VisualiSAR: A Web-based SAR Tool

Overview of presentation

• Introduction to VisualiSAR technologies
• Features and Scope of VisualiSAR
• Current Developments & Spinoff Work
• Improved alignment of Daylight depictions
• Comparison of different fingerprint types for clustering
• Evaluation of cluster level selection methods

The origins of VisualiSAR

• We wanted to develop the Stigmata and Modal Fingerprint idea and make it more accessible
• We saw the potential for using the technology for analyzing 'fuzzy' high-volume screening data
• finding clusters of active compounds
• highlighting commonality within the clusters
• comparing with similar, inactive compounds
• A tool for doing this could also have application for general 2D structure browsing. It should be as easy to use as possible.

VisualiSAR techniques

• Ward's Clustering
• for grouping similar compounds together
• Modal Fingerprints / Stigmata
• for sorting compounds and highlighting similarities
• Web Interface
• for easy use, platform independence and interoperability

Technique 1: Ward's clustering

• Hierarchical, agglomerative clustering method
• Begins with each compound in its own cluster, then iteratively merges clusters until there is just one cluster, producing a cluster hierarchy
• We use binary fingerprints as the descriptors
• Similarity measure is Euclidean distance between cluster centroids
• 'Problem' of which level in the hierarchy to select

Technique 2: Modal fingerprints and Stigmata coloring

Here are some images which give an example of how sorting a cluster of compounds by MSIM and then using coloring can help to show the common features to the cluster. The first image shows the cluster without any sorting or coloring. The second image shows the cluster sorted by MSIM (using the 75% modal) and colored showing the common features.

With large clusters, viewing a representative sample of the cluster can be useful, as can viewing the top and bottom-ranked compounds from the set:

Technique 3: VisualiSAR Web interface

VisualiSAR is one example application that uses a Perl / CGI interface on top of the fingerprint manipulation toolkit.. It is written in Perl, and is interacted with through a CGI / HTML interface. VisualiSAR uses standard HTML, with no Java or Javascript. Our philosophy was to use good information design and interface design in the development of the interface, and then to implement the interface using the simplest technology possible.

The opening screen is where compounds can be supplied as a SMILES or SD file, or pasted into the paste box. VisualiSAR then shows an initial view of the data set, with a sample of nine compounds (representing different levels of similarity to the modal) shown. The toolbar on the left allows the user access to the functionality of VisualiSAR. After clustering, the user can scroll through the clusters, or jump directly to a particular cluster using the links on the left. Here, each cluster has also been colored by commonality to the modal of the cluster.

VisualiSAR features

• Input via SMILES or SDFILE
• Can accept cutting and pasting e.g. from Excel or JMP
• Clustering based on fingerprints
• Automatic prioritization of cluster levels
• Modal analysis and color mapping of datasets or clusters, including threshold manipulation
• Flexible sorting options
• Comparison with an external modal fingerprint and with other datasets
• Export of clusters, cluster information and modal fingerprints
• Non-Java frames-based Web interface - platform independent

VisualiSAR scope

• Can currently handle up to 5,000 compounds (for clustering) or 50,000 (for searching)
• Clustering speed could be improved
• 500 cmpds = 1.5min, 5000 cmpds = 2 hours
• can be speeded up with RNN
• could use a faster algorithm, e.g. Jarvis-Patrick or K-Means
• finding optimal level slows things down further
• Could handle >50,000 compounds by externalizing similarity search
• if Merlin could accept a fingerprint for a similiarity search, then it could be used for modal similarity searching
• Requires special software for generating color mapping
• atom-fingerprinting is available for Daylight fingerprints, and is being investigated for BCI fingerprints
• is computationally intensive

An example SAR strategy with VisualiSAR

• Collect and cluster all compounds
• Sort each cluster by activity or similarity to the modal
• Use Stigmata coloring to highlight the similarities and differences within the clusters
• Look for the key differences within clusters that cause a large change in activity

A different way of viewing compounds is to just cluster the actives, and then search the inactives for similar compounds to those in the active cluster (done by sorting and coloring the inactives by similarity to the modal of the active cluster). Thus features that may be responsible for activity and inactivity can be highlighted. An active cluster and similar inactives can be compared using the split-screen capability of VisualiSAR (active cluster at the top).

Summary

• By presenting compounds in the right way, you can make discernment of chemical similarity and difference easier
• There are a number of ways of visually identifying structure-activity relationships
• Finding a cluster of actives and sorting and coloring the inactives by similarity to the modal of the actives
• Clustering actives and inactives together and looking for small changes in structure which result in large changes in activity
• A structure browsing and exploration tool like VisualiSAR can help in any situation where there are more than a handful of compounds to be studied

Future developments

• Explore the use of other fingerprint types
• Cluster more efficiently
• use an RNN implementation of Wards
• or link in with Daylight Jarvis-Patrick Clustering
• Cluster more effectively
• different fingerprint types
• hierarchy level selection methods

Spinoff fingerprint & clustering research (in progress)

• What kind of fingerprints are best for clustering compounds together into chemical series?
• MACCS - 166 bits each representing a ?chemically relevant? fragment
• Daylight - structural sequences in a compound hashed onto 2,048 bits
• Unity - 60 fixed fragments plus 928 hashed from compounds
• BCI - can generate fragments or use a pre-defined dictionary
• How can we select a level from a Wards clustering hierarchy that most closely represents the chemical series present?
• Methods mainly selected from:
• Milligan & Cooper, Psychometrika, 50, 2, 1985

Algorithm for improved alignment of depictions

• Normalize coordinates of A and B
• Store four sets of coordinates for B: as is, flipped horizontally, flipped vertically, and flipped horizontally and vertically
• For each atom in A, sum the distances between the atom and each atom in each coordinate set of B that has the same geometric environment (atom type and number of bonds)
• Sum this value over all the atoms in A for each coordinate set, and choose the coordinate set with the lowest value as our preferred depiction

Acknowledgements

• Parke-Davis
• George Cowan - Algorithm design
• T.J. O'Donnell - Web development
• Alain Calvet, Christine Humblet
• Daylight
• Norah McCuish - Original Stigmata code, code for SMILES depiction in GIF
• Jeremy Yang - Integrated atom fingerprinting into toolkit