Derivation of a scoring function for crystal structure prediction

The ever increasing number of experimentally resolved crystal structures su pports the possibility of fully empirical crystal structure prediction for small organic molecules. Empirical methods promise to be significantly more efficient than methods that attempt to solve the same problem from first p rinciples. However, the transformation from data to empirical knowledge and further to functional algorithms is not trivial and the usefulness of the result depends strongly on the quantity and the quality of the data. In thi s work, a simple scoring function is parameterized to discriminate between the correct structure and a set of decoys for a large number of different m olecular systems. The method is fully automatic and has the advantage that the complete scoring function is parametrized at once, leading to a self-co nsistent set of parameters. The obtained scoring function is tested on an i ndependent set of crystal structures taken from the P1 and P <(1)overbar>1 space groups. With the trained scoring function and FlexCryst, a program fo r small-molecule crystal structure prediction, it is shown that approximate ly 73% of the 239 tested molecules in space group P1 are predicted correctl y. For the more complex space group P <(1)overbar> 1, the success rate is 2 6%. Comparison with force-field potentials indicates the physical content o f the obtained scoring function, a result of direct importance for protein threading where such database-based potentials are being applied.