An Efficient Projection Protocol for Chemical Databases: Singular Value Decomposition Combined with Truncated-Newton Minimization

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.

Click to go back to the publication list