Derivation of a scoring function for crystal structure prediction

The ever increasing number of experimentally resolved crystal structures supports 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 principles. 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 this work, a simple scoring function is parameterized to discriminate between the correct structure and a set of decoys for a large number of different molecular systems. The method is fully automatic and has the advantage that the complete scoring function is parametrized at once, leading to a self-consistent set of parameters. The obtained scoring function is tested on an independent set of crystal structures taken from the P1 and P1; space groups. With the trained scoring function and FlexCryst, a program for small-molecule crystal structure prediction, it is shown that approximately 73% of the 239 tested molecules in space group P1 are predicted correctly. For the more complex space group P1;, the success rate is 26%. Comparison with force-field potentials indicates the physical content of the obtained scoring function, a result of direct importance for protein threading where such database-based potentials are being applied.