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  • Review Article
  • Published:

Combinatorial informatics in the post-genomics era

Nature Reviews Drug Discovery volume 1, pages 337–346 (2002)Cite this article

Key Points

  • A practical and cost-effective embodiment of a chemogenomics approach to drug discovery involves the following steps:

  • Gene sequences for targets that have been identified by genomics approaches are cloned and expressed as target proteins that are suitable for screening with a probe library of small, drug-like chemical compounds.

  • These compounds are screened to find active hits using a quantitative universal binding assay.

  • Initial hits or quantitative structure–activity data emerging from the binding assay are analysed and used to formulate a selection strategy for the synthesis of additional compounds with improved properties.

  • These compounds are selected from a computer database of synthetically accessible analogues of the initial probe library, constructed using verified synthetic protocols and characterized by an extensive set of computed drug-related molecular properties.

  • The selected compounds are synthesized by parallel-synthesis methods and are subsequently tested to elaborate the structure–activity profile of the target under investigation, and refine the selection criteria for additional rounds of chemical synthesis and biological testing.

  • In each iteration, priority is assigned to the synthetic candidates using a multiobjective optimization process designed to assure that compounds are not only optimized for target binding affinity, but also have drug-like characteristics that will allow them to be used directly as tool compounds in appropriate cellular or biological model systems.

  • The potential for improved performance using such a strategy lies in the ability to rapidly follow up on initial hits through intelligent selection of related compounds from a computer database of synthetically accessible analogues with predefined synthesis recipes and predicted property profiles.

  • To address the full spectrum of targets emerging from genomics-based efforts, it will be necessary to physically screen probe libraries that span a wide range of chemotypes and contain hundreds of thousands of compounds.

  • These libraries will derive from synthetic strategies that could, in theory, produce billions of related analogues, which far exceeds the capabilities of conventional chemical-database management systems and data-modelling tools.

  • Thus, the following key questions need to be addressed:

  • How can huge combinatorial libraries be generated, represented, accessed, searched and manipulated?

  • What are the most appropriate chemical-property spaces, and how can they best be computed, sampled, visualized and validated?

  • What are the most effective ways to design, execute and analyse a combinatorial-chemistry experiment?

  • Successful deployment of such a system requires a new generation of computational tools that work effectively on a massive scale.

Abstract

The multitude of potential drug targets emerging from genome sequencing demands new approaches to drug discovery. A chemogenomics strategy, which involves the generation of small-molecule compounds that can be used both as tools to probe biological mechanisms and as leads for drug-property optimization, provides a highly parallel, industrialized solution. Key to the success of this strategy is an integrated suite of chemi-informatics applications that can allow the rapid and directed optimization of chemical compounds with drug-like properties using 'just-in-time' combinatorial chemical synthesis. An effective embodiment of this process requires new computational and data-mining tools that cover all aspects of library generation, compound selection and experimental design, and work effectively on a massive scale.

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Figure 1: A practical and cost-effective embodiment of a chemogenomics strategy.
Figure 2: Virtual-library generation.
Figure 3: Representative chemical classes in the 3DP probe library.
Figure 4: Diversity and drug likeness of the 3DP probe library.
Figure 5: Iterative library design.
Figure 6: Multiobjective library design.
Figure 7: Combinatorial networks.

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  1. 3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, 19341, Pennsylvania, USA

    Dimitris K. Agrafiotis, Victor S. Lobanov & F. Raymond Salemme

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  1. Dimitris K. Agrafiotis
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  2. Victor S. Lobanov
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  3. F. Raymond Salemme
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Correspondence to Dimitris K. Agrafiotis.

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Glossary

PROBE LIBRARY

A collection of diverse compounds that is aimed at discovering hits across a wide variety of biological targets.

MULTIOBJECTIVE OPTIMIZATION

The solution to a problem that involves the simultaneous optimization of multiple design objectives.

VIRTUAL LIBRARY

A computer representation of a collection of chemical compounds.

COMBINATORIAL LIBRARY

A collection of compounds that are derived from the systematic application of a synthetic sequence on a prescribed set of building blocks.

ENUMERATION

The process of constructing the connection tables of the combinatorial products from their respective building blocks, as prescribed by the reaction sequence.

CLIPPED REAGENT

The (potentially modified) part of a reagent that becomes part of the final product.

CONNECTION TABLE

A computer representation of the atoms and bonds that comprise a molecule. This is the computer equivalent of a chemical sketch of a molecule.

LAZY ENUMERATION

The on-demand virtual synthesis of combinatorial products.

MOLECULAR PERCEPTION

The computational detection of important structural features, such as rings, aromaticity, stereochemistry and topological symmetry, from the molecule's connection table.

MOLECULAR DIVERSITY

The chemical-information content of a collection of compounds. The concept is often context dependent.

GRAPH THEORY

Formally, a connection table for a molecule records its chemical structure as a graph — a set of vertices (the atoms) linked by edges (the bonds). This allows mathematical analyses to be used to classify the structure or calculate molecular properties.

FINGERPRINT

A set of binary numbers (1s and 0s) that are used to characterize a molecular structure. Each bit signifies the presence (1) or absence (0) of one or more structural features in the target molecule.

PHARMACOPHORE

The ensemble of steric and electronic features that are necessary to ensure optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological function. Only molecules that interact at the same receptor site in the same way share a common pharmacophore.

DRUG LIKENESS

The thesis that drugs have certain common properties that differentiate them from other ordinary chemicals.

LOG P

The octanol/water partition coefficient is the ratio of the compound's solubility in octanol to its solubility in water. The logarithm of this partition coefficient is called log P. It provides an estimate of the compound's ability to pass through a cell membrane.

OUTLIER

A point that, because of observation noise, does not follow the characteristics of the input (or desired response) data.

CURSE OF DIMENSIONALITY

The sparsity of data in higher dimensions.

QUADRATIC COMPLEXITY

Quadratic complexity means that if the size of the problem doubles, the computational time that is required by the algorithm to solve it quadruples. The complexity (or order) of an algorithm is an important criterion for comparing algorithms that involve the analysis of large data sets.

LIPINSKI RULE OF 5

For compounds that are not substrates of biological transporters, poor absorption and permeation are more likely to occur when there are more than 5 hydrogen-bond donors, more than 10 hydrogen-bond acceptors, the molecular mass is greater than 500 Da, or the log P is greater than 5.

BIOISOSTERISM

The idea that a chemical group in a biologically active molecule can be replaced by another chemical group without loss of activity.

COMBINATORIAL OPTIMIZATION

The number of different combinations of k objects out of a set of n objects is given by the binomial coefficient Cnk = n!/(n − k)!k!. This can be used to calculate the number of distinct k1×k2×...×kd combinatorial arrays in a n1×n2×...×nd combinatorial library. For example, there are approximately 1040 different 10×10×10 arrays in a 100×100×100 library.

COMBINATORIAL NEURAL NETWORK

(CNN). A neural network that is trained to predict molecular properties of combinatorial products from pertinent features of their respective building blocks.

FEATURE SELECTION

A computational technique that attempts to identify a small subset of features that are most relevant to a particular machine learning task.

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Agrafiotis, D., Lobanov, V. & Salemme, F. Combinatorial informatics in the post-genomics era. Nat Rev Drug Discov 1, 337–346 (2002). https://doi.org/10.1038/nrd791

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