D

Simple filter Simple molecular properties (e.g., mole- 103 to Is cular weight, molecular volume, element composition, etc.)

Complex filter Molecular properties that are more com- 0.1 to 10 s plicated to compute (basicity, acidity, drug-likeness, etc.)

Atom types and substructures in a binary representation Lipophilicity, pK,,-values

Fingerprints

Physico-chemical properties Substructure descriptors

Topological indices

Autocorrelation coefficients Quantum chemical descriptors Feature - feature distances

Three-dimensional pharmacophore patterns

Structure - activity relationship-

patterns

Autocorrelation coefficients

Virtual screening

Patterns of substructures (functional groups, atom types, chains, ring systems, etc.)

Topology, branching, general shape 0.1 to 10 s

General shape, distribution of atomic 1 to 100 s properties

Charge distribution in the molecules, 10 to 102 s molecular interaction fields

Representation of certain pharmaco- 1 to 102 s phore patterns

Target interaction sites (hydrogen bonds, 10 to 102 s electrostatic or lipophilic interactions, etc.). Similarities between different chemical classes and scaffolds can be recognized. Pharmacological activity 10tol02s

Similarity of shape and similarity of pos- 102 to 103 s sible interaction sites. Similarities between different chemical classes. Pharmacological activity 102 to 103 s

6. Descriptors based on the three-dimensional structure of molecules are applied when sets of structurally diverse compounds must be compared. The time needed for the calculations may exceed several minutes or even hours.

7. If the three-dimensional structure of the biological target (an enzyme or receptor that is to be affected by the hypothetical drug molecule) is known, it might be informative to test every structure of the library for a steric fit to this target. Such calculations take at least a few minutes for each structure and are therefore only feasible for relatively small virtual libraries.

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