Title:

Rapid Evaluation of Synthetic and Molecular Complexity for In
Silico Chemistry

Authors:
Tharun Kumar Alluâ and Tudor I. Oprea
Department of Computer Science and Division of Biocomputing
University of New Mexico School of Medicine


Rapid methods to evaluate molecular complexity and synthetic
feasibility are becoming increasingly important for in
silico chemistry. We propose a new metric that evaluates
both synthetic and molecular complexity (SMCM) starting from
chemical structures. Against molecular weight, SMCM has the
lowest fraction of adjusted variance (R2 = 0.553) on a series of
261,048 diverse compounds, when compared to the complexity metric
of Baron and Chanon (R2=0.777; J. Chem. Inf. Comput. Sci. 2001,
41, 269-272) and RĂĽcker (R2=0.895 for log10 complexity values;
J. Chem. Inf. Comput. Sci. 2004, 44, 378-386), respectively. SMCM
counts atom types, substructure patterns and atomic and bond
parameters from Balaban (J. Chem. Inf. Comput. Sci. 1998, 38,
395-401) and can be further tailored to rapidly evaluate virtual
(combinatorial) libraries.