CDK2 (PDB code 1OIT) to modify the design of an oxime based inhibitor.

As can be seen in Figure 4, HDT interacts with the hinge region of the active site of CDK2 by making two H-bond interactions with the backbone carbonyl and NH of Leu 83, and a H-bond interaction with Lys 33. The sulphonamide group also makes H-bond interactions with Asp86 (not shown).

Figure 4. HDT bound to the CDK2 active site.
In this design experiment, more potent CDK2 inhibitors are designed starting from the 2D structure of compound CK3 (Figure 5), a smaller and less potent CDK2 inhibitor with a Ki 2200 nM using the interactions of HDT as a guide.
The 2D structure of CK3 (drawn with a favorite drawing package) was imported in Torch by copy/paste. CK3 was then aligned to HDT using an accurate but slow conformation hunt followed by a ‘Normal’ (field based) alignment.

Figure 5. Structure of CK3, an inhibitor of CDK2 (Ki 2200 nM).
Figure 6 shows the results of the alignment experiments. CK3 (grey) is nicely superimposed to HDT (pink) and it is straightforward to see which changes should be made to increase CDK2 potency, replacing the formamidine moiety with a phenyl ring, possibly decorated with a sulphonamide or other H-bond acceptor group in the para position.

Figure 6. CK3 (grey) aligned to HDT (pink).
This change can be easily done in the molecule editor available in Torch, using the reference structure as a guide. As changes are made in the editor, the similarity score (Figure 7) is updated on the fly by clicking on the ‘Minimize’ and ‘Optimize Alignment’ buttons. Once the editing is completed, clicking the ‘Align’ button in the molecule editor will prompt Torch to carry out a full conformation hunt and field alignment on the new design.

Figure 7. The Molecule Editor in Torch.
The structure of CK6, an analogue of CK3 with CDK2 Ki 70 nM, aligned to HDT in Torch are shown in Figure 8 (left). The superimposed crystal structures of CK6 and HDT as in the PDBs 1PXN and 1OIT, respectively shown in Figure 8 (right). The alignment in Torch almost perfectly matches the crystallographic alignment of these two ligands in the CDK2 active site.

Figure 8. Left: CK6 (grey) aligned to HDT (pink) using Torch. Right: superimposed crystal structures of CK6 (grey) and HDT (pink) as in PDB entries 1PXN and 1OIT.

Multi-Parameter Scoring

Multi-Parameter Scoring in Torch helps medicinal and synthetic chemists assess the overall physico-chemical profile of the compounds of interest using colors and radial plots. As can be seen in Figure 9, columns in Torch are colored according to a profile set up in the Torch preferences. Properties perfectly matching the desired profile are colored in green, those with an acceptable value in yellow, while those with an unacceptable value in red.

The profile can be tailored to the specific project needs in the Radial Plot Properties window. In this window, a weight can be also associated to each property based on its importance in the ideal project profile. The score and fit to the project profile for each molecule is then summarized in the radial plot.

The radial plot is based on the idea that molecule properties that are ‘perfect’ should be displayed at the center of the radial plot. Thus, a molecule with perfect or near perfect properties should have a radial plot with a small encapsulated area (shown in green). Conversely, poor properties would be plotted at the edge of the radial plot such that a molecule with sub-ideal properties would have a radial plot with a large enclosed area (this can be reversed using the Radial Plot Preferences).

In Figure 9, you can see the column coloring for the CDK2 project. Comparing the color coloring of CK3 and CK6, most properties have values matching the ideal property profile. CDK2 Ki has significantly improved from CK3 to CK6, while lipophilicity (SlogP) is less good in CK6. CK3+phenyl (Figure 9, Molecules table) is slightly less active than CK6 and its lipophilicity is high with respect to the other two compounds: another good reason for including a hydrophilic H-bond acceptor in the para position of the phenyl ring.

The radial plot properties are combined into a single score that represents the overall fit of molecule to the ideal project profile. Radial plots can be sorted and filtered based on this score, making it easier to select the best candidates for your projects.

Figure 9. Multi-parameter scoring in Torch.


This blog highlights some of the additional features in Torch, the powerful molecular design tool for medicinal and synthetic chemists.

Additional functionality available in Torch includes the capability to:

  • run virtual screening of up to 500 molecules
  • use Activity Atlas and 2D/3D-QSAR models built with Forge
  • create interactive multi-series scatter plots and histograms of biological or physical properties
  • import calculated and/or measured physical properties and data from an external web service through a REST interface.

Contact us to benefit from this functionality and try the full power of Torch.

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