期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 51, 期 3, 页码 541-557出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci1002087
关键词
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A new molecular enumerator is described that allows chemically and architecturally diverse sets of natural product-like and drug-like structures to be generated from a core structure as simple as a single carbon atom or as complex as a polycyclic ring system. Integrated with a rudimentary machine-learning algorithm, the enumerator has the ability to assemble. biased virtual libraries enriched in compounds predicted to meet target criteria. The ability to dynamically generate relatively small focused libraries in a recursive manner could reduce the computational time and infrastructure necessary to construct and manage extremely large static libraries. Depending on enumeration conditions, natural product-like structures can be produced with a wide range of heterocyclic and alicyclic ring assemblies. Because natural products represent a proven source of validated structures for identifying and designing new drug candidates, mimicking the structural and topological diversity found in nature with a dynamic set of virtual natural product-like compounds may facilitate the creation of new ideas for novel, biologically relevant lead structures in areas of uncharted chemical space.
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