We describe a rapid algorithm for visualizing large chemical databases in a low-dimensional space (2D or 3D) as a rst step in chemical database analyses and drug design applications. The compounds in the database are described as vectors in the high-dimensional space of chemical descriptors. The algorithm is based on the singular value decomposition (SVD) combined with a minimization procedure implemented with the e cient truncated-Newton program package (TNPACK). Numerical experiments show that the algorithm achieves an accuracy in 2D for scaled datasets of around 30 to 46%, re ecting the percentage of pairwise distance segments that lie within 10% of the original distance values. The low percentages can be made close to 100% with projections onto a ten-dimensional space. The 2D and 3D projections, in particular, can be e ciently generated and easily visualized and analyzed with respect to clustering patterns of the compounds.