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  • Oral presentation
  • Open Access

Go with the flow and accessorize your drugs

Journal of Cheminformatics20146 (Suppl 1) :O11

  • Published:


  • Combinatorial Library
  • Target Prediction
  • Peptide Library
  • Equivalence Relationship
  • Discovery Program

The fast pace of drug discovery programs is supported by high-throughput screening campaigns to identify new chemical entities, where the underlying screening compound collections benefit from combinatorial libraries with lead- and drug-like properties. We will discuss recent developments in this field from two perspectives by addressing how to (i) assemble focused combinatorial libraries with desired properties (search problem), and (ii) identify targets for the designed compounds and, vice versa, compounds with desired target activities (scoring problem). Developments from our laboratory will be highlighted, specifically the MAntA (Molecular Ant Algorithm) approach for molecular building block prioritization, the target prediction method SPiDER (SOM-based Prediction of Drug Equivalence Relationships), and a quantitative Gaussian process model covering several hundred macromolecular targets for compound scoring. Using these algorithms we successfully generated activity-focused combinatorial small molecule and peptide libraries, de-orphaned de novo designed molecules, and correctly predicted off-target liabilities of known drugs. Selected examples of these practical applications will be presented, including an integrative application of combinatorial design, microfluidics-assisted synthesis, and activity testing.

Authors’ Affiliations

Department of Chemistry and Applied Biosciences, Eidgenössische Technische Hochschule (ETH), Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland


© Schneider; licensee Chemistry Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.