In the next couple of years the human genome will be fully sequenced. This will provide us with the sequence and overall function of all human genes as well as the complete genome for many micro-organisms. Subsequently it is hoped, by means of powerful bioinformatic tools, to determine the gene variants that contribute to various multifactorial diseases and genes that exist in certain infectious agents but not humans. As a consequence, this will allow us to define the most appropriate levels for drug intervention. It can be expected that the number of potential drug targets will increase, possibly by a factor of 10 or more. Nevertheless, sequencing the human genome or, for that matter, the genome of other species will only be the starting point for the understanding of their biological function. Structural genomics is a likely follow-up, combined with new techniques to validate the therapeutic relevance of such newly discovered targets. Accordingly, it can be expected that in the near future we will witness a substantial increase in novel putative targets for drugs. To address these new targets effectively, we require new approaches and innovative tools. At present, two alternative, yet complementary, techniques are employed: experimental high-throughput screening (HTS) of large compound libraries, increasingly provided by combinatorial chemistry, and computational methods for virtual screening and de novo design.As kind of status report on the maturity of virtual screening as a technique in drug design, the first workshop on new approaches in drug design and discovery was held in March 1999, at Schloß Rauischholzhausen, near Marburg in Germany. More than 80 scientists gathered and discussed their experience with the different techniques. The speakers were invited to summarize their contributions together with their impressions on the present applicability of their approach. Several of the speakers followed this request which is summarized in this publication.

Combination of molecular similarity measures using data fusion.- Optimization of the drug-likeness of chemical libraries.- Generating consistent sets of thermodynamic and structural data for analysis of protein-ligand interactions.- Multiple molecular superpositioning as an effective tool for virtual database screening.- A recursive algorithm for efficient combinatorial library docking.- Modifications of the scoring function in FlexX for virtual screening applications.- A knowledge-based scoring function for protein-ligand interactions: Probing the reference state.- Predicting binding modes, binding affinities and 'hot spots' for protein-ligand complexes using a knowledge-based scoring function.- Hydrophobicity maps and docking of molecular fragments with solvation.- Virtual screening with solvation and ligand-induced complementarity.- Similarity versus docking in 3D virtual screening.- Discovering high-affinity ligands from the computationally predicted structures and affinities of small molecules bound to a target: A virtual screening approach.- In vitro and in silico affinity fingerprints: Finding similarities beyond structural classes.- Computer-assisted synthesis and reaction planning in combinatorial chemistry.- Evaluation of reactant-based and product-based approaches to the design of combinatorial libraries.