Rapid evaluation of synthetic and molecular complexity for in silico chemistry.
Methods that rapidly evaluate molecular complexity and synthetic feasibility are becoming increasingly important for in silico chemistry. We propose a new metric based on relative atomic electronegativities and bond parameters that evaluate both synthetic and molecular complexity (SMCM) starting from chemical structures. Against molecular weight, SMCM has the lowest fraction of adjusted variance (R2=0.535) 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 Rücker (R2=0.895 for log complexity values; J. Chem. Inf. Comput. Sci. 2004, 44, 378-386), respectively. These metrics are in general agreement when the metabolic synthesis of cholesterol from S-3-hydroxy-3-methyl-glutaryl coenzyme A is monitored, indicating that SMCM can be useful in discerning increases in complexity. Because the presence of substructure patterns can be directly incorporated into this scheme, SMCM is relatively straightforward and can be easily tailored to rapidly evaluate virtual (combinatorial) libraries and high throughput screening hits.
Allu
Tharun Kumar
TK
Department of Computer Science, University of New Mexico School of Medicine, MSC 11 6145, 1 University of New Mexico, Albuquerque, New Mexico 87131, USA.
Oprea
Tudor I
TI
D000214:Acyl Coenzyme A / Q000378:metabolism; D000818:Animals; D002784:Cholesterol / Q000378:metabolism; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D015394:Molecular Structure; D008970:Molecular Weight; D012984:Software
D000214:Acyl Coenzyme A; C015901:glutaryl-coenzyme A; D002784:Cholesterol