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 A Am B Bn Total No of Compounds m*n ,000 compounds per experiment.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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