EUGM 2013 - Dragos Horváth (Labooratoire de Chemoinformatique Univ Strasbourg-CNRS): Dealing with 'exotic' similarity metrics - live on the Web
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1) The document discusses methods for setting up similarity-driven virtual screening using various molecular similarity metrics and descriptor spaces. 2) It finds that traditional dogmas like only using Tanimoto similarity above 0.85 can be inaccurate, and recommends calibrating similarity cutoffs specifically for each target, query, and chemical space. 3) Tversky similarity with an alpha value of 0.7-0.9, which more heavily penalizes the query missing features of actives, is found to often give excellent results. The best approach is to test multiple options and calibrate for each individual virtual screening project.
EUGM 2013 - Dragos Horváth (Labooratoire de Chemoinformatique Univ Strasbourg-CNRS): Dealing with 'exotic' similarity metrics - live on the Web
- 1. Dealing with ‘exotic’ similarity metrics How to set up a (ChemAxon-powered) Similarity-driven Virtual Screening server… Dragos Horvath, dhorvath@unistra.fr UMR 7140 CNRS – Université de Strasbourg
- 2. Introduction & Definitions • Similarity-based Virtual Screening (SVS): – Search, in a database of candidates m for similar analogues of a query compound M of wanted properties, hoping that the « similarity principle » magic will operate. • Molecular Similarity S(M,m): – distance (metric) between the two Descriptor Space (DS) points 𝐷 𝑀 , 𝐷 𝑚 - let us call these 𝐷, 𝑑, for simplicity. • Similarity Radius s defines « how similar is similar » – Delimits a sphere in descriptor space around M, thought to contain a minimum of inactive, but a maximum of active m. • Virtual Hits – aka True & False « Positives » (TP,FP): – Compounds m with S(M,m)<s
- 3. Compound Sets • For server calibration: – Candidate database: 165 ChEMBL ligand sets with >50 molecules of reported pKi values with respect to the 165 associated receptors & enzymes (targets T). – Queries of T: MT 1, MT 2 … MT i, i=1..QT is composed of the top 1/5 (max 100) actives on T, plus 1/5 (max 100) of binders of medium potency, can be classified by pharmacophore complexity (Nr. of populated FPT1 triplets) – 10,000 randomly picked commercial molecules from ZINC, assumed to be inactive “decoys”. • Operational database: – 1.5 M commercial compounds, from various sources – Above « reference » molecules, for annotation purposes
- 8. All are Feature Counts Di(M) = integer (positive or null) population level of « feature » i (a substructure or a pharmacophore triplet) in molecule M Descriptor Spaces
- 9. Dissimilarity Scores… • Based on the comparison of descriptor vectors 𝐷, 𝑑 𝑁 𝑀 𝑁𝑂𝑅𝑀(𝑀) = 𝐷𝑖 2 𝑁(𝑀) 𝑖=1 𝑁𝐴𝑁𝐷 𝑚, 𝑀 𝐴𝑁𝐷(𝑚, 𝑀) = 𝐷𝑖 × 𝑑𝑖 𝑁 𝑂𝑅(𝑚,𝑀) 𝑖=1 𝑁𝐸𝑋𝐶 𝑀, 𝑚 𝐸𝑋𝐶(𝑀, 𝑚) = 𝐷𝑖 2 𝑖|𝑑 𝑖=0
- 10. Euclidean & Related… 𝐸 𝑚, 𝑀 = 𝐷𝑖 − 𝑑𝑖 2 𝑁 𝑂𝑅(𝑚,𝑀) 𝑖=1 𝑅 𝑚, 𝑀 = 𝐷𝑖 − 𝑑𝑖 2𝑁 𝑂𝑅(𝑚,𝑀) 𝑖=1 𝑁 𝑂𝑅(𝑚, 𝑀) 𝐴 𝑚, 𝑀 = 𝐷𝑖 − 𝑑𝑖 𝑁 𝑂𝑅(𝑚,𝑀) 𝑖=1 𝑁 𝑂𝑅(𝑚, 𝑀) 𝑅𝑊 𝑚, 𝑀 = 𝑅 𝑚, 𝑀 𝑁 𝐸𝑋𝐶(𝑚, 𝑀) + 𝑁𝐸𝑋𝐶(𝑀, 𝑚) 𝑁 𝑂𝑅(𝑚, 𝑀) 𝐴𝑊 𝑚, 𝑀 = 𝐴 𝑚, 𝑀 𝑁 𝐸𝑋𝐶(𝑚, 𝑀) + 𝑁𝐸𝑋𝐶(𝑀, 𝑚) 𝑁 𝑂𝑅(𝑚, 𝑀)
- 11. (A)Symmetric Correlation Scores – Tanimoto & Tversky 𝑇 𝑀, 𝑚 = 1 − 𝐴𝑁𝐷(𝑀, 𝑚) 𝑁𝑂𝑅𝑀(𝑀) + 𝑁𝑂𝑅𝑀(𝑚) − 𝐴𝑁𝐷(𝑀, 𝑚) 𝑇𝑣 𝑀, 𝑚, 𝛼 = 1 − 𝐴𝑁𝐷(𝑀, 𝑚) 𝛼𝐸𝑋𝐶(𝑀, 𝑚) + 1 − 𝛼 𝐸𝑋𝐶(𝑚, 𝑀) + 𝐴𝑁𝐷(𝑀, 𝑚) Situations where: (a) candidate m misses a feature seen in active M, and (b) it contains some novel feature not seen in M may be distinguished! At a>0.5, cases (a) will be relatively more penalized than the symmetric situation (b). A raw guess of a should suffice! Three implementations of Tv are considered: • Tv+ (a=0.9) • Tv (a=0.7) • Tv- (a=0.3)
- 12. 2. Fine, but « how similar is similar »? • You may be a believer of the dogma « Tanimoto>0.85 » (T<0.15) – But the Bible mentions not the other metrics, less subjected to religious fervor. • Alternatively, try to infer reasonable choices of similarity radii for each Chemical Space (CS – the combination of Descriptor Space & Similarity score) – For each query, on every target, compute s* corresponding to the « optimal » SVS scenario. – This also allows to measure & benchmark SVS success with respect to its Operational Premises (CS, nature of Target, degree of complexity of the query, etc).
- 13. s W 1.0 )()( )()( )( E FN E FP FNFP NN NN s W SS S A basic SVS Optimality Criterion: W L(M,m) l L(M,m)> l S(M,m)s True Positives (TP) False Positives (FP) False (?) Negatives (FN) True Negatives (TN) )()( )()( )( E FN E FP FNFP NN NN s W SS S s Activity (profile) differences L(m,M) Λ 𝑀, 𝑚 = 0 𝑖𝑓 𝑝𝐾𝑖(𝑀) − 𝑝𝐾𝑖(𝑚) < 0.5 1 𝑖𝑓 𝑝𝐾𝑖(𝑀) − 𝑝𝐾𝑖(𝑚) > 3.0 𝑝𝐾𝑖(𝑀) − 𝑝𝐾𝑖(𝑚) − 0.5 2.5 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
- 14. s W 1.0 )()( )()( )( E FN E FP FNFP NN NN s W SS S A basic SVS Optimality Criterion: W L(M,m) l L(M,m)> l S(M,m)s True Positives (TP) False Positives (FP) False (?) Negatives (FN) True Negatives (TN) )()( )()( )( E FN E FP FNFP NN NN s W SS S s Activity (profile) differences L(m,M)
- 15. The Ascertained Optimality Excess X Compound Pairs selected at cutoff s Random S values Meaningful S values Var(W)W X Fraction of Compound Pairs selected at cutoff s sVars randrand WWWX X
- 16. Workflow ForEach Target T Set database db=set of tested ligands (known pKi ) + decoy set (pKi=0); ForEach Query M of T ForEach DescriptorSpace D ForEach SimilarityScore S # Start Current SVS experiment defined by Target, Query, Descriptors & Similarity Score ForEach m!=M in db Calculate S(M,m)|D ; EndLoop(m) Scan over s → X(s) and return s* such that X(s*)=maximal; Classify SVS(T,M,D,S) wrt X(s*) as « failed », « acceptable », « good » or « excellent » ; EndLoop S; EndLoop D; EndLoop M; EndLoop T; Analyze Success Rates & s* distributions in terms of various Operational premises (nature of T, complexity of M, choice of D, of S or of D-S combinations)
- 17. Insights: (1) – So much for dogmas! 0 5 10 15 20 25 30 35 40 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 0.44 0.48 0.52 0.56 0.6 %ofTanimoto-basedqueriesofGoodoptimalitylevelatd* d* FPT1 treeSY03 s* Use distribution to « teach » the web server how to rank prospective SVS hits! Top Hits (0) Good Hits (1) Average Hits (2) Acceptable Hits (3) Are these Hits? (4) Ignore… Top Hits (0) Good Hits (1) Average Hits (2) Acceptable Hits (3) Are these Hits? (4) Ignore…
- 18. Insights: (2) – Tversky at a>0.5: an excellent similarity scoring scheme. 0 2 4 6 8 10 12 14 16 Tv+ Tv T RW AW Tv- E A R R:all-acceptable R:all-good R:all-excellent Relative«marketshare»ofmetric:fractionofSVSrunsbasedonshown metric,outofallSVSexperimentshavingreachedgivensuccesslevels
- 19. Tv+ may pick actives that are more complex than queries (NK1 example) T
- 20. Insights: (3) – Trends with respect to target classes could be evidenced… Relative«marketshare»ofmetric:fractionofSVSruns–withintargetclasses-based onshownmetric,outofallSVSexperimentshavingreachedgivensuccesslevels 0 2 4 6 8 10 12 14 16 Tv+ Tv T RW AW Tv- E A R R:all-good R:Kinases-good R:monoamineGPCR-good R:otherGPCR-good
- 21. Insights: (4) – when the query compound is complex, the metric matters less Relative«marketshare»ofmetric:fractionofSVSruns–withinquerycomplexity classes-basedonshownmetric,outofallSVShavingreachedgivensuccesslevels 0 2 4 6 8 10 12 14 16 Tv+ Tv T RW AW Tv- E A R R:all-good R:pharm-high-good R:pharm-low-good
- 22. Some conclusions • The study has highlighted many interesting aspects – Intrinsic usefulness of Tversky scores biased towards of query feature loss penalty: a=0.9…0.7 will do! – Other target-, query complexity-, query activity-, descriptor space-dependent trends of the SVS success – Some inevitable sources of bias, showing that not even ChEMBL is not large/diverse enough to cover it all… • Main message: use this protocol – or related – to calibrate web servers, rather than sticking to well-studied metrics and descriptors for which « Universal » similarity cutoffs are believed to hold. • Try infochim.u-strasbg.fr/webserv/VSEngine.html – to our knowledge, the only public SVS server to support atypical, but powerful metrics coupled to chemically relevant, pH-sensitive descriptor spaces… all while exploiting the power of ChemAxon tools!