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LoFT: Similarity-Driven Multiobjective Focused Library Design
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AI
DOI：10.1021/ci900287p.png)
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Abstract
En 中文
We present LoFT, a tool for focused combinatorial library design. LoFT provides a set of algorithms, constructing a focused library from a chemical fragment space under optimization of multiple design criteria. A weighted multiobjective scoring function based oil physicochemical descriptors is employed for traversing the chemical search space. The new aspect of LoFT is that a similarity-driven product-based library design approach is provided on fragment level. For this reason the feature tree descriptor is incorporated for similarity comparison of library compounds to given bioactive molecules as well as for diversifying the resulting libraries. The feature tree descriptor abstracts the molecular graph to a tree structure where the nodes are labeled with physicochemical properties. For comparison, the nodes of two trees are mapped onto each other. This strictly hierarchical mechanism is suitable for the efficient comparison of chemical fragments, allowing the evaluation of the resulting products Oil fragment level without explicitly enumerating them. LoFT was validated, applying three different data sets. Starting with a random reagent selection, we optimized the libraries using maximum similarity to known bioactive molecules and iteratively adding further criteria. Moreover, we compared these results with data we obtained with FTrees-FS.
Keywords:
DE-NOVO DESIGN
PHYSICOCHEMICAL PARAMETERS
FRAGMENT SPACES
COMBINATORIAL
OPTIMIZATION
ALGORITHM
PREDICTION
SURFACE
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