{'fraction_non_alphanumeric': 0.01791044776119403, 'fraction_numerical': 0.003917910447761194, 'mean_word_length': 5.846743295019157, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Virtual compound screening and SAR analysis: method development and practical applications in the design of new serine and cysteine protease inhibitors

Virtual screening is an important tool in drug discovery that uses different computational methods to screen chemical databases for the identification of possible drug candidates. Most virtual screening methodologies are knowledge driven where the availability of information on either the nature of the target binding pocket or the type of ligand that is expect to bind is essential. In this regard, the information contained in X-ray crystal structures of protein-ligand complexes provides a detailed insight into the interactions between the protein and the ligand and opens the opportunity for further understanding of drug action and structure activity relationships at molecular level. Protein-ligand interaction information can be utilized to introduce target-specific interactionbased constraints in the design of focused combinatorial libraries. Furthermore, such information can also be directly transformed into structural interaction fingerprints and can be applied in virtual screening to analyze docking studies or filter compounds. However, the integration of protein-ligand interaction information into two-dimensional compound similarity searching is not fully explored. Therefore, novel methods are still required to efficiently utilize protein-ligand interaction information in two-dimensional ligand similarity searching. Furthermore, application of protein-ligand interaction information in the interpretation of SARs at the ligand level needs further exploration. Thus, utilization of three-dimensional protein ligand interaction information in virtual screening and SAR analysis was the major aim of this thesis. The thesis is presented in two major parts. In the first part, utilization of three-dimensional protein-ligand interaction information for the development of a new hybrid virtual screening method and analysis of the nature of SARs in analog series at molecular level is presented. A new virtual screening hybrid methodology, termed the interaction annotated structural features, was introduced that assigns energy-based scores to two-dimensional substructures based on three-dimensional protein-ligand interaction information and utilize interaction-annotated features in virtual screening. Database molecules containing annotated fragments were assigned cumulative scores that serve as a measure of similarity to active reference compounds. In benchmark calculations on different high-throughput screening i data sets, the hybrid approach mostly performed better than conventional fragment-based two-dimensional fingerprint similarity searching and threedimensional docking calculations. On the other hand, to better understand how SAR discontinuity detected at the ligand level is reflected by three-dimensional protein-ligand interaction information, different compound series in combinatorial analog graphs were analyzed and substitution patterns that introduce activity cliffs were determined. The identified SAR determinants were then studied on the basis of threedimensional ligand-target X-ray crystal complexes to enable a structural interpretation of SAR discontinuity and underlying activity cliffs. The analysis showed that many discontinuous SAR features extracted from combinatorial analog graphs can be directly associated with experimental three-dimensional receptor-ligand interactions. However, this was not always possible and some substitution site patterns that introduce significant SAR discontinuity in analog series cannot be explained in structural terms. The second part of the thesis is focused on the application of different virtual screening methods for the identification of new cysteine and membranebound serine proteases inhibitors. In addition, molecular modeling studies were also applied to analyze the binding mode of cyclic peptide inhibitors. Two major virtual screening campaigns were carried out to identify cathepsin K, cathepsin S and matriptase-2 inhibitors. While cathepsins K and S are cysteine proteases, matriptase-2 is a newly identified type II membranebound serine protease. These proteases are considered to be important current pharmaceutical targets due to their involvement in bone resorption, immune response and iron metabolism, respectively. The first virtual screening application was focused at identification of dual cathepsin K and S inhibitors using a ligand-based compound mapping algorithm. By testing only 10 candidate compounds selected from a source database containing ~3.7 million molecules, two inhibitors of cathepsin K and S with new scaffolds were identified. Both inhibitors did not contain an electrophilic “warhead” that usually is present in most of the previously reported covalently interacting cathepsin inhibitors. In a second study, through structure-based virtual screening in combination with similarity searching and knowledge-based compound design, two N-protected dipeptide amides containing a 4-amidinobenzylamide were identified as the first small molecule inhibitors of matriptase-2 with Ki values of 170 nM and 460 nM, respectively. An inhibitor of the closely related protease, matriptase-1 (Ki = 220 nM) with more than 50-fold selectivity over matriptase-2 was also identified.