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![Page 1: Leiden University. The university to discover. Enhancing Search Space Diversity in Multi-Objective Evolutionary Drug Molecule Design using Niching 1. Leiden.](https://reader036.fdocuments.net/reader036/viewer/2022070417/56649e495503460f94b3ca12/html5/thumbnails/1.jpg)
Leiden University. The university to discover.
Enhancing Search Space Diversity in Multi-Objective Evolutionary Drug Molecule Design using Niching
1. Leiden Institute of Advanced Computer Science (LIACS)2. Leiden/Amsterdam Center for Drug Research (LACDR)3. NuTech Solutions, Inc.
A. Aleman1
A.P. IJzerman2
E. van der Horst2
M.T.M Emmerich1
T. Bäck1,3
J.W. Kruisselbrink1
A. Bender2
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Leiden University. The university to discover.
- Search for molecular structures with specific pharmacological or biological activity that influence the behavior of certain targeted cells
- Objectives: Maximization of potency of drug (and minimization of side-effects)
- Constraints: Stability, synthesizability, drug-likeness, etc.- A huge search space: 1020-1060 drug-like molecules- Aim: provide the medicinal chemist a set of molecular
structures that can be promising candidates for further research
Scope: drug design and development
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Leiden University. The university to discover.
Molecule Evolution
Fragments extracted fromFrom Drug Databases
While not terminate do
Generate offspring O from PPt+1= select from (P U O)
Evaluate O
Initialize population P0
- ‘Normal’ evolution cycle- Graph based mutation and
recombination operators- Deterministic elitistic (μ+λ)
parent selection (NSGA-II)
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Leiden University. The university to discover.
Molecule Evolution
![Page 5: Leiden University. The university to discover. Enhancing Search Space Diversity in Multi-Objective Evolutionary Drug Molecule Design using Niching 1. Leiden.](https://reader036.fdocuments.net/reader036/viewer/2022070417/56649e495503460f94b3ca12/html5/thumbnails/5.jpg)
Leiden University. The university to discover.
Fitness
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:- a fuzzy constraint score based on Lipinski’s rule of five and bounds
on the minimal energy confirmation:
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Leiden University. The university to discover.
Desirability indexes for modeling fuzzy constraints
The degree of satisfaction can be measured on a scale between 0 and 1Constraints can be modeled in the form of desirability values
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Leiden University. The university to discover.
Diversity for Molecule Evolution
- A ‘normal’ search yields very similar molecular structures- Aim for a set of diverse candidate structures because:
- Vague objective functions may result in finding structures that fail in practice
- The chemist desires a set of promising structures rather than only one single solution
- Explicit methods are required to enforce diversity in the search space; i.e. niching
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Leiden University. The university to discover.
All molecules are variations of the same theme!
Typical output of a ‘normal’ evolutionary search
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Leiden University. The university to discover.
Niching in Multi-Objective EA- Explicitly aim for diversity in the decision space- Different than aiming for diversity in the objective space- Points that lie far apart in the objective space do not
necessarily also lie far apart in the decision space
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Leiden University. The university to discover.
Niching-based NSGA-II
A Niching-based NSGA-II algorithm as proposed by Shir et al.
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Leiden University. The university to discover.
Dynamic Niche Identification
Peak individuals
q=3 Individuals that do not belong to niche
B.L. Miller, Shaw, M.J.: Genetic algorithms with dynamic niche sharing for multimodal function optimization, Proceedings of IEEE International Conference on EC, May 1996, Pages: 786-791
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Leiden University. The university to discover.
Similarity in Molecular Spaces
- Molecules are represented by bitstrings identifying certain structural properties
- A ‘1’ at position i denotes the presence of property i in the molecule, and ‘0’ at position i denotes the absence of property i
- How to define a similarity measure for the graph-like molecular structures?
- Idea: use molecular fingerprints
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Leiden University. The university to discover.
Distance based on fingerprints
- The distance between two molecules A and B can be based on the four terms:
- a: the number of properties only present in A
- b: the number of properties only present in B
- c: the number of properties present in both A and B
- d: the number of properties not present in A and B
- One possible distance measure can be created using the Jaccard coefficient (also known as Tanimoto coefficient):
The Jaccard distance fullfills the triangular equation, as opposed to for example the cosine-distance!
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Leiden University. The university to discover.
Triangle inequality
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Leiden University. The university to discover.
Triangle inequality
Why do we want to have a dissimilarity (distance) measure that obeys the triangle inequality?
If we have very similar molecules, say molecule A is similar to B and molecule A is also similar to C,
then we want to be able to say that B is similar to C.
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Leiden University. The university to discover.
Triangle inequality
![Page 17: Leiden University. The university to discover. Enhancing Search Space Diversity in Multi-Objective Evolutionary Drug Molecule Design using Niching 1. Leiden.](https://reader036.fdocuments.net/reader036/viewer/2022070417/56649e495503460f94b3ca12/html5/thumbnails/17.jpg)
Leiden University. The university to discover.
Molecule Evolution with Niching
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Leiden University. The university to discover.
ExperimentsAim:
Compare the niching-based NSGA-II method with the normal NSGA-II method
Two test-cases:- Find ligands for the Neuropeptide Y2 receptor (NPY2)- Find inhibitors for the Lipoxygenase (LOX)
Two objectives:- Aggregated fitness score based on activity predictors - Aggregated constraints score function
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Leiden University. The university to discover.
Experimental setup- 5 runs for each method on each test-case- 1000 generations per runs- Normal NSGA-II:
- 50 parents- 150 offspring
- Niching-based NSGA-II:- 10 niches- 5 parents per niche- 150 offspring- niche radius set to 0.85 (empirically set)
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Leiden University. The university to discover.
Average Pareto Fronts
NPY2:
LOX:
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Leiden University. The university to discover.
Average distance between the individuals in the final populations
NPY2:
LOX:
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Leiden University. The university to discover.
Output sets of a NPY2 run without and with niching
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Leiden University. The university to discover.
Output sets of a LOX run without and with niching
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Leiden University. The university to discover.
Multi-dimensional Scaling Plots
No Niching Niching
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Leiden University. The university to discover.
The chemist’s view on the output
Regarding the niching:- The molecules found with the niching method are clearly
more diverse than the molecules found by the non-niching approach
In general:- The molecules look reasonable overall, but:
- Most molecules still possess unstable and/or toxic features that are not easy to synthesize in practice
- Similar types of uncommon features seem to appear
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Leiden University. The university to discover.
Conclusions and OutlookConclusions:- Applying niching using the Jaccard distance based on
molecular fingerprints and is a way to enhance search space diversity in molecule evolution
- It yields more diverse sets of molecules than a normal evolutionary algorithm for molecule evolution
Future research:- Applying these methods on other (more sophisticated)
models as well- In vitro testing of selected molecules found using this
method- Incorporate more sophisticated measures for testing the
synthesizability of candidate molecules
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Leiden University. The university to discover.
Thank you!
Alexander AlemanNatural Computing GroupLIACS, Universiteit Leidene-mail: [email protected]