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  • Review Article
  • Published:

Integration of virtual and high-throughput screening

Nature Reviews Drug Discovery volume 1, pages 882–894 (2002)Cite this article

Key Points

  • High-throughput (HTS) and virtual screening (VS) have progressed rather independently over the years. However, these disciplines have similar goals and are highly complementary. There are good indications that drug discovery research will increasingly benefit from an integrated approach to screening.

  • A diverse array of VS methods has been developed, including structural queries, pharmacophores, molecular fingerprints, QSAR models, diverse cluster analysis tools, statistical techniques and docking calculations. In addition, VS techniques have been implemented to filter large databases for compounds with desired or undesired chemical groups, drug-like character, preferred solubility and absorption characteristics, or oral bioavailability.

  • Both small-molecule- and structure-based VS have recently produced several success stories in the search for novel inhibitors or antagonists of diverse biological targets.

  • Some VS methods have been introduced or adapted for the analysis of HTS data, taking into account that such data sets are usually noisy and error prone. Prominent among these methods are different partitioning and clustering algorithms that can derive predictive models of biological activity from screening data.

  • Similar approaches are used to interface HTS and VS directly. At present, this is best accomplished by the application of iterative screening strategies, such as focused or sequential screening. Although the details of such strategies can differ considerably, they have in common that small subsets of compounds are computationally selected from large databases and assayed. On the basis of the obtained results, the search for biologically active molecules is further refined in subsequent iterations.

  • In several case studies, sequential screening has yielded significant improvements in hit rates over random screening. It is not uncommon for iterative screening to achieve hit rates between 10% and 40% (by markedly reducing the number of compounds to be tested).

  • As the size of compound databases and the number of available screening targets rapidly increase, it is conceivable that combined computational and biological screening might soon become a focal point of pharmaceutical research, despite the advances that are being made in the HTS arena towards even higher throughput.

Abstract

High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.

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Figure 1: Representative molecular descriptors and their classification.
Figure 2: Different methods and tools for virtual screening.
Figure 3: Recognition of remote-similarity relationships.
Figure 4: Clustering versus partitioning.
Figure 5: Generation of low-dimensional chemical spaces for cell-based partitioning.
Figure 6: Results of a virtual-screening benchmark 'experiment'.
Figure 7: A two-descriptor model for passive absorption.
Figure 8: Structural similarity versus biological activity.
Figure 9: Strategies for sequential screening.
Figure 10: Frequent hitters.

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Acknowledgements

The author is grateful to F. Stahura for critical review of the manuscript and help with illustrations.

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Authors and Affiliations

  1. Department of Computer-Aided Drug Discovery, Albany Molecular Research, Inc. (AMRI), Bothell Research Center (AMRI–BRC), 18804 North Creek Parkway, Bothell, 98011, Washington, USA

    Jürgen Bajorath

  2. Department of Biological Structure, University of Washington, Seattle, 98195, Washington, USA

    Jürgen Bajorath

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  1. Jürgen Bajorath
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DATABASES

LocusLink

α1-adrenoceptor

BCL-xL

carbonic anhydrase

β-lactamase

monoamine oxidase

oestrogen receptor

tyrosine phosphatase-1B

VLA-4

FURTHER INFORMATION

Cancer.gov

Daylight Chemical Information Systems

MDL

Glossary

SUBSTRUCTURE

A defined structural fragment of a molecule.

PHARMACOPHORE

The spatial arrangement of chemical groups or features in a molecule that are known or thought to determine its activity. The most popular pharmacophore models consist of three or four points separated by defined distance ranges. In most cases, pharmacophore geometry is not known from experiment, but is predicted.

MOLECULAR GRAPH

A two-dimensional representation of the connectivity pattern in a molecule, with atoms shown as vertices and bonds as edges.

QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP (QSAR).

QSAR analysis refers to methods that relate structural features of molecules to biological activity in quantitative terms. In most cases, QSAR analysis attempts to establish linear relationships between selected structural features in a series of related molecules and their known level of activity. If successful, models derived from training sets can be applied to predict molecules with higher potency.

BINARY BIT STRING

A series of 1 or 0 characters. Each bit position is either set 'on' (that is, set to 1) or 'off' (0), and can account for the presence or absence of a specific feature.

TANIMOTO COEFFICIENT

The most popular metric for the quantitative comparison of binary molecular fingerprints. This coefficient is defined as Tc = bc/(b1 + b2 − bc). In this formulation, b1 represents the number of bits that are set on in the first fingerprint, b2 is the number of bits that are set on in the second fingerprint, and bc is the number of bits common to both fingerprints. If the Tc value is 1, then the compared fingerprints are identical.

COMBINATORIAL PROBLEM

As used here, the term describes the situation that the number of possible pairwise comparisons c grows with the number of objects n according to the formula c = n(n − 1)/2. So, if n becomes increasingly large, methods that rely on pairwise comparisons of, for example, database molecules become computationally infeasible.

BINNING

This process divides coordinate axes into intervals (typically of equal size). If binning is applied to the axes of 2D and 3D coordinate systems, grids and cells are obtained, respectively.

NEURAL NETWORK

Artificial neural networks are collections of mathematical models that are interconnected and organized in different layers. Given this architecture, the models correspond to neurons and the connections to synapses of the nervous system. Neural network simulations are analogous to an adaptive learning process. So, neural nets are typically trained to distinguish between different objects and their properties in learning sets, and the resulting models are then applied to make predictions on test sets.

QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIP (QSPR).

A variation of the QSAR approach, in which structural features of molecules are not quantitatively related to biological activity, but instead to physical properties, such as aqueous solubility or passive absorption.

DRUG-LIKE

The concept of 'drug-likeness' is based on the premise that drugs share specific molecular characteristics that systematically distinguish them from other synthetic or natural compounds.

LOGP(O/W)

The logarithm of the octanol/water partition coefficient (often abbreviated logP) describes the solubility of a compound in octanol (hydrophobic solvent) relative to its solubility in water (polar solvent).

RULE-OF-FIVE

On the basis of statistical analysis of known drugs, candidate compounds are likely to have unfavourable absorption, permeation and bioavailability characteristics if they contain more than 5 hydrogen-bond donors, more than 10 hydrogen-bond acceptors, a logP greater than 5 and/or a molecular mass of more than 500 Da.

PRINCIPAL COMPONENT ANALYSIS

(PCA). A mathematical method that captures the variance in a data set with respect to chosen variables, and transforms correlated variables into a smaller number of uncorrelated ones for data presentation.

GENETIC ALGORITHM

Computational implementation of a problem-solving approach that uses principles of biological competition and population dynamics. Model parameters are encoded in a 'chromosome', and are varied. Chromosomes yield possible solutions to a given problem by means of a fitness function. Chromosomes that correspond to the best intermediate solutions are subjected to operations that are analogous to gene recombination and mutation to produce the next generation. This process continues until solutions reach a predefined convergence criterion.

ADME

Absorption, distribution, metabolism and excretion are important effects that determine the in vivo characteristics of drug (candidate) molecules.

DECISION TREE

A data set is successively divided at decision points. At each point, a 'yes' or 'no' decision is made for each object, dividing the data into smaller and smaller subsets along the tree. All objects in a given subset share the same signature of 'yes' or 'no' decisions.

BINARY DESCRIPTORS

These types of descriptor capture two defined states (and not continuous value ranges). Typical examples include a specific substructure or bond pattern. The feature detected by a binary descriptor is either 'present' (state 1) or 'absent' (state 2). Application of binary descriptors allows the classification of molecular data sets by means of decision trees.

SCAFFOLD

Often defined as the core structure of a small molecule, the scaffold is typically a ring system that has diverse chemical groups attached. Accordingly, it is obtained by removal of these attached groups.

CHEMOTYPE

A family of molecules that has a unique core structure or scaffold.

PHYLOGENETIC TREE

This classification structure has its origin in biology to describe evolutionary relationships. It classifies a family of objects into 'most-similar' sets by subdividing them at branch points into successively smaller subsets with increasing object similarity. The final subsets represent unique leaves of the tree. Different from a simple decision tree, a phylogenetic tree structure can create multiple branches at each point.

BAYES' THEOREM

A mathematical formulation that determines the probability that a specific result was due to a particular cause, if multiple possible causes exist. For example, a molecular database consists of 50% synthetic reagents, 30% drug-like molecules and 20% natural products. If the activity rates of synthetic compounds, drug-like molecules and natural products are 1%, 50% and 15%, respectively, what is the probability that a given biological activity in this database is represented by a natural product?

SIMILARITY PARADOX

In the context of virtual screening (VS), minor chemical modifications of otherwise similar molecules can render them either active or inactive. VS calculations are expected to identify series of molecules that share the same scaffold. However, if only inactive compounds were selected for testing, VS analysis would have 'failed', although a relevant chemotype was identified. This highlights potential problems associated with the selection of only one or a few representative molecules from a series of similar ones.

ANALOGUE

A member of a series of closely related molecules that has only minor chemical modifications that distinguish it from others belonging to this chemotype. Analogues of active molecules are often generated to improve potency and/or other compound characteristics, such as solubility or oral availability.

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Bajorath, J. Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1, 882–894 (2002). https://doi.org/10.1038/nrd941

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