Title: The quest of computer scientists to discover new drugs
 
Abstract:
Depending on which paper you read, the number of drug-like molecules ranges from ∼109 to 1060 [1, 2] (looks like science is not that exact after all!). Theoretically, when exhaustively searching for new drugs we should visit each point (molecule) in this space and rank it using some 'drug scoring’ function. In fact, when considering molecules in 3D (which is how they are found in nature) the search space is orders of magnitude larger because each of these molecules can adopt different molecular shapes (called 'conformer ensembles’) [3].

In order to discover new drugs, we need to be able to represent all these molecules, to store them, and to develop quick and efficient algorithms to search and rank them (in terms of their drug potential). Techniques for visualization of this space are also an active area of research. I will highlight the use of computer science techniques in order to achieve this. I will argue that the often misused and overused terms 'big data’ and 'data science’ apply beautifully to this field.

References:

[1]  Drew KL, Baiman H, Khwaounjoo P, Yu B, Reynisson J. Size estimation of chemical space: how big is it?, J Pharm Pharmacol. 2012 Apr;64(4):490-5.
[2]  Reymond JL, Awale M. Exploring chemical space for drug discovery using the chemical universe database., ACS Chem Neurosci. 2012 Sep 19;3(9):649-57.
[3]  Ebejer JP, Morris GM, Deane CM. Freely available conformer generation methods: how good are they?, J Chem Inf Model. 2012 May 25;52(5):1146-58.