Abstract

Scientific knowledge production is a domain of human excellence, but recently this position became progressively challenged by machine intelligence. Some human tasks will be conquered sooner; others, which include design, may still require human intuition. Would the automated knowledge production truly challenge the human, or is one just an extension – a prosthetic – of the other? What could be the place of art in such environment? Mess and messy scientific models have the potential to provide paradigm shifts in molecular modelling, cancer research and systems biology where not only living humans but also machine learning algorithms craft knowledge. Together with art they have the potential for different reading and re-framing, which are the qualities scientists are looking for when engaging works of art within their molecular modelling process. In my opinion, these are the same qualities scientists are also looking for in the outputs of machine learning and computational chemistry modelling.