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Leiden University. The university to discover. Desirability Indexes for Soft Constraint Modeling in Drug Design Johannes Kruisselbrink

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Presentation on theme: "Leiden University. The university to discover. Desirability Indexes for Soft Constraint Modeling in Drug Design Johannes Kruisselbrink"— Presentation transcript:

1 Leiden University. The university to discover. Desirability Indexes for Soft Constraint Modeling in Drug Design Johannes Kruisselbrink

2 Leiden University. The university to discover. Scope Context: -Quality measures for candidate molecular structures for automated optimization Contents: -Using the concept of Desirability for modeling soft or fuzzy constraints -The applicability in automated drug design and examples for integration within a scoring function

3 Leiden University. The university to discover. Uncertainty and noise can arise in various parts of the optimization model: Uncertainty and noise in optimization problems Input X External (uncontrollable) parameters A Output Y System (Model) GOALSGOALS f 1  max / min f 2  max / min | f m  max / min g 1 ≤ 0 g 2 ≤ 0 | g n ≤ 0 Objectives Constraints A) Uncertainty and noise in the design variables B) Uncertainty and noise environmental parameters C) Uncertain and/or noisy system output D) Vagueness / fuzziness in the constraints

4 Leiden University. The university to discover. Our setup for Automated Molecule Evolution

5 Leiden University. The university to discover. Automated molecule design -Search for molecular structures with specific pharmacological or biological activity -Objectives: Maximization of potency of drug (and minimization of side-effects) -Constraints: Stability, synthesizability, drug- likeness, etc. -Aim: provide a set of molecular structures that can be promising candidates for further research

6 Leiden University. The university to discover. Molecule Evolution Fragments extracted from From Drug Databases While not terminate do Generate offspring O from P P t+1 = select from (P U O) Evaluate O Initialize population P 0 -‘Normal’ evolution cycle -Graph based mutation and recombination operators -Deterministic elitist (μ+λ) parent selection (NSGA-II with Niching) “The molecule evoluator. An interactive evolutionary algorithm for the design of drug-like molecules.“, E.-W. Lameijer, J.N. Kok, T. Bäck, A.P. IJzerman, J. Chem. Inf. Model., 2006, 46(2):

7 Leiden University. The university to discover. Objectives and constraints Objectives -Activity predictors based on support vector machines: -f1: activity predictor based on ECFP6 fingerprints -f2: activity predictor based on AlogP2 Estate Counts -f3: activity predictor based on MDL Constraints -Bounds based on Lipinski’s rule of five and the minimal energy confirmation: -Number of Hydrogen acceptors -Number of Hydrogen donors -Molecular solubility -Molecular weight -AlogP value -Minimized energy

8 Leiden University. The university to discover. Soft constraints in drug design

9 Leiden University. The university to discover. Soft constraints in Drug Design -Estimating the feasibility of candidate structures can be done using boundary values for certain molecule properties -Examples are Lipinski’s rule-of-five and estimations of the minimal energy conformations -But…, how strict are those rules? -Sometimes violations are easy to fix manually -Sometimes violations are not violations in practice

10 Leiden University. The university to discover. Molecules failing Lipinski Atorvastatin Liothyronine Ethopropazine Olmesartan Doxycycline Bexarotene Acarbose MW MW / HA log P (5.088) HA / HD MW / HA

11 Leiden University. The university to discover. Modeling constraints using desirability functions

12 Leiden University. The university to discover. The real nature of the constraints The constraints are of the following forms: Where -x denotes a candidate structure -g(x) denotes the property value of x -A j is the lower bound of the property filter -B j is the upper bound of the property filter - reads: A is preferred to be smaller than B

13 Leiden University. The university to discover. Modeling constraints as objectives Constraints can be transformed into ‘objectives’ by mapping their values onto a function with the domain where: -Values close to 0 correspond to undesirable results -Values close to 1 correspond to desirable results -Values between 0 and 1 fall into the grey area 1 0 violated satisfiedgrey area 1 0 violatedsatisfiedgrey area One-sided Two-sided There are multiple ways to create such mappings! Cutoff bound Constraint bound

14 Leiden University. The university to discover. Constraints in our studies Fuzzy constraint scores based on Lipinski’s rule of five and bounds on the minimal energy confirmation: DescriptorLBABUB Num H-acceptors01610 Num H-donors0135 Molecular solubility-6-4NA Molecular weight ALogP0145 Minimized energyNA * Bounds settings were determined based on chemical intuition

15 Leiden University. The university to discover. Harrington Desirability Functions One-sided:Two-sided:

16 Leiden University. The university to discover. Example one-sided Harrington DF Molecular solubility: -Soft constraint: Y > -4 -Absolute cutoff: Y < -6 violatedgrey areasatisfied

17 Leiden University. The university to discover. Example two-sided Harrington DF Molecular weight: -Absolute lower cutoff: Y < 150 -Lower bound constraint: Y > 250 -Upper bound constraint: Y < 450 -Absolute upper cutoff: Y > 600 Problematic! -No support for non-symmetric boundaries -No explicit support for ‘completely satisfied’ intervals

18 Leiden University. The university to discover. violated grey area satisfied violatedgrey area Example two-sided Harrington DF One possibility: -Make symmetric -Base d(Y) on cutoff bounds -Tune n using a constraint bound

19 Leiden University. The university to discover. Example two-sided Harrington DF Or: -Make symmetric -Base d(Y) on constraint bounds -Tune n using a cutoff bound violated grey area satisfied violatedgrey area

20 Leiden University. The university to discover. violated grey area satisfied violatedgrey area Example two-sided Harrington DF Or: -Make symmetric -Base d(Y) on average between constraint bounds and cutoff bounds -Tune n using a cutoff bound

21 Leiden University. The university to discover. Harrington -Advantages: -Maps onto a continuous function -Strictly monotonous mapping -Distinction between completely violated points -Downsides: -Tuning the DF is somewhat arbitrary -Distinction between completely satisfied solutions -Not really suited for ‘completely satisfied intervals’ -Does not allow non-symmetric constraints

22 Leiden University. The university to discover. Derringer Desirability Functions One-sided:Two-sided:

23 Leiden University. The university to discover. violatedgrey areasatisfied Example one-sided Derringer DF Molecular solubility: -Soft constraint: Y > -4 -Absolute cutoff: Y < -6 Note: l=1  linear

24 Leiden University. The university to discover. Example two-sided Derringer DF Molecular weight: -Absolute cutoff: Y < 150 -Soft constraint: Y > 250 -Soft constraint: Y < 450 -Absolute cutoff: Y > 600 violated grey area satisfied violated grey area

25 Leiden University. The university to discover. Derringer -Advantages: -Easy straightforward implementation -Control for modeling non-symmetric constraints -Easy integration for ‘completely satisfied’ intervals -No distinction between completely satisfied solutions -Downsides: -Maps onto a discontinuous function -Not strictly monotonous (just monotonous) -No distinction between solutions after lower cutoff

26 Leiden University. The university to discover. Aggregating the Desirability Functions into score functions

27 Leiden University. The university to discover. Many objective optimization -Modeling fuzzy constraints using DFs generates many additional objective functions -In our case: -3 original objectives + 6 constraints  9 objectives -The possibilities: -Pareto optimization -Aggregation -A combination of the both

28 Leiden University. The university to discover. Aggregation -Desirability functions can be easily integrated into one single scoring function, e.g.: -Weighted sum -Min performance -Geometrical mean -Average The Desirability Index

29 Leiden University. The university to discover. Remodeling the objectives -Desirability index aggregation of the objectives requires a normalization function that maps the objective function values to the interval [0,1] -One possibility: -Or…, use Harrington or Derringer DFs Original objective function  minimization

30 Leiden University. The university to discover. The aggregation possibilities -Full aggregation: -Aggregate the constraints and the objectives into one quality score (1 objective) -Partial aggregation: -Aggregate the constraints into one constraint score (1 extra objective  4 objectives) -Aggregate the constraints and the objectives into two separate scoring function (2 objectives)

31 Leiden University. The university to discover. A case study

32 Leiden University. The university to discover. Experiments Comparison of: -Complete aggregation (1 objective) -Separate aggregation of objectives and constraints (2 objectives) -Only aggregate constraint scores (4 objectives) Objectives: -three activity prediction models for estrogen receptor antagonists EA settings: -NSGA-II for the multi-objective test-cases -80 parents / 120 offspring generations -No niching

33 Leiden University. The university to discover. 4D Pareto fronts The Pareto fronts obtained using three different scoring methods Optimization direction Complete aggregation (1 objective) Only aggregate constraint scores (4 objectives) Aggregate constraints and objectives separately (2 objectives)

34 Leiden University. The university to discover. Random subsets of the results

35 Leiden University. The university to discover. Separate constraints and objectives Color: constraint scores (white = 0  black = 1) f 3 : MDL  max (=1) f 2 : ECFP  max (=1) f 1 : AlogP2 EC  max (=1) Tamoxifen

36 Leiden University. The university to discover. Conclusions

37 Leiden University. The university to discover. Discussion - Ranking issues -DFs that can yield 0 values will generate 0 values for the performance when aggregating using the geometric mean -DFs that make distinctions between completely satisfied constraints might be involved in unnecessary further optimization (maximization while already satisfied) 1 0 violated satisfiedgrey area An ideal DF? Never 0 (distinction on the degree of constraint) When satisfied 1 (no distinction between satisfied regions)

38 Leiden University. The university to discover. Conclusions -Desirability Functions and Desirability Indexes for modeling soft / fuzzy constraints: -Are intuitive and easy to incorporate -Allow for easy integration of additional constraints -Incorporate the concept of vagueness present in all rule-of-thumb measures -Prevent the optimization method from ruling out promising candate structures

39 Leiden University. The university to discover. Thank you! Johannes Kruisselbrink Natural Computing Group LIACS, Universiteit Leiden

40 Leiden University. The university to discover. Matlab codes (no presentation stuff, just for creating the DF plots)

41 Leiden University. The university to discover. Harrington one-sided example clf x = [0:.1:10]; y = exp(-exp(-( * x))); plot(x, y) ylim([ ]) xlabel('Y') ylabel('d(Y)')

42 Leiden University. The university to discover. Harrington two-sided example clf x = [0:.01:10]; y = exp(-abs((2 * x - (6 + 4))/(6 - 4)).^(3)); plot(x, y) ylim([ ]) xlabel('Y') ylabel('d(Y)')

43 Leiden University. The university to discover. One-sided Harrington DF in MATLAB clf x = [-8:.1:-2]; y = exp(-exp(-( * x))); plot(x, y) hold on plot([ ],[ ], '-.r') ylim([ ]) xlabel('Y') ylabel('d(Y)') legend('Harrington DF', 'Linear DF', 'Location', 'NorthWest')

44 Leiden University. The university to discover. Two-sided Harrington DF 1 in MATLAB clf x = [0:1:800]; y = exp(-abs((2 * x - ( ))/( )).^(7.8273)); plot(x, y) hold on plot([ ], [ ], '-.r') ylim([ ]) xlabel('Y') ylabel('d(Y)') legend('Harrington DF', 'Linear DF', 'Location', 'NorthEast')

45 Leiden University. The university to discover. Two-sided Harrington DF 2 in MATLAB clf x = [0:1:800]; y = exp(-abs((2 * x - ( ))/( )).^(2.2033)); plot(x, y) hold on plot([ ], [ ], '-.r') ylim([ ]) xlabel('Y') ylabel('d(Y)') legend('Harrington DF', 'Linear DF', 'Location', 'NorthEast')

46 Leiden University. The university to discover. Two-sided Harrington DF 3 in MATLAB clf x = [0:1:800]; y = exp(-abs((2 * x - ( ))/( )).^(5.6927)); plot(x, y) hold on plot([ ], [ ], '-.r') ylim([ ]) xlabel('Y') ylabel('d(Y)') legend('Harrington DF', 'Linear DF', 'Location', 'NorthEast')

47 Leiden University. The university to discover. One-sided Derringer DF in MATLAB clf hold on x = [-8:.01:-2]; y1 = (x >= -4) * 1 + (x = -6).* ((x + 6)/(-4 + 6)).^0.5; plot(x, y1, '-.b') y2 = (x >= -4) * 1 + (x = -6).* ((x + 6)/(-4 + 6)).^1; plot(x, y2, '--r') y3 = (x >= -4) * 1 + (x = -6).* ((x + 6)/(-4 + 6)).^2; plot(x, y3, 'g') ylim([ ]) xlabel('Y') ylabel('d(Y)') legend('Derringer DF (l=0.5)', 'Derringer DF (l=1)', 'Derringer DF (l=2)', 'Location', 'NorthWest')

48 Leiden University. The university to discover. Two-sided Derringer DF in MATLAB clf hold on x = [0:.1:800]; y1 = (x >= 150).* (x = 250).* (x 450).* (x <= 600).* ((x - 600) / ( )).^(0.5); plot(x, y1, '-.b') y2 = (x >= 150).* (x = 250).* (x 450).* (x <= 600).* ((x - 600) / ( )).^(1); plot(x, y2, '--r') y3 = (x >= 150).* (x = 250).* (x 450).* (x <= 600).* ((x - 600) / ( )).^(2); plot(x, y3, 'g') ylim([ ]) xlabel('Y') ylabel('d(Y)') legend('Derringer DF (l=0.5)', 'Derringer DF (l=1)', 'Derringer DF (l=2)', 'Location', 'NorthEast')


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