Modeling a Crowdsourced Definition of Molecular Complexity

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• 作者：Robert P. Sheridan ; Nicolas Zorn ; Edward C. Sherer ; Louis-Charles Campeau ; Charlie (Zhenyu) Chang ; Jared Cumming ; Matthew L. Maddess ; Philippe G. Nantermet ; Christopher J. Sinz ; Paul D. O鈥橲hea
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2014
• 出版时间：June 23, 2014
• 年：2014
• 卷：54
• 期：6
• 页码：1604-1616
• 全文大小：811K
• ISSN：1549-960X

文摘

This paper brings together the concepts of molecular complexity and crowdsourcing. An exercise was done at Merck where 386 chemists voted on the molecular complexity (on a scale of 1鈥?) of 2681 molecules taken from various sources: public, licensed, and in-house. The meanComplexity of a molecule is the average over all votes for that molecule. As long as enough votes are cast per molecule, we find meanComplexity is quite easy to model with QSAR methods using only a handful of physical descriptors (e.g., number of chiral centers, number of unique topological torsions, a Wiener index, etc.). The high level of self-consistency of the model (cross-validated R² 0.88) is remarkable given that our chemists do not agree with each other strongly about the complexity of any given molecule. Thus, the power of crowdsourcing is clearly demonstrated in this case. The meanComplexity appears to be correlated with at least one metric of synthetic complexity from the literature derived in a different way and is correlated with values of process mass intensity (PMI) from the literature and from in-house studies. Complexity can be used to differentiate between in-house programs and to follow a program over time.