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This work outlines a new de novo design process for the creation of novel kinase inhibitor libraries. It relies on a profiling paradigm that generates a substantial amount of kinase inhibitor data from which highly predictive QSAR models can be constructed. In addition, a broad diversity of X-ray structure information is needed for binding mode prediction.(More)
LY3009120 is a pan-RAF and RAF dimer inhibitor that inhibits all RAF isoforms and occupies both protomers in RAF dimers. Biochemical and cellular analyses revealed that LY3009120 inhibits ARAF, BRAF, and CRAF isoforms with similar affinity, while vemurafenib or dabrafenib have little or modest CRAF activity compared to their BRAF activities. LY3009120(More)
Support Vector Machine (SVM), one of the most promising tools in chemical informatics, is time-consuming for mining large high-throughput screening (HTS) data sets. Here, we describe a parallelization of SVM-light algorithm on a graphic processor unit (GPU), using molecular fingerprints as descriptors and the Tanimoto index as kernel function. Comparison(More)
A reconstructive approach based on computational fragmentation of existing inhibitors and validated kinase potency models to recombine and create "de novo" kinase inhibitor small molecule libraries is described. The screening results from model selected molecules from the corporate database and seven computationally derived small molecule libraries were(More)
BAO (BACIIS Ontology) is a domain ontology for BACIIS (Biological And Chemical Information Integration System). The main goals of BAO are: 1) to guide users in creating efficient queries, 2) to facilitate resolution of the variabilities among various data formats and data sources, and 3) to facilitate the integration of biological and chemical data web(More)
The incompatibilities among complex data formats and various schema used by biological databases that house these data are becoming a bottleneck in biological research. For example, biological data format varies from simple words (e.g., gene name), numbers (e.g., molecular weight) to sequence strings (e.g., nucleic acid sequence), to even more complex data(More)
The application of translational approaches (e.g. from bed to bench and back) is gaining momentum in the pharmaceutical industry. By utilizing the rapidly increasing volume of data at all phases of drug discovery, translational bioinformatics is poised to address some of the key challenges faced by the industry. Indeed, computational analysis of clinical(More)
Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome(More)
The complexity of cross-disciplinary knowledge discovery is two-fold: integration of vast amount of information in disparate silos, and dissemination of discovery to stakeholders with different interests. Here we propose a framework that combines Semantic Web technology, graph algorithms, and user profiling to discover and prioritize novel associations(More)
Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network(More)