Title | ||
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An Efficient Projection Protocol for Chemical Databases: Singular Value Decomposition Combined with Truncated-Newton Minimization. |
Abstract | ||
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A rapid algorithm for visualizing large chemical databases in a low-dimensional space (2D or 3D) is presented as a first step in database analysis and design applications. The projection mapping of the compound database (described as vectors in the high-dimensional space of chemical descriptors) is based on the singular value decomposition (SVD) combined with a minimization procedure implemented with the efficient truncated-Newton program package (TNPACK). Numerical experiments on four chemical datasets with real-valued descriptors (ranging from 58 to 27 255 compounds) show that the SVD/TNPACK projection duo achieves a reasonable accuracy in 2D, varying from 30% to about 100% of pairwise distance segments that lie within 10% of the original distances. The lowest percentages, corresponding to scaled datasets, can be made close to 100% with projections onto a 10-dimensional space. We also show that the SVD/TNPACK duo is efficient for minimizing the distance error objective function (especially for scaled datasets), and that TNPACK is much more efficient than a current popular approach of steepest descent minimization in this application context. Applications of our projection technique to similarity and diversity sampling in drug design can be envisioned. |
Year | DOI | Venue |
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2000 | 10.1021/ci990333j | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
Keywords | Field | DocType |
steepest descent,molecular structure,drug design,objective function,database management systems,singular value decomposition | Pairwise comparison,Projection mapping,Singular value decomposition,Combinatorics,Minification,Ranging,Chemical database,Mathematics,Database analysis | Journal |
Volume | Issue | ISSN |
40 | 1 | 0095-2338 |
Citations | PageRank | References |
6 | 0.81 | 7 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Dexuan Xie | 1 | 67 | 10.92 |
Alexander Tropsha | 2 | 639 | 69.76 |
Tamar Schlick | 3 | 251 | 62.71 |