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Computational Approach for Combinatorial Library Design

Computational Approach for Combinatorial Library Design. Journal club-1 Sushil Kumar Singh IBAB, Bangalore. Chemical Synthesis of compounds. Traditional Synthesis A + B → AB(1 chemist=50 compounds/Year) Combinatorial Synthesis A1.......Am

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Computational Approach for Combinatorial Library Design

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  1. Computational Approach for Combinatorial Library Design Journal club-1 Sushil Kumar Singh IBAB, Bangalore

  2. Chemical Synthesis of compounds • Traditional Synthesis A + B →AB(1 chemist=50 compounds/Year) • Combinatorial Synthesis A1.......Am B1.......Bn Total No of Compounds m*n 1000 - 10,000 compounds per experiment.

  3. Challenges Do these new technology will lead to better drugs faster ? Can we make and test every thing ?So, Lead discovery and optimization is not a just game of numbers and it requires intelligent design choice.

  4. Library design method • Setting up library with maximum diversity. • Diverse libraries - • Lead generation libraries for screening against no of targets • A structurally diverse library should cover biological activity space as well.

  5. Requirements for measuring diversity Molecular descriptors to define structure space 1D- mw, log p(o/w) 2D- surface area, flexibility. 3D-Pharmacophore- collections of atoms/functional group & their orientations. Way to quantify the (dis)similarity of compounds. Subset selection algorithm to ensure full coverage of structural space.

  6. Descriptors selection and validation On comparing 2-D and 3-D descriptors, it was found that 2-D descriptors were more effective and more accurate for structure search. 2-D descriptors consist majority of substructures present in the molecule. 3-D pharmacophore encodes information about only three atoms or group at a time with limited no of conformations; but important for biological activity of molecule.

  7. Descriptors and chemical space While choosing descriptors • Avoid correlated descriptors. • Choose those which can add maximum biological activity to library. Every descriptor adds a dimension to chemical space. A large no of descriptors is often reduce to smaller no using PCA.

  8. Quantifications of (dis)similarity of compounds • Tanimoto coefficient= bc/(b1+b2 – bc) for 2-D molecular similarity by comparing bit strings. (e.g MDL information systems.) • Fingerprints like Daylight, ISIS etc. also compare 2-D similarity. • 3-D pharmacophore similarity also calculated by on the basis of bit string in 2D case.

  9. Other methods • Distance-based- • MaxMin: chooses points to maximize the smaller near-neighbor distance in design set. • Grid/Cell based • BCUT: Combination of 2-D and 3-D descriptor; commonly use in QSAR. • 3-D pharmacophore: maximizing diversity - rigid and flexible conformers with multiple energy.

  10. Lead optimization After library generation → Lead optimization -before optimizing a hit, do activity analysis of different regions of a molecule with small no. of individual molecule.

  11. Conclusion Library design depends • Design algorithm and • Property space So Comparing library is a difficult – different design with different property space. It requires • Combination of structural diversity calculations. • Experience. • Good medicinal chemical intuition.

  12. Thank You

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