This is a lecture delivered to students of Indian Agrcultural Research Institute, New Delhi

1. Chemoinformatics and Applications in Agrochemical Discovery C. Devakumar and Rajesh Kumar Division of Agricultural Chemicals IARI, New Delhi-110012 [email_address]

2. Chemical Space Virtual Real Mode “ Easy” Difficult Access 10 60 (?) 0 Virtual 10 7 10 22 Existing Small Molecules Stars

4. Cost to develop and time to market of various products

5. Registration of safer chemicals Proportion of pesticide active ingredients that are considered to be safer (biological chemicals and reduced-risk conventional chemicals) has steadily increased over the last several years. Source: EPA, 1999.

6. Plant biotechnology opens new markets / solutions

7. The development of the agrochemical in vivo screening

12. History of Chemoinformatics The first, and still the core, journal for the subject, the Journal of Chemical Documentation , started in 1961 (the name Changed to the Journal of Chemical Information and computer Science in 1975) The first book appeared in 1971 (Lynch, Harrison, Town and Ash, Computer Handling of Chemical Structure Information) The first international conference on the subject was held in 1973 at Noordwijkerhout and every three years since 1987

15. Why We Need Chemoinformatics? 1) An enormous amount of data and maintenance of data 2) Can we gain enough knowledge from the known data to make predictions for those cases where the required information is not available? 3) Relationships between the structure of a compound and its biological activity, or for the influence of reaction conditions on chemical reactivity.

16. Advances in theoretical and computational chemistry now allow chemists to model chemical compounds “in silico” with ever-increasing accuracy. Molecular properties now becoming accessible through computation include molecular shape, electronic structure, physical properties, chemical reactivity, protein folding, structures of materials and surfaces, catalytic activity, and biochemical activities.

17. integrates a comprehensive knowledge of chemistry with an extensive understanding of information technology . The intersection of chemistry and information technology embraces an expanding territory ; computational modeling of individual molecules, thermodynamic methods of estimating chemical properties, methods of predicting biological activity of hypothetical compounds, and organization and classification of chemical information. Chemoinformatics

18. Schematic representation of a crowded cell. An array of different molecules can function independently under extremely crowded conditions, partly because of judicious distributions of oppositely charged polar groups on the molecular surfaces. However, such systems are in some ways extremely fragile. For example, a mutation that alters just one amino acid in the haemoglobin molecule can stimulate massive aggregation and give rise to a fatal genetic disease, sickle-cell anaemia. More generally, many disorders of old age, most famously Alzheimer’s disease, result from the increasingly facile conversion of normally soluble proteins into intractable deposits that occur particularly as we get older Many of these aggregation processes involve the reversion of the unique biologically active forms of polypeptide chains into a generic and non-functional ‘chemical’ form

19. Additional computational challenges lie in indexing and classifying the infinite population of chemical compounds that could be synthesized or are already known. Specific indexing and search problems include how to find a compound that might block a specific biological target; how to predict the most efficient synthetic strategy for a desired compound from available precursors; how to employ results of bioactivity tests on a family of molecules to design improved versions;

21. Currently combinatorial chemists are developing new methods of synthesizing libraries of related compounds on an unprecedented scale. Such libraries can be used to produce huge arrays of materials for investigation of biochemical, catalytic, or material properties. Systems are required to design, catalog, and search these libraries, assess test results in a meaningful way, and integrate new information with existing chemical databases. Investigations into information storage at the molecular level are underway, bringing to full circle the link between chemistry and information technology.

22. The Scope of Chemoinformatics Representations and Structure Searching Substructure Searching Similarity Searching, Clustering, and Diversity Analysis Searching Databases Computer-aided Structure Elucidation 3D Substructure Searching QSAR and Docking

23. Structure and applications of chemoinformatics Database design and programming Representation and searching of chemical structures Structure, substructure & similarity searching in 2D & 3D Markush and reaction searching Representation and searching of biological databases chemoinformatics software Data analysis techniques Clustering; Evolutionary algorithms; Graph theory; Neural networks; Chemical information sources Cheminformatics applications Techniques used to design bioactive compounds Molecular simulation and design Drug discovery process; QSAR; Combi-chem; SBDD Spectroscopy and crystallography in cheminformatics

25. Accelrys -Large chemoinformatics company ACD/Labs - analytical informatics & predictions BCI - 2D fingerprinting, clustering toolkits & software Bioreason - HTS data analysis software Cambridgesoft - 2D drawing tools & E-notebooks CAS - produce Scifinder Scholar searching software ChemAxon - Java based toolkits and software Daylight- 2D representation & searching software Leadscope - 2D structure and property tools Lion Bioscience - produce LeadNavigator MDL - Large chemoinformatics company Openeye - Fast 3D docking, structure generation, toolkits Quantum Pharmaceuticals - prediction, docking, screening Sage Informatics - ChemTK 2D analysis software Tripos -Large chemoinformatics company Software Companies

26. Journal of Chemical Information and Computer Sciences Journal of Computer-Aided Molecular Design Journal of Molecular Graphics and Modelling Journal of Medicinal Chemistry NetSci (online journal) Scientific Computing World Bio-IT World Drug Discovery Today Journals & Magazines Newsletters, Mailing Lists & Other Hubs Chemical Informatics Letters- Monthly newsletter CHMINF-L (Indiana)- Email discussion list Chemoinf Yahoo Group -Email discussion list Chemistry Software Yahoo Group Cheminformatics.org Lots of links and QSAR datasets Reactive Reports Chemistry Web Magazine

30. Concord from Tripos, Inc. One of the first 3D structure generation programs, and is still being refined and developed. It generates single, minimal-energy structures from input 2D structures. The program can input and output a variety of file formats. http://www.tripos.com/sciTech/inSilicoDisc/chemInfo/concord.html Corina from the Gasteiger group. It is similar to Concord. http://www2.chemie.uni-erlangen.de/software/corina/free_struct.html Omega from OpenEye is the latest release. It offers very fast generation of multiple low-energy conformers. http://www.eyesopen.com/products/applications/omega.html 3D Structure generation and minimization

33. Chemistry Based Data Mining And Exploration Chemical(s) of concern Chemical Specific data Structural analogue Property analogue Biological or mechanistic analogue Data bases Data mining Structure searchable Structure activity relationships

41. Toxicity Prediction for chemical Q Global toxicity model Supporting information Toxicity prediction Hypothesis generation Analogue search Chemical class assignment Class based SAR model Weight of evidence of toxicity presentation Data collection Q

42. University of Barcelona, Spain University of Erlangen-Nürnberg, Germany Bioinformatics Institute Of India , Chandigarh Georgia Institute of Technology University of Sheffield (Willett) - MSc/PhD programs University of Erlangen (Gasteiger) UCSF (Kuntz) University of Texas (Pearlman) Yale (Jorgensen) University of Michigan (Crippen) Indiana University (Wiggins) - MSc program Cambridge Unilever (Glen, Goodman, Murray-Rust) Scripps - Molecular Graphics lab Institutes are Offering Courses on Chemoinformatics

44. QSAR STUDIES

45. DESIGN OF INSECTICIDE SYNERGISTS FURAPIOLE ANALOGUES log S F = 0.319 R M + 0.445σ R + 0.248B 1 + 0.034B4 - 0.966 n s r F 14 0.057 0.950 21.04

46. DESIGN OF INSECTICIDE SYNERGISTS SESAMOL ETHERS log S F = 0.153D 2 + 0.240D 1 - 1.711 σ I - 0.429R M + 0.070L - 0.384 n s r F 29 0.087 0.938 33.72

47. DILLAPIOLE SIDE CHAIN ANALOGUES log S F = n s r F 0.467 - 0.105 D - 1.537 R M 2 - 0.980σ R 17 0.046 0.948 38.84 0.305 - 0.1 I0 D - 1. I 14 R M 2 - 1.626 σ R + 0.012 B 4 17 0.045 0.955 31.37 0.071- 0.120 D - 0.619 R 2 M - 2.066 σ R + 0.080 B 4 - 0.003 L 2 17 0.045 0.958 24.86 0.053-0.134D - 0.216 R 2 M – 1.290 σ R + 0.135B 4 + 0.006L 2 - 0.67 σ I 17 0.046 0.961 20.30

48. DESIGN OF CHEMICAL HYBRIDISING AGENTS

49. Hybrid Technology Pollination Control system Male sterility

50. Male sterility Three - line Cytoplasmic Genetic Male Sterility Two - line Chemical Hybridising Agents

51. * p values (%) QSAR Equations for Ethyl Oxanilates Sl.No Equation (Ms = ) Statistics r s F 1 49.99F p – 2.39ΣMR + 64.73 0.7 14.10 11.75 (0.00)* 2 39.73 F p -3.24ΣMR +0.32 MW- 0.91 0.76 13.20 10.43 (0.02) 3 43.74F p – 3.04ΣMR +0.36 MW- 5.63D - 0.71 0.81 12.10 10.41 (0.01) 4 44.61F p – 2.93ΣMR +0.65MW- 5.78D +8.02ΣE s – 56.94 0.86 10.80 12.05 (0.00) 5 35.56F p – 2.96ΣMR +0.85MW- 4.94D +10.36ΣE s –10.00Σπ - 96.48 0.90 9.37 14.49 (0.00) 39.59 F p -2.86 ΣMR+0.67MW-5.11D-2.57ΣR-16.91 Σπ-64.28 0.91 3.73 16.99 J. Agri. Food Chem. 2003, 51, 992-998 n = 27

52. F Br CF 3 CN / / / Agrophore Group

53. QSAR equations for 2-pyridones analogues -3.00  MR + 49.50 F p – 7.87D + 211.67lnMw + 12.19  E s -6.87  E s(m) -1117.35 6.37 0.94 22.94 J. Agri. Food Chem. 2005, 53, 3468-3475 0.92 26 7.01 7.24 21.82 (0.00) n 0.93 -3.43  MR + 38.60 F p – 4.79D + 210.64lnMw + 10.42  E s -1113.06 19.74 (0.00) Equations (Ms =) Statistics s r F(p %) -2.34  MR + 64.45 F p -3.70R p – 4.69D + 71.49 26 9.34 0.85 14.13 (0.00) -3.21  MR + 57.18 F p -3.77R p – 5.38D + 93.98lnMw -459.06 26 7.77 0.91 18.38 (0.00) -3.32  MR+47.47F p -1.74R p –5.10D+173.39lnMw+7.05  E s -904.54 26

54. QSAR equations for N-acylanilines 67.54F P –1.67  R-6.59D + 0.13P + 15.37 9.56 0.86 16.97 J. Agri. Food Chem. 2005, 53, 5959-5968 Equations (Ms=) Statistics n r r 2 s F (Probability) 62.76F P – 1.66  R-6.39D + 43.38 29 0.81 0.65 10.79 15.65 (0.0000) 67.54F P –1.67  R-6.59D + 0.13P + 15.37 29 0.86 0.74 9.55 16.97 (0.0000)

55. Strategy of identifying new targets

56. Model organisms for target identification

57. Test systems for targets in UHTBS

58. UHTVS - Automated evaluation of activity of compounds

59. The virtual discovery cycle

60. Unique research platform – Network of complementary technologies to meet the challenges in compound discovery

61. Discovery of the target proteins of novel fungicides

62. De novo target discovery by functional genomics and the steps aiming to develop and perform high throughput biochemical tests

63. Gene expression profiling, a revolutionary tool in herbicide discovery Gene Expression Profiling (GEP) with DNA microarrays (chips) is a new technology used to measure changes in the entire transcriptome, i.e. full complement of active genes, of an organism in a single experiment. A catalogue of genetic fingerprints of the plant Arabidopsis thaliana , is created and each fingerprint being characteristic for a single herbicidal MoA is then used to rapidly classify herbicidal compounds from UHTVS according to their MoA. Helps to identify the affected metabolic pathway and the MoA of pro-drugs, which cannot be elucidated by conventional biochemical methods. GEP provides insight into the interactions of any herbicidal compound with the entire plant metabolism with unprecedented accuracy and completeness.

64. The principle of Gene Expression Profiling.

65. THANK YOU