Automated Inference of Chemical Discriminants of Biological Activity

  • Sebastian Raschka
  • Anne M. Scott
  • Mar Huertas
  • Weiming Li
  • Leslie A. Kuhn
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1762)

Abstract

Ligand-based virtual screening has become a standard technique for the efficient discovery of bioactive small molecules. Following assays to determine the activity of compounds selected by virtual screening, or other approaches in which dozens to thousands of molecules have been tested, machine learning techniques make it straightforward to discover the patterns of chemical groups that correlate with the desired biological activity. Defining the chemical features that generate activity can be used to guide the selection of molecules for subsequent rounds of screening and assaying, as well as help design new, more active molecules for organic synthesis.

The quantitative structure–activity relationship machine learning protocols we describe here, using decision trees, random forests, and sequential feature selection, take as input the chemical structure of a single, known active small molecule (e.g., an inhibitor, agonist, or substrate) for comparison with the structure of each tested molecule. Knowledge of the atomic structure of the protein target and its interactions with the active compound are not required. These protocols can be modified and applied to any data set that consists of a series of measured structural, chemical, or other features for each tested molecule, along with the experimentally measured value of the response variable you would like to predict or optimize for your project, for instance, inhibitory activity in a biological assay or ΔGbinding. To illustrate the use of different machine learning algorithms, we step through the analysis of a dataset of inhibitor candidates from virtual screening that were tested recently for their ability to inhibit GPCR-mediated signaling in a vertebrate.

Key words

Fingerprint analysis GPCR Invasive species control Ligand-based screening Machine learning Pharmacophore Quantitative structure–activity relationship Random forest Virtual screening 

Abbreviations

2D

Two-dimensional

3D

Three-dimensional

3kPZS

3-keto petromyzonol sulfate

CAS

Chemical Abstracts Service Registry

CSD

Cambridge Structural Database

DKPES

3,12-diketo-4,6-petromyzonene-24-sulfate

EOG

Electro-olfactogram

GPCR

G protein-coupled receptor

QSAR

Quantitative structure–activity relationship

SBS

Sequential backward selection

SFS

Sequential feature selection

VS

Virtual screening

ZINC12

Zinc Is Not Commercial database, version 12

Notes

Acknowledgments

This research was supported by funding from the Great Lakes Fishery Commission from 2012 to 2017 (Project ID: 2015_KUH_54031). We gratefully acknowledge OpenEye Scientific Software (Santa Fe, NM) for providing academic licenses for the use of their ROCS, Omega, QUACPAC (molcharge), and OEChem toolkit software. We also wish to express our special appreciation to the open source community for developing and sharing the freely accessible Python libraries for data processing, machine learning, and plotting that were used for the data analysis presented in this chapter.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sebastian Raschka
    • 1
  • Anne M. Scott
    • 2
  • Mar Huertas
    • 2
    • 3
  • Weiming Li
    • 2
  • Leslie A. Kuhn
    • 1
    • 2
    • 4
  1. 1.Department of Biochemistry and Molecular Biology Michigan State UniversityEast LansingUSA
  2. 2.Department of Fisheries and WildlifeMichigan State UniversityEast LansingUSA
  3. 3.Department of BiologyTexas State UniversitySan MarcosUSA
  4. 4.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

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