Literature DB >> 26126976

The challenges of making decisions using uncertain data.

Matthew D Segall1, Edmund J Champness2.   

Abstract

All of the experimental compound data with which we work have significant uncertainties, due to imperfect correlations between experimental systems and the ultimate in vivo properties of compounds and the inherent variability in experimental conditions. When using these data to make decisions, it is essential that these uncertainties are taken into account to avoid making inappropriate decisions in the selection of compounds, which can lead to wasted effort and missed opportunities. In this paper we will consider approaches to rigorously account for uncertainties when selecting between compounds or assessing compounds against a property criterion; first for an individual measurement of a single property and then for multiple measurements of a property for the same compound. We will then explore how uncertainties in multiple properties can be combined when assessing compounds against a profile of criteria, a process known as multi-parameter optimisation. This guides rigorous decision-making using complex, uncertain data to focus on compounds with the best chance of success, while avoiding missed opportunities by inappropriately rejecting compounds.

Keywords:  Compound optimisation; Desirability function; Drug discovery; Multi-parameter optimisation; Probability; Uncertainty

Mesh:

Year:  2015        PMID: 26126976     DOI: 10.1007/s10822-015-9855-2

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  9 in total

Review 1.  Multi-parameter optimization: identifying high quality compounds with a balance of properties.

Authors:  Matthew D Segall
Journal:  Curr Pharm Des       Date:  2012       Impact factor: 3.116

Review 2.  Overcoming psychological barriers to good discovery decisions.

Authors:  Andrew T Chadwick; Matthew D Segall
Journal:  Drug Discov Today       Date:  2010-05-27       Impact factor: 7.851

3.  Making priors a priority.

Authors:  Matthew Segall; Andrew Chadwick
Journal:  J Comput Aided Mol Des       Date:  2010-10-16       Impact factor: 3.686

4.  Ligand efficiency indices as guideposts for drug discovery.

Authors:  Cele Abad-Zapatero; James T Metz
Journal:  Drug Discov Today       Date:  2005-04-01       Impact factor: 7.851

Review 5.  Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery.

Authors:  Matt D Segall; Alan P Beresford; Joelle Mr Gola; Dan Hawksley; Mike H Tarbit
Journal:  Expert Opin Drug Metab Toxicol       Date:  2006-04       Impact factor: 4.481

6.  Judgment under Uncertainty: Heuristics and Biases.

Authors:  A Tversky; D Kahneman
Journal:  Science       Date:  1974-09-27       Impact factor: 47.728

Review 7.  Advances in multiparameter optimization methods for de novo drug design.

Authors:  Matthew Segall
Journal:  Expert Opin Drug Discov       Date:  2014-05-03       Impact factor: 6.098

8.  Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability.

Authors:  I J Hidalgo; T J Raub; R T Borchardt
Journal:  Gastroenterology       Date:  1989-03       Impact factor: 22.682

9.  MDCK (Madin-Darby canine kidney) cells: A tool for membrane permeability screening.

Authors:  J D Irvine; L Takahashi; K Lockhart; J Cheong; J W Tolan; H E Selick; J R Grove
Journal:  J Pharm Sci       Date:  1999-01       Impact factor: 3.534

  9 in total
  1 in total

Review 1.  From flamingo dance to (desirable) drug discovery: a nature-inspired approach.

Authors:  Aminael Sánchez-Rodríguez; Yunierkis Pérez-Castillo; Stephan C Schürer; Orazio Nicolotti; Giuseppe Felice Mangiatordi; Fernanda Borges; M Natalia D S Cordeiro; Eduardo Tejera; José L Medina-Franco; Maykel Cruz-Monteagudo
Journal:  Drug Discov Today       Date:  2017-06-15       Impact factor: 7.851

  1 in total

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