There is growing interest in RNA as a drug target due to its widespread involvement in biological processes. To exploit the power of structure-based drug-design approaches, novel scoring and docking tools need to be developed that can efficiently and reliably predict binding modes and binding affinities of RNA ligands. We report for the first time the development of a knowledge-based scoring function to predict RNA-ligand interactions (DrugScoreRNA). Based on the formalism of the DrugScore approach, distance-dependent pair potentials are derived from 670 crystallographically determined nucleic acid-ligand and -protein complexes. These potentials display quantitative differences compared to those of DrugScore (derived from protein-ligand complexes) and DrugScoreCSD (derived from small-molecule crystal data). When used as an objective function for docking 31 RNA-ligand complexes, DrugScoreRNA generates "good" binding geometries (rmsd (root mean-square deviation) < 2 A) in 42% of all cases on the first scoring rank. This is an improvement of 44% to 120% when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function. Encouragingly, good docking results are also obtained for a subset of 20 NMR structures not contained in the knowledge-base to derive the potentials. This clearly demonstrates the robustness of the potentials. Binding free energy landscapes generated by DrugScoreRNA show a pronounced funnel shape in almost 3/4 of all cases, indicating the reduced steepness of the knowledge-based potentials. Docking with DrugScoreRNA can thus be expected to converge fast to the global minimum. Finally, binding affinities were predicted for 15 RNA-ligand complexes with DrugScoreRNA. A fair correlation between experimental and computed values is found (RS = 0.61), which suffices to distinguish weak from strong binders, as is required in virtual screening applications. DrugScoreRNA again shows superior predictive power when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function.

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In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required. They can be provided by genetic algorithms, which mimic Darwinian evolution. GARLig (genetic algorithm using reagents to compose ligands) has been developed to perform subset selection in large chemical compound spaces subject to target-specific 3D-scoring criteria. GARLig uses different scoring schemes, such as AutoDock4 Score, GOLDScore, and DrugScore(CSD), as fitness functions. Its genetic parameters have been optimized to characterize combinatorial libraries with respect to the binding to various targets of pharmaceutical interest. A large tripeptide library of 20(3) members has been used to profile amino acid frequencies in putative substrates for trypsin, thrombin, factor Xa, and plasmin. A peptidomimetic scaffold assembled from a selection of a 25(3) building block was used to test the performance of the evolutionary algorithm in suggesting potent inhibitors of the enzyme cathepsin D. In a final case study, our program was used to characterize and rank a combinatorial drug-like library comprising 33,750 potential thrombin inhibitors. These case studies demonstrate that GARLig finds experimentally confirmed potent leads by processing a significantly smaller subset of the fully enumerated combinatorial library. Furthermore, the profiles of amino acids computed by the genetic algorithm match the observed amino acid frequencies found by screening peptide libraries in substrate cleavage assays.

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