- Commentary
- Open Access
Reply to the comment made by Šicho, Vorśilák and Svozil on ‘The Power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability’
- Hans De Winter1Email authorView ORCID ID profile and
- Julio Cesar Dias Lopes1
- Received: 13 January 2018
- Accepted: 15 February 2018
- Published: 15 March 2018
The original article was published in Journal of Cheminformatics 2018 10:13
The authors of the comment [1] raised an interesting remark about the relation between the power metric (PM) [2] and the precision metric (PR), also known as the positive predictive value (PPV).
- (a)in EF, χ is the fraction of compounds selected (χ = N s /N), related to the number of true and false positives (TP and FP):$$\chi = \frac{TP + FP}{N}$$(7)
- (b)in ROCE, χ can be related to the fraction of inactive instances wrongly classified as positives:$$\chi = FPR = \frac{FP}{{n_{i} }}$$(8)
- (c)in PM, χ can be related to the sum of the true and false positive rates:$$\chi = TPR + FPR = \frac{TP}{{n_{a} }} + \frac{FP}{{n_{i} }}$$(9)
Due to these characteristics all these metrics are interconvertible.
In addition, using the number of actives and inactives, all values of TP, FP, TN (true negatives) and FN (false negatives) can be calculated, and from these values any metric can be derived.
The fact that all these metrics are functionally related to the precision metric do not invalidated them as being useful metrics (‘not suitable for performance assessment’, as stated by the authors of the comment). All these metrics have their scopes, strengths and weaknesses. Each one has its meaning and can be used by the user depending on the desired aims. For example, the precision or EF metrics might be more appropriate if the user is more concerned about false positives, while in applications with more emphasis on true positive rates the PM or ROCE metrics would be recommended instead.
This leads us to the interpretation of the PM as the fraction of active compounds that are correctly predicted in relation to the maximum fraction of active compounds that could be recovered at the chosen threshold χ, or, in other words, PM express the probability of an active compound to be correctly classified.
Notes
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Authors’ contributions
HDW and JCDL wrote, reviewed and edited the manuscript. Both authors read and approved the final manuscript.
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References
- Svozil D, Šícho M, Voršilák M (2018) Comment on “The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability”. J Cheminf. https://doi.org/10.1186/s13321-018-0267-x Google Scholar
- Lopes JCD, Dos Santos FM, Martins-José A, Augustyns K, De Winter H (2017) The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability. J Cheminform 9:7View ArticleGoogle Scholar