Biomedical subjects

Andrew J S Knox

Publications and source records attributed to Andrew J S Knox.

4 recordsLinked to original sources

PubMed2006

Permuting input for more effective sampling of 3D conformer space.

SMILES strings and other classic 2D structural formats offer a convenient way to represent molecules as a simplistic connection table, with the inherent advantages of ease of handling and storage. In the context of virtual screening, chemical databases to be screened are often initially represented by canonicalised SMILES strings that can be filtered and pre-processed in a number of ways, resulting in molecules that occupy similar regions of chemical space to active compounds of a therapeutic target. A wide variety of software exists to convert molecules into SMILES format, namely, Mol2smi (Daylight Inc.), MOE (Chemical Computing Group) and Babel (Openeye Scientific Software). Depending on the algorithm employed, the atoms of a SMILES string defining a molecule can be ordered differently. Upon conversion to 3D coordinates they result in the production of ostensibly the same molecule. In this work we show how different permutations of a SMILES string can affect conformer generation, affecting reliability and repeatability of the results. Furthermore, we propose a novel procedure for the generation of conformers, taking advantage of the permutation of the input strings--both SMILES and other 2D formats, leading to more effective sampling of conformation space in output, and also implementing fingerprint and principal component analyses step to post process and visualise the results.

Algorithms↗

PubMed2006

Oncology exploration: charting cancer medicinal chemistry space.

Approaches for the experimental determination of protein-ligand molecular interactions are reliant on the quality of the compounds being tested. The application of large, randomly designed combinatorial libraries has given way to the creation of more-focused 'drug-like' libraries. Prior to synthesis, we wish to screen the potential compounds to remove undesired chemical moieties and to be within a required range of physiochemical properties. We have used a principal-component analysis (PCA) computational approach to analyze the 3D descriptor space of active and non-active (hit-like) cancer medicinal chemistry compounds. We define hit-like those molecules passing the unmodified OpenEye FILTER program. Our analysis indicates that these compounds occupy quite different regions in space. Cancer-active compounds exist in a much greater volume of space than generic hit-like space and most of them fail the commonly applied filters for orally bioavailable drugs. This is of great significance when designing orally bioavailable cancer target drugs.

Antineoplastic Agents↗

PubMed2006

Estrogen receptors: molecular interactions, virtual screening and future prospects.

Identification of the Estrogen Receptor (ER) as a key mediator of the proliferation of breast cancer, and its involvement in pathways leading to osteoporosis and coronary heart disease, has resulted in a surge to discover and design compounds with the ability to modulate its actions (SERMs). Concurrently, a dramatic increase in the number of crystal structures of the ER has led to a more in depth understanding of the governing mechanisms involved in ER modulation. Entwining computational techniques with the availability of 3D structural data, has allowed not only the rational design of potent inhibitors of the ER, but also its incorporation in Virtual Screening (VS) in the search for novel chemotypes that can modulate the ER. An important initial step in the VS process is to filter towards molecules that occupy similar chemical space to a set of known actives prior to docking. We illustrate through Principal Component Analysis (PCA) of 145 descriptors the region of chemical space antiestrogens occupy compared with 'drug-like' space. We also review all available studies involving validation of several docking algorithms utilizing the ER, ultimately focusing on analysis of Enrichment (E) rates and False Positive (FP) rates to illustrate the successes attributed to each docking algorithm. Finally, we relate the recent discovery of non-genomic mechanisms of the ER and subsequently present a model involving a recently identified alternative, second binding-pocket of the ER in our laboratory through cavity analysis that suggests how the same receptor can invoke these, 'classical' and rapid responses concurrently.

Algorithms↗

PubMedDate not supplied

Considerations in compound database preparation--"hidden" impact on virtual screening results.

Structure-based virtual screening (SBVS) utilizing docking algorithms has become an essential tool in the drug discovery process, and significant progress has been made in successfully applying the technique to a wide range of receptor targets. In silico validation of virtual screening protocols before application to a receptor target using a corporate or commercially available compound collection is key to establishing a successful process. Ultimately, retrieval of a set of active compounds from a database of inactives is required, and the metric of enrichment (E) is habitually used to discern the quality of separation of the two. Numerous reports have addressed the performance of docking algorithms with regard to the quality of binding mode prediction and the issue of postprocessing "hit lists" of docked ligands. However, the impact of ligand database preprocessing has yet to be examined in the context of virtual screening and prioritization of compounds for biological evaluation. We provide an insight into the implications of cheminformatic preprocessing of a validation database of compounds where multiple protonated, tautomeric, stereochemical, and conformational states have been enumerated. Several commonly used methods for the generation of ligand conformations and conformational ensembles are examined, paired with an exhaustive rigid-body algorithm for the docking of different "multimeric" compound representations to the ligand binding site of the human estrogen receptor alpha. Chemgauss, a shapegaussian scoring function with intrinsic chemical knowledge, was combined with PLP as a consensus-scoring scheme to rank output from the docking protocol and enrichment rates calculated for each screen. The overheads of CPU consumption and the effect on relative database size (disk requirement) for each of the protocols employed are considered. Assessment of these parameters indicates that SBVS enrichments are highly dependent on the initial cheminformatic treatment(s) used in database construction. The interplay of SMILES representations, stereochemical information, protonation state enumeration, and ligand conformation ensembles are critical in achieving optimum enrichment rates in such screening.

Computer Simulation↗