The next generation of phosphodiesterase inhibitors: structural clues to ligand and substrate selectivity of phosphodiesterases.
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Animals↗
Biomedical subjects
David T Manallack
Publications and source records attributed to David T Manallack.
Scaffold hopping in de novo design. Ligand generation in the absence of receptor information.
We report here the de novo generation of chemotypes and scaffolds for the estrogen receptor, without use of the receptor structure in the assembly phase. Through use of ligand superpositions or a single bound conformation of a known active, a pseudoreceptor can be generated as a design envelope, within which novel structures are readily assembled. Many of these structures have high similarity to known chemotypes. Scaffold hopping is readily achieved within this pseudoreceptor, indicating the advantages of such an approach in discovery research.
Benzene Derivatives↗
Database searching for thymidine and thymidylate kinase inhibitors using three-dimensional structure-based methods.
Structure-based drug design methods were used to search for novel inhibitors of herpes simplex virus type 1 (HSV-1) thymidine kinase and Mycobacterium tuberculosis thymidylate kinase. The method involved the use of crystal structure complexes to guide database searching for potential inhibitors. A number of weak inhibitors of HSV-2 were identified, one of which was found to inhibit HSV-1 TK and HSV-1 TK-deficient viral strains. Each compound tested against M. tuberculosis thymidylate kinase was found to have some activity. The best of these compounds was only 4.6-fold less potent than 3'-azido-3'-deoxythymidine-5'-monophosphate (AZTMP). This study demonstrates the utility of structure-based drug design methods in the search for novel enzyme inhibitors.
Anti-Bacterial Agents↗
Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks.
A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. In each case, the networks identified over 80% of the compounds targeting a gene family. The technique was applied to purchasing compounds from external suppliers, and results from screening against one gene family demonstrated impressive abilities to predict the activity of the majority of known hit compounds.
Databases, Factual↗
A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.
BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.
Journal Article↗