An introduction to cheminformatics. Introduction, main concepts and algorithms. Jannis Apostolakis

Transkript

1 An introduction to cheminformatics Introduction, main concepts and algorithms Jannis Apostolakis LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 1

2 Ich bin Jannis Apostolakis Joannis.apostolakis at bio.ifi.lmu.de Zimmer 202 LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 2

3 Eintragen in die Liste Name, , Übung ja/nein, Semester, Master/Diplom, Fach (falls nicht Bioinformatik) LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 3

4 Nur der Schein zählt Vorlesung 11:00 14:00 Vorlesungsbegleitende Übung Do 12:00-14:00 Übung: Einige Übungen am Anfang die dann zu einem Projekt ausarten werden Das Projekt wird in Gruppen bearbeitet (2-3 Leute) Kurz vor Semesterende wird das Projekt vorgestellt und bewertet >>Zulassung zur Klausur Zum bestehen des Scheines zählt nur das Ergebnis der Klausur Änderung des Termins? LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 4

5 A few general remarks LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 5

6 What is cheminformatics about? A new name for an old game? encompasses the design, creation, organization, management, retrieval, analysis, disemination, visualization and use of chemical information AR Leach Introduction to cheminformatics, quoting Paris 2000 Development and application of methods and principles from computer science for problems in experimental chemistry Draws mainly from chemistry, computational science and modelling, and computer science Cheminformatics has evolved from the need to make more professional software for the analysis of data that come up in the chemical industry If we are going to fail anyway, we might as well be fast with it. LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 6

7 Motivation A target for therapy is not much use without the therapy itself Small molecule drugs vs. proteins/antibodies/genetic therapy Drug development Toxicity and clinical outcomes, pharmacokinetics Avoiding patents, scaffold hopping Pharmacogenomics Patient tailored medicine Structural genomics Structural metabolomics Chemical genomics Chemical space against target space LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 7

8 The drug development pipeline HTS Combi Chem Target identification Isolation of target Screening Synthesis Activity (Affinity) omics Expression Clinical studies Adme/Tox Animal studies Iterative refinement analysis LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 8

9 Cheminformatics in drug development Diversity analysis Combi Chem Clustering HTS Virtual screening Reaction databases QSAR Free energy Target identification Isolation of target Screening Synthesis Activity (Affinity) Docking omics Clinical studies Adme/Tox Animal studies Iterative refinement Expression analysis Bioinformatics and statistics ADMET Design Similarity search LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 9

10 Advantages and disadvantages of working on the computer No fear of big accidents Cheap Fast (getting faster by the minute) Well defined conditions Reproducible It is almost surely your own fault if it does not work Create impossible experimental conditions Molecular detail Every day in every way we are getting better and better Algorithms Accuracy vs. efficiency LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 10

11 Bioinformatics vs Cheminformatics (similarities) Bioinformatics Protein structure prediction Protein-Protein docking Ab initio methods (MC) Scoring functions Protein design Homology based modeling Structural alignment Microarray analysis Filtering of noisy data Clustering Machine learning Motif identification Protein motifs DNA signals Metabolomics, resistance, specificity, chemical genomics Cheminformatics Complex structure prediction Protein-Ligand docking Ab initio methods (MC) Scoring functions (statistical) Ligand design Homology based search Structural alignment HTS analysis Filtering of noisy data Clustering Machine learning Motif identification Combinatorial libraries Pharmacophore elucidation Metabolomics, resistance, specificity, chemical genomics LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 11

12 Bioinformatics vs Cheminformatics (differences) Bioinformatics Target finding for diagnosis and therapy of disease Target structure and function Cheminformatics Given the target, find the ligands Given the ligand find the drug Biological macromolecules are (long) sequences, built up from a small alphabet NAs, proteins Often modelled as strings Ligands need more complex representations (labeled graphs, geometry information) Often well defined algorithmic problems Often the problem lies with the modeling LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 12

13 What is this lecture about? Cheminformatics for molecular design Computer aided ligand design Drug design Problems, principles, algorithms, methods and data structures Own activities in cheminformatics Interfaces to bioinformatics? Discussion of our own methods What it s not about Quantum chemistry / Computational chemistry MD Software lab Software development Hard core algorithms LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 13

14 Contents of this lecture PART I: Introduction and general concepts Introduction (Cheminformatics and drug design) The drug development pipeline Models and data representation Physical and chemical background PART II: Target structure based methods Structure prediction/docking Structure based ligand design Scoring functions and prediction of binding affinities PART III: Target structure independent methods Molecular superposition and similarity QSAR Prediction of activities and ADME/Tox properties Regression and classification (ML) Inverse screening PART IV: No Targets and no leads Target identification Compound Databases Chemical spaces and their representation High Throughput Screening Other connections to bioinformatics Visualization LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 14

15 Methods discussed in this lecture Data structures Descriptors and representation Systematic search K-greedy Geometric hashing Optimization Gradient based methods Evolutionary algorithms/genetic Algorithms Monte Carlo, simulated annealing Branch and Bound Dynamic programming Graph theory Subgraph isomorphism Approximate tree matching Maximum Clique Statistics, Machine learning OLS, PCA, PLS LLE (locally linear embedding) Neural nets, GNN Feature selection/genetic function approximation LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 15

16 Overview for the next lecture(s) Historical perspective of medicinal chemistry Some facts about DI Drug development pipeline Paradigm shift in pharmaceutical research and the life sciences LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 16

17 An introduction to CI Introduction, main concepts and algorithms Jannis Apostolakis LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 17

18 Buying an expensive or tailor made suit Which one of you is it for? Would you please follow me? Test some suits off the rack Takes measures and sews Test the fit Suit Iterative refinement LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 18

19 Systematic search Algorithm: Systematic search Initialize: Make heap of possible sweaters While (heap is not empty) { Take a sweater from the heap wear (sweater) # call the wear algorithm Rank according to fit (sweater) # fit-ness } Return (sweater that fits best) LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 19

20 The BASFYNG/B problem Buy a sweater for your new boy/girlfriend Idea: borrow another sweater from that person Algorithm: While (heap is not empty) { Take a sweater from the heap Compare it to the borrowed sweater # size-wize Rank it according to similarity } Return (most similar sweater) LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 20

21 What can we do to support problem solving when I don t have the person or one of his/her clothes Clothes shops do that! Partitioning/Clustering LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 21

22 A new paradigm in clothes manufacture Ready made garments, in different sizes, colors etc. that can be combined with each other How does one find the best combination? 10 shirts x 5 pairs of trousers x 5 x jackets x 10 pairs of shoes 2500 combinations LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 22

23 Possible solutions of combinatorial optimization problems Greedy algorithm: Init: Start with the trousers and take the best While not fully dressed Continue with the next type of garment and take the best of its type that is compatible with what you are wearing Return (clothes you are wearing) LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 23

24 Possible solutions of combinatorial optimization problems Incompatibility problem: The best pair of trousers may not mach with the best shirt Backtracking (DFS) When you find there are no more choices at this level go one level higher and try the next best choice This way you can actually test all possible combinations Systematic search LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 24

25 Options tree and the DFS undressed shirt shirt shirt trousers trousers trousers trousers trousers trousers LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 25

26 Wrap up We have talked about: The drug development pipeline (Virtual) Screening (Receptor) structure based methods Statistical thermodynamics and scoring functions Molecular alignment and similarity analysis (Receptor) structure independent Partitioning methods No structure, and no known ligands Combinatorial chemistry and HTS Combinatorial optimization problems and algorithms And finally modeling LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 26

27 Buying an expensive or tailor made suit Which one of you is it for? Would you please follow me? Test some suits off the rack Takes measures and sews Test the fit Suit Iterative refinement LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 27

28 The drug development pipeline Target identification Isolation of target (HT) Screening Synthesis Affinity (activity) Clinical studies Adme/Tox Animal studies Iterative refinement LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 28

29 Receptor based methods Algorithm: Systematic search Initialize: Make heap of possible sweaters While (heap is not empty) { Take a sweater from the heap wear (sweater) # call the wear algorithm Rank according to fit (sweater) # fit-ness } Return (sweater that fits best) LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 29

30 Receptor free methods Buy a sweater for your new boy/girlfriend Idea: borrow another sweater from that person Algorithm: While (heap is not empty) { Take a sweater from the heap Compare it to the borrowed sweater # size-wize Rank it according to similarity } Return (most similar sweater) LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 30

31 No receptor no ligand What can we do to support problem solving when I don t have the person or one of his/her clothes Clothes shops do that! Partitioning/Clustering LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 31

32 Partitioning LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 32

33 Combinatorial chemistry Ready made garments, in different sizes, colors etc. that can be combined with each other How does one find the best combination? 10 shirts x 5 pairs of trousers x 5 x jackets x 10 pairs of shoes 2500 combinations LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 33

34 Wrap up We have talked about: The drug development pipeline (Virtual) Screening (Receptor) structure based methods Statistical thermodynamics and scoring functions Molecular alignment and similarity analysis (Receptor) structure independent Partitioning methods No structure, and no known ligands Combinatorial chemistry and HTS Combinatorial optimization problems and algorithms Part 1 Part 2 Part 3 Part 4 Algorithms And finally modeling LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 34

35 Termine? LMU Institut für Informatik, LFE Bioinformatik, Einführung in die Cheminformatik J. Apostolakis 35