A practical approach to a viral detection pipeline using existing viral and non-viral sequence resources.

For public health safety, vaccines and other pharmaceutical products as well as the raw materials used in their manufacture need to be tested for adventitious virus contamination. The current standard of practice is to develop culture-based or polymerase chain reaction assays for the types of viruses one might expect based upon the source of reagents used. High-throughput sequencing technology is well-suited for building an unbiased strategy for the purpose of adventitious virus detection. We have developed an approach to automate curation of publically available nucleotide sequences, and have practically balanced the desire to capture all viral diversity while simultaneously reducing the use of partial viral sequences that represent the largest source of false positive results. In addition, we describe an effective workflow for virus detection that can process sequence data from all currently available High-throughput sequencing technologies and produce a report that summarizes the weight of sequence data in support of each detected virus.

Systematic approach to conformational sampling for assigning absolute configuration using vibrational circular dichroism.

Systematic methods that speed-up the assignment of absolute configuration using vibrational circular dichrosim (VCD) and simplify its usage will advance this technique into a robust platform technology. Applying VCD to pharmaceutically relevant compounds has been handled in an ad hoc fashion, relying on fragment analysis and technical shortcuts to reduce the computational time required. We leverage a large computational infrastructure to provide adequate conformational exploration which enables an accurate assignment of absolute configuration. We describe a systematic approach for rapid calculation of VCD/IR spectra and comparison with corresponding measured spectra and apply this approach to assign the correct stereochemistry of nine test cases. We suggest moving away from the fragment approach when making VCD assignments. In addition to enabling faster and more reliable VCD assignments of absolute configuration, the ability to rapidly explore conformational space and sample conformations of complex molecules will have applicability in other areas of drug discovery.

Scoring of KDR kinase inhibitors: using interaction energy as a guide for ranking.

Within a congeneric series of ATP-competitive KDR kinase inhibitors, we determined that the IC(50) values, which span four orders of magnitude, correlated best with the calculated ligand-protein interaction energy using the Merck Molecular Force Field (MMFFs(94)). Using the ligand-protein interaction energy as a guide, we outline a workflow to rank order virtual KDR kinase inhibitors prior to synthesis. When structural information of the target is available, the ability to score molecules a priori can be used to rationally select reagents. Our implementation allows one to select thousands of readily available reagents, enumerate compounds in multiple poses and score molecules in the active site of a protein within a few hours. In our experience, virtual library enumeration is best used when a correlation between computed descriptors/properties and IC(50) or K (i) values has been established.

Calculating similarities between biological activities in the MDL Drug Data Report database.

There are a number of licensed databases that assign biological activities to druglike compounds. The MDL Drug Data Report (MDDR), compiled from the patent literature, is a popular example. It contains several hundred distinct activities, some of which are therapeutic areas (e.g., Antihypertensive) and some of which are related to specific enzymes or receptors (e.g., ACE inhibitor). There are several data mining applications where it would be useful to calculate a similarity between any two activities. Two distinct activity labels can have a significant similarity for a number of reasons: two activities can be nearly synonymous (e.g., CCK B antagonist vs Gastrin antagonist), one activity may be a subset of another (e.g., Dopamine (D2) agonist vs Dopamine agonist), or an activity can be the mechanism by which another activity works (e.g., ACE inhibitor vs Antihypertensive), etc. In an ideal world, similarities for two activities could be calculated simply by comparing the compounds they have in common, but in hand-curated databases such as the MDDR the assignment of activities to compounds are inevitably inconsistent and incomplete. We propose a number of methods of calculating activity-activity similarities that hopefully compensate for errors in hand-curation. Two of these, TIMI and trend vector, show promise. Soft clustering of the activities using a union of similarity methods shows a reasonable association of therapeutic areas with their mechanisms.