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