A Day in the Life of a Computational Biologist

Most of my other projects are concerned with large-scale virtual screening applications: In collaboration with labs that do experimental biology, we develop and apply methods to predict candidate molecules that either inhibit (or activate, depending on the project) an individual protein, in absence or presence of a protein crystal structure. The interesting part about it is the interplay between predictions and feedback: I get to make predictions and (at some point), I get the experimental results to see whether I was right or wrong and to analyze why certain predictions worked better than others. Another exciting challenge in such projects is that one has to find ways to make this all computationally feasible -- if you have 15 million molecules, selecting 100 candidate molecules for experimental testing is a bit like searching for the needle in the haystack. Usually, it comes down to formulating specific hypotheses upfront as "filtering" steps since a brute-force docking, which computationally not feasible (since we also have time constraints). My projects require a certain amount of creativity and technical skills to put the ideas into action, but eventually, the approach (the hypotheses) also have to make sense to our collaborators (and the funding agencies).