- Open Access
Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits
© Gobbi et al.; licensee Springer. 2015
Received: 21 November 2014
Accepted: 9 February 2015
Published: 25 March 2015
After performing a fragment based screen the resulting hits need to be prioritized for follow-up structure elucidation and chemistry. This paper describes a new similarity metric, Atom-Atom-Path (AAP) similarity that is used in conjunction with the Directed Sphere Exclusion (DISE) clustering method to effectively organize and prioritize the fragment hits. The AAP similarity rewards common substructures and recognizes minimal structure differences. The DISE method is order-dependent and can be used to enrich fragments with properties of interest in the first clusters.
The merit of the software is demonstrated by its application to the MAP4K4 fragment screening hits using ligand efficiency (LE) as quality measure. The first clusters contain the hits with the highest LE. The clustering results can be easily visualized in a LE-over-clusters scatterplot with points colored by the members’ similarity to the corresponding cluster seed. The scatterplot enables the extraction of preliminary SAR.
The detailed structure differentiation of the AAP similarity metric is ideal for fragment-sized molecules. The order-dependent nature of the DISE clustering method results in clusters ordered by a property of interest to the teams. The combination of both allows for efficient prioritization of fragment hit for follow-ups.
Screening of low-affinity and low-molecular weight fragments has become a powerful approach to identify lead matter and to initiate medicinal chemistry programs . It has been shown that binding affinity does not increase linear with the molecular size . Fragments are more likely to bind with high ligand efficiencies [3,4] and can reveal important interactions required in more mature drug-like molecules. Choosing fragment hits for structure determination and chemical follow-up involves the evaluation of many project-specific parameters. Ligand efficiency (LE) is often used as one measure for assessing the quality of initial fragment hits.
Due to the generally higher hit rates of fragment screens (1-10%) most campaigns on libraries of 1,000-5,000 compounds will produce more hits, than downstream structural biology and chemistry resources can absorb for hit-to-lead development. To increase the chance of success, careful prioritization of the initial hits by a group of experienced specialists from different areas of drug discovery is important for advancing the most promising fragment hits. Fragment hit triage in advance of a structure determination typically weighs the LE parameter. However, many other properties including affinity, selectivity, and most importantly the chemical structure of the compound need critical consideration. In addition, fragment libraries often contain related molecules providing initial SAR and confidence in scaffold types. Clustering hit sets helps bring related molecules and features together for consideration, but the cluster order is usually determined by the algorithm and is independent of other factors such as LE. This results in a functional randomization of the order of the experimental data making trends harder to identify. To direct the attention of the specialists to the most promising hits we have employed a directed clustering method using a new similarity algorithm, to group hits with respect to both structure and data.
The Directed Sphere Exclusion (DISE) algorithm  used for the fragment clustering is an extension of the Sphere Exclusion (SE) algorithm [6,7]. The first step consists of ordering input molecules by a non-structural parameter such as LE, which is followed by a diverse subset selection using the SE algorithm. The selected molecules are considered as cluster seeds. In the final step, the remaining molecules are assigned to the cluster seeds. The investigator has the choice of two assigning rules: Assign the remaining molecules to (a) the first cluster seed whose similarity to the candidate molecule is within the specified threshold or (b) the most similar cluster seed.
Sorting the input molecules according to decreasing LE ensures that fragments having the highest LE are evaluated first in the cluster seed selection. Assigning the remaining molecules to the cluster seeds using rule (a) yields clusters with decreasing maximum LE while using rule (b) produces clusters that are structurally more consistent. In either case, the DISE clustering places the fragment clusters with favorable LE on top of the list, thus, directing the focus of the review team to the most promising clusters. Consequently, molecules with low LE (LE < 0.3, typically 30-50% of hits in most screens) are either moved towards the bottom of the list or included in clusters with a higher LE compound as cluster seeds, and therefore do not distract the team from clusters that are of higher interest. To our knowledge there are no other clustering methods that allow the clustering to be influenced by a target property. Frequently used clustering methods in chemical informatics  include hierarchical clustering, Jarvis Patrick clustering and k-means clustering. They are either order independent or aim to minimize the order dependency (k-means clustering). This means that their results depend only on the similarity matrix and cannot be influenced by external target properties.
Equally important is the choice of similarity metric. Many similarity metrics have been published in the past [9-15]. For fragment hit clustering, the similarity metric should meet the following requirements: (1) To satisfy the perception of the medicinal chemists, the similarity metric should put a high weight on common substructures. (2) The similarity metric should be sensitive to small changes in substitution. The latter is important because fragments are small in comparison to High Throughput Screening (HTS) compounds. Therefore, small changes have a strong effect on similarity in fragments.
Fingerprint based similarity methods generate fingerprints by systematically recording patterns present in compounds. Generally linear or circular patterns originating from each atom are used. The similarity of two compounds is then computed by comparing the patterns present or absent in each [9-17]. Their relative orientation is not stored while recording the patterns. Consequently, the relative orientation of the patterns in the compounds is not taken into consideration when computing the similarity. When comparing compound 1a using linear fingerprints to 1b a low similarity is found due to the difference of the central atom on the piperidine/piperazine ring as noted by Stahl et al. . When using circular fingerprints compounds 1a and 1b are nearest neighbors. However, because circular fingerprints have a small pattern size and do not encode any information on the relative position of the patterns, the closest compound to 2a is 1b.
For clustering fragment hits we have developed a new similarity metric: the Atom-Atom-Path (AAP) similarity metric that does not need empirical terms. AAP, too, puts a high weight on the common substructures. In the case of the compounds in Figure 1 it correctly recognizes the pairs 1a/1b and 2a/2b as nearest neighbors. The calculation of the AAP similarity of two molecules A and B consists of four steps. In the first step, each atom is described by the set of linear paths originating from it. In the second step, the atom-to-atom similarity of each atom in A to each atom in B is computed. This similarity matrix is used in the third step to map each atom in the smaller molecule to a unique atom in the other. The final step consists of computing the AAP similarity from the atom-to-atom similarities of the mapped atom pairs.
Because the set of atom paths encodes the neighborhood of the given atom, the more similar the sets of paths of two atoms are, the larger the atom-to-atom similarity is. An atom-to-atom similarity of 1 indicates that the two atoms are embedded in a large substructure present in both molecules. As a result, the AAP similarity of a molecule pair increases with the size of the common substructures. The change of a single atom affects the atom-to-atom similarities of neighboring atoms. The AAP similarity is sensitive to very small changes in substitution. Hence, we found that molecules with AAP similarities as low as 0.3 still share many common features. The similarity distribution of AAP as well as circular and linear fingerprint similarities for the full pairwise comparison of 4000 compounds from the Novartis-GNF Malaria Box data  sets is shown in Additional file 1: Figure S3. The distributions show how the AAP similarities are generally much lower than the fingerprint based similarities.
Similarity for use to organize chemical compounds for medicinal chemists is highly subjective . During repeated use at Genentech we have found that clustering using AAP similarity consistently gives results that are in agreement with medicinal chemists’ perception. The mapping of atoms ensures that each atom is considered equally and the description of the atoms ensures a fine grained weighting through their environment. This results in clusters in which ring systems have a high weight avoiding the formation of clusters with large differences in scaffolds. To provide a comparison with Tanimoto similarity using linear and circular fingerprints we have included the clustering of a random subset of the Novartis-GNF Malaria Box dataset using three similarity metrics as Additional file 2.
Atom-Atom-Path similarity algorithm
nAT is the number of atom types (2x108) plus one and nBT is the number of bond types (4) plus one. This representation guarantees a unique integer for each possible path. In the current implementation the path is stored as unsigned integer number with 16 bits. Overflows during the computation of p are ignored and might result in collisions. We have compared similarities calculated using p stored in 8, 16, 32 and 64 bit integer numbers and found that using 16 bit integers gives very similar results to using 64 bits. Using 16 bit integers has a slight performance benefit. If a path is found multiple times, when performing the depth first search on an atom, it is stored multiple times in the list of paths.
The range of SimA,B is between 0 and 1. Given two identical molecules A and A’, all atoms will be mapped to equivalent atoms yielding atom-to-atom similarities SimAi,A’i equal to 1. Therefore, the sum over the mapped atoms in Equation 3 will yield the number of atoms and as a result SimA,A’ will be 1. Any difference between structure A and B will yield atom-to-atom similarities which are lower than 1, therefore SimA,B will be less than 1.
Elapsed time for single threaded similarity matrix computation
Number of compounds
Number of comparisons
The code used to perform the DISE clustering with AAP similarity is available in the Additional file 3. The readme file in the root directory contains information on how to install, compile and get further documentation. The readme file in the “examples/NovartisMalariaBox” subdirectory explains how to reproduce the DISE clustering with AAP similarity using data from the Novartis-GNF Malaria Box .
List of command line programs used in the clustering workflow
sort input records by LE and sort output by cluster index
modify SDF fields and filter records by field contents
copy, rename and delete SDF fields
Additional file 3
select cluster seeds
Additional file 3
assign cluster members to seeds
Additional file 3
perform sphere exclusion clustering by calling other tools (cf. Figure 2)
Results and discussion
Utilizing additional fragment collections, we expanded our previous ~2,500 compound screen of the MAP4K4 kinase  by performing an additional screen of 8,000 compounds . Approximately 600 additional hits with LE ranging from 0.24 to 0.67 and with KD’s between 3 and 930 μM were identified and added to the earlier hit set (Note: LE was computed as 1.4*pKd/nHeavyAtoms). AAP similarity and DISE clustering were used to cluster the combined hit set fragments. The fragment hits were sorted according to their LE, followed by the cluster seed selection. The AAP similarity threshold in this step was 0.3. The remaining hits were assigned to the most similar cluster seed.
The plot can be used to identify clusters containing some initial SAR. Green points that differ largely in LE from their cluster seed are indicative of activity cliffs because small structural changes strongly affect the LE. Red points that differ little in LE from the seed may suggest structural changes that affect the ligand binding mode. These compounds may indicate scaffolds that are prone to flipping their binding pose or selecting different protein conformations during hit expansion.
The cluster seed may not be the smallest molecule in the cluster as exemplified by C02M01 and C02M02. This is intentional as the C02M01 has the higher LE.
Equivalent structural transformations result in identical similarities. C02M02 and C02M03 differ in replacement of the oxygen by hydrogen and chlorine, respectively. They have the same similarity of 0.70 to the cluster seed.
The AAP similarity is sensitive to the substitution pattern. C02M03 and C02M04 differ only in the position of the chlorine atom. In C02M03 the chlorine is a direct replacement for the oxygen in the cluster seed C02M01 while C02M04 differs from the seed in two positions. This results in lower similarity for C02M04. If even more atoms in the molecules are changed, as in C02M05 and C02M06, then the similarity drops further.
A cluster can contain molecules with differences in the core ring system. C12M02 differs from the seed C12M01 in the position of the phenyl group. C12M05 contains a pyrazolo pyridine ring instead of the indazole ring. Both, C12M02 and C12M05 have low similarity to the cluster seed, 0.38 and 0.39 respectively. These values are close to the sphere exclusion threshold of 0.3.
A few activity cliffs can be immediately identified in these clusters. C02M02 and C02M03 show a LE difference of 0.13 and 0.17 to the cluster seed C02M01 although there is a single atom change. C12M03 has also a single atom change to its cluster seed and exhibits a 0.09 difference in LE. Another interesting SAR finding is the small difference in LE between C12M01 and C12M02 although they differ in the core ring system. This is indicative of a change in binding mode or could be due to the phenyl ring in C12M02 occupying a new high affinity binding pocket.
Most screening campaigns produce too many hits to pursue with structure determination or chemical follow-up and the list must be reduced to a small number of high priority compounds. The structural features and physico chemical properties that make a fragment attractive to a given medicinal chemistry program will vary by project. For fragments the LE parameter is typically given a significant weight. During the fragment hit triage meeting, each hit is considered for follow-on activities, including crystallography and SAR exploration through purchase or limited synthesis of analogs. Fragments of high priority are assigned a score of 1, fragments of moderate priority and backups for priority 1 compounds are assigned a score of 2, and hits not of interest a score of 3. When reviewing hit sets clustered using undirected methods every single row must be discussed as the highest LE molecules may be anywhere in the list. For large sets, this can take several hours resulting in fatigue and unintentional exclusion of attractive compounds that fall late in the list. The MAP4K4 screen, as with most of our screens, returned a hit set with 30-50% of the members having LE < 0.3. We could have accelerated the triage by removing them from the hit set. However, when using DISE with AAP similarity the low LE members of high LE clusters stay grouped together giving a more comprehensive view of the SAR as described above, and increased confidence in the chemotypes. This leaves fewer, but more complete, clusters of high interest for review.
The results of the clustering of the MAP4K4 hits were presented to the panel of experts in the order of the cluster index. The panel was asked to collaboratively and interactively assign a score to each hit. To facilitate the conversation the entire hit set was pre-scored on the 1–3 scale using a simple binning and linear weighting function. Briefly, the LE, potencies, chemical diversity (based on the DISE results), selectivity against other co-screened kinases and our historical kinase fragment screening data, historical crystallization success on different targets, were grouped into high, medium, or low bins based on histograms for each property for the full hit set. The linear weighting approach means that different or additional properties can also be included as suits a particular project, such as competition assessments, NMR binding data, thermal melt or enzyme inhibition data, etc. Points were assigned for each property (2 points for a high bin, 1 for a medium, and zero for a low bin). Scores were added and binned by histogram into score 1, 2, and 3. These computed scores were included in the data sheets as unbiased assessments of the compound properties to help focus the discussion towards the merits of the chemistry more than potency. The time for the prioritization meeting was reduced by half. The team submitted 61 compounds to the X-ray workflow providing valuable information for the MAP4K4 project team .
We have described the implementation and use of AAP similarity coupled with DISE clustering as a tool to organize fragment hits. The presorting of the hits by decreasing LE ensures that the most interesting clusters appear at the top, thus, drawing the attention of the review team to the most promising compounds. While we have used LE in the discussion here, in principle the dataset can be presorted and re-clustered based on other properties or efficiency indices. The detailed description of atom neighborhoods used in the AAP similarity allows for the differentiation of even small changes in molecular structure. The combination of the AAP similarity and DISE clustering has also been successfully applied to prioritize high throughput screening hits . The code for this software is available in Additional file 3.
Availability and requirements
Availability: As zip file including source code and examples in the Additional file 3.
Operating system(s): Platform independent (tested on Linux).
Programming language: Java, (csh and perl wrappers around the java programs).
Other requirements: Java 1.6 or higher; Command line programs from the Autocorrelator open source project; OEChem Toolkit 2013 (commercial license required).
License: Apache License.
Any restrictions: software requires license for OEChem Toolkit (commercial license required).
We would like to thank Harald Mauser and Neil Taylor for detailed discussion of the MOS similarity. We also thank Jeff Blaney, Nicholas Skelton and, William Young for their feedback on the manuscript and program code.
- Joseph-McCarthy D, Campbell AJ, Kern G, Moustakas D. Fragment-Based Lead Discovery and Design. J Chem Inf Model. 2014;54:693–704.View ArticleGoogle Scholar
- Kuntz ID, Chen K, Sharp KA, Kollman PA. The Maximal Affinity of Ligands. Proc Natl Acad Sci U S A. 1999;96:9997–10002.View ArticleGoogle Scholar
- Reynolds CH, Tounge BA, Bembenek SD. Ligand Binding Efficiency: Trends, Physical Basis, and Implications. J Med Chem. 2008;51:2432–8.View ArticleGoogle Scholar
- Carr RAE, Congreve M, Murray CW, Rees DC. Fragment-based lead discovery: leads by design. Drug Discov Today. 2005;10:987–92.View ArticleGoogle Scholar
- Gobbi A, Lee M. DISE: Directed Sphere Exclusion. J Chem Inf Comput Sci. 2002;43:317–23.View ArticleGoogle Scholar
- Hudson BD, Hyde RM, Rahr E, Wood J, Osman J. Parameter Based Methods for Compound Selection from Chemical Databases. Quant Struct-Act Relat. 1996;15:285–9.View ArticleGoogle Scholar
- Butina D. Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets. J Chem Inf Comput Sci. 1999;39:747–50.View ArticleGoogle Scholar
- MacCuish JD, MacCuish NE. Clustering in Bioinformatics and Drug Discovery. Boca Raton, FL, USA: CRC Press; 2010.View ArticleGoogle Scholar
- Willett P. The Calculation of Molecular Structural Similarity: Principles and Practice. Mol Inf. 2014;33:403–13.View ArticleGoogle Scholar
- Riniker S, Landrum GA. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform. 2013;5:26.View ArticleGoogle Scholar
- Todeschini R, Consonni V, Xiang H, Holliday J, Buscema M, Willett P. Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets. J Chem Inf Model. 2012;52:2884–901.View ArticleGoogle Scholar
- Raymond JW, Blankley CJ, Willett P. Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures. J Mol Graph Model. 2003;21:421–33.View ArticleGoogle Scholar
- Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, et al. Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org Biomol Chem. 2004;2:3256–66.View ArticleGoogle Scholar
- Willett P, Barnard JM, Downs GM. Chemical Similarity Searching. J Chem Inf Comput Sci. 1998;38:983–96.View ArticleGoogle Scholar
- Stahl M, Mauser H, Tsui M, Taylor NR. A Robust Clustering Method for Chemical Structures. J Med Chem. 2005;48:4358–66.View ArticleGoogle Scholar
- Rogers D, Hahn M. Extended-Connectivity Fingerprints. J Chem Info Model. 2010;50:742–54.View ArticleGoogle Scholar
- Daylight Theory: Fingerprints [http://www.daylight.com/dayhtml/doc/theory/theory.finger.html]
- Raymond JW, Gardiner EJ, Willett P. Heuristics for Similarity Searching of Chemical Graphs Using a Maximum Common Edge Subgraph Algorithm. J Chem Inf Comput Sci. 2002;42:305–16.View ArticleGoogle Scholar
- Gagaring K, Borboa R, Francek C, Chen Z, Buenviaje J, Plouffe D, Winzeler E, Brinker A, Diagana T, Taylor J, Glynne R, Chatterjee A, Kuhen K Novartis-GNF Malaria Box. Genomics Institute of the Novartis Research Foundation (GNF), 10675 John Jay Hopkins Drive, San Diego CA 92121, USA and Novartis Institute for Tropical Disease, 10 Biopolis Road, Chromos # 05–01, 138 670 Singapore. [https://www.ebi.ac.uk/chemblntd]
- Muthas D, Boyer S. Exploiting Pharmacological Similarity to Identify Safety Concerns – Listen to What the Data Tells You. Mol Inf. 2013;32:37–45.View ArticleGoogle Scholar
- Kuhn HW. The Hungarian method for the assignment problem. Naval Research Logistics. 1955;2:83–97.View ArticleGoogle Scholar
- Nedas KA. HungarianAlgorithm.java [https://github.com/w01fe/hungarian]
- Fröhlich H, Wegner JK, Sieker F, Zell A. Optimal Assignment Kernels for Attributed Molecular Graphs. In: Proceedings of the 22Nd International Conference on Machine Learning. New York, NY, USA: ACM; 2005. p. 225–32 [ICML ’05].Google Scholar
- Spinifex version 2.16: Desert Scientific Software Castle Hill, 2154, NSW, Australia (2013). [http://www.desertsci.com/]
- OpenEye Toolkits: OpenEye Scientific Software, Santa Fe, NM. [http://www.eyesopen.com]
- Autocorrelator. [https://code.google.com/p/autocorrelator/]
- Crawford TD, Ndubaku CO, Chen H, Boggs JW, Bravo BJ, DeLaTorre K, et al. Discovery of Selective 4-Amino-pyridopyrimidine Inhibitors of MAP4K4 Using Fragment-Based Lead Identification and Optimization. J Med Chem. 2014;57:3484–93.View ArticleGoogle Scholar
- Giannetti AM. From experimental design to validated hits a comprehensive walk-through of fragment lead identification using surface plasmon resonance. Meth Enzymol. 2011;493:169–218.View ArticleGoogle Scholar
- Martin YC, Kofron JL, Traphagen LM. Do Structurally Similar Molecules Have Similar Biological Activity? J Med Chem. 2002;45:4350–8.View ArticleGoogle Scholar
- Wang L, Stanley M, Boggs JW, Crawford TD, Bravo BJ, Giannetti AM, et al. Fragment-based identification and optimization of a class of potent pyrrolo[2,1-f][1,2,4]triazine MAP4K4 inhibitors. Bioorg Med Chem Lett. 2014;18:4546–52.View ArticleGoogle Scholar
- Beresini MH, Liu Y, Dawes TD, Clark KR, Orren L, Schmidt S, et al. Small-Molecule Library Subset Screening as an Aid for Accelerating Lead Identification. J Biomol Screen. 2014;19:758–70.View ArticleGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.