The term Big Data has become very popular and is applied to many scenarios and domains though not always in a meaningful manner. While many big data problems are characterized by large amounts of data being generated very rapidly, this is rarely the case for chemical problems. Instead, a greater challenge is that of multiple scales of data and multiple types of data. In this talk I will discuss how the challenges of chemical genomics require new computational approaches that must scale to large data sizes but also be capable of integrating multiple heterogeneous data types. I will focus on some problems associated with the analysis of high throughput screening datasets using fragment based methods, which while not really Big, can generate downstream data that easily consumes significant computational resources. Specifically I will describe the ongoing development of distributed methods for fragment based isostere analysis. I will also take a broad look at the informatics challenges that occur when faced with many large datasets, covering multiple cell lines and tissue types and how they must be addressed to achieve a comprehensive view of small molecule action in biological systems. In this context I will highlight recent developments in data sources (BARD, LINCS, GDB-13 & GDB-17) as well as the opportunities for method development that they present.

Bridging chemistry and biology with data

248th National Meeting (2014)

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