Sebastian Böcker

Every (put a large number here) days I stumble over the question, “how large is the universe of small molecules?” in a publication or a blog. Not the number of small molecules that we already “know” – those are covered in databases such as HMDB, PubChem or ZINC. (Although many compounds in PubChem etc. are purely hypothetical, too.) Not the number of metabolites that actually exist in nature. Also, we do not care if these small molecules are synthesizable with current technology. Rather, we want to know: How large is the space of small molecules that could exist? These could be natural small molecules (natural products, secondary metabolites) or synthetic compounds. I usually do not like the numbers that people report as “the truth”, as one estimate was taken out of context and started to develop a life of its own. Hence, I thought I write down what I know.

Obviously, we have to first define precisely what we actually counting. Since the problem is already hard enough, we usually do not care about 3d considerations (usually including stereochemistry), and simply ask for the number of molecular graphs (chemical 2d structures). On the one hand, resulting estimates are a lower bound, as adding stereochemistry into the mix results in more structures; on the other hand, resulting estimates are upper bounds, in the sense that many molecular graphs that we draw, cannot exist in 3d space. For the sake of simplicity, let’s forget about this problem.

Next, we have to define what a “small molecule” actually is. Usually, we simply apply a threshold on the mass of the molecule, such as “all molecules below 1000 Dalton“. If you prefer, you may instead think of “molecular weight”; this is the expected mass of the molecule, taken over all isotopologues and, hence, slightly larger. Yet, people usually consider mass in these considerations. Maybe, because mass spectrometry is one of the (maybe, the) most important experimental technology for the analysis of small molecules; maybe, because isotopes make a big difference for small molecules. Since our bounds on mass are somewhat arbitrary, anyways, this is not a big problem: Why exactly 1000 Dalton? Is prymnesin-B1 a small molecules? It is not a peptide, not a lipid, not a macromolecule, and it sure looks like a biological small molecule; it is just “too heavy” (1819.5 Dalton). For our calculations we will stick with the mass of 1000 Dalton; yet, it all still applies when you want to use a different threshold, and doing so is actually very simple, see below.

Now, the number of small molecules that people are reporting and repeating, making this number “more and more true” (to the point that Google Gemini will return that number as the correct answer when you query with Google) comes from a paper by Bohacek et al. (1997). The caption of Figure 6 contains the sentence “A subset of molecules containing up to 30 C, N, O, and S atoms may have more than 10⁶⁰ members”. The authors make some back-of-the-envelope calculations in a long footnote, to further support this claim. The authors clearly state that the actual numbers they are giving are incorrect, for each step of their calculations. These calculations are merely meant to demonstrate that the true number is enormously huge; and, that the actual number is not that important, and might never be known to mankind. Unfortunately, many readers did not grasp the second part of what the authors were saying and instead, starting reporting the number “10⁶⁰ small molecules” as if this number had been calculated for good. From there, it was only a small step into a Science/Nature news (I have to search for it), publications, websites, and finally, the “knowledge” of a large language model such as Gemini. The Wikipedia article about Chemical Space is much more cautious, and also mentions the 500 Dalton cutoff that Bohacek et al. assumed, as they were interested in pharmacologically active molecules. Funnily, Google Gemini does not link to the Wikipedia article, but rather to a “random” website that cites the number of Bohacek et al..

Let us see if we can do better than that. First, we look at a few things that are known. The On-Line Encyclopedia of Integer Sequences (OEIS) gives us loads of helpful numbers:

• Alkanes are hydrocarbons (carbohydrates with no oxygen) with the “minimal” molecular formula C_n H_2n+2 and have no multiple bonds, a tree structure, and no cycles. The number of alkanes can be found in sequence A000628, see also here. Yet, this sequence counts each individual stereoisomer, different from what we said above. The sequence A000602 counts different stereoisomers as identical. For upper bound 1000 Dalton we use n = 71 (nominal mass 996 Dalton), and this is 1 281 151 315 764 638 215 613 845 510 structures, about 1.28 · 10²⁷, or 1.28 octillion. Clearly, not all of them are possible; yet, the number of of “impossible” structures may be smaller than one may think. Alkanes are small molecules, so this is a lower bound on the number of small molecules of this mass.
• In fact, we can approximate – in the colloquial and in the mathematical sense – this number using the closed formula (i.e. the formula has no recurrences)
  0.6563186958 · n^-5/2 · 2.815460033ⁿ,
  see Table 3 and equation (16) here (reprint here). For n = 71 this approximation is 1.279 · 10²⁷, which is already pretty close to the true (exact) number. Importantly, this allows us to classify the growth as exponential, meaning that increasing n by one, we have to multiply the number of molecules by a constant (roughly 2.8). We can safely ignore the non-exponential part of the equation, as its influence will get smaller and smaller when n increases. Exponential growth might appear frightening, and in most application domains it would be our enemy; but for counting small molecules, it is actually our friend, as we will see next.
• Molecules are no trees, they have cycles. So, another idea to get an idea on the number of small molecules is to count simple, connected graphs. “Simple” means that we do not allow multiple edges between two nodes (vertices). We have different types of bounds (single, double, etc.) but multi-edges means that there can be 1000 edges between two nodes, and that is definitely not what we want. We also ignore that we have different elements, that is, labeled nodes. This number is found in sequence A001349. This number growths super-exponential, meaning that it growths faster (for n to infinity) than any exponential function. That is fast, and we indeed observe that already for reasonable number of nodes, numbers become huge: For n = 19 nodes it is 645 465 483 198 722 799 426 731 128 794 502 283 004 (6.45 · 10³⁸) simple graphs; for n = 49 we already have 1.69 · 10²⁹¹ simple graphs. That is a lot, compare to the number of alkanes. Even more intimidating is the growth: Going from n = 49 to n = 50 nodes we reach 1.89 x 10³⁰⁴ simple graphs, a factor of 11 258 999 068 424 (11 trillion) for a single node added. Suddenly, factor 2.8 does not look that bad any more, does it?
• Now, one can easily argue that simple graphs are too general: The number of edges incident to (touching) a node is not restricted, whereas it is clearly restricted for molecular graphs. For simplicity, let us consider Carbon-only molecules; carbon has valence 4. Let us further simplify things by only considering cases where each node is connected to exactly 4 other nodes (chemically speaking, no hydrogen atoms, no double bonds). The number of 4-regular, simple, connected graphs is sequence A006820. Given that there is a single such graph for n = 6, it might be surprising how quickly numbers are increasing even for moderate number of nodes n: For n = 28 there are 567 437 240 683 788 292 989 (5.67 · 10²⁰) such graphs.

All of these numbers give us a first impression of the number of small molecules we could consider. Yet, none of them give us any precise number to work with: The number of alkanes is just a lower bound, the number of simple graphs is too large, and the number of 4-regular simple graphs is simultaneously too large (we cannot simply demand that two Carbon atoms are connected, we somehow have to realize that in 3d) and too small (4-regular, Carbon-only). That is not very satisfactory. Yet, we can already learn a few important things:

• The coefficient c before the 10^k is actually not very important. If we are dealing with numbers as large as 10¹⁰⁰, then a mere factor of below ten does not make a substantial difference. It is the exponent that is relevant. Similarly, it does not make a relevant change if we think about mass or nominal mass.
• If we have exponential growth, as we did for alkanes, then it is straightforward to calculate your own estimate in case you are unhappy with the upper bound on mass of 1000 Dalton. Say, you prefer 500 Dalton; then, the estimate for the number of alkanes goes from 10²⁷ to 10^27/2 = 3.16 · 10¹³. Similarly, for 1500 Dalton, we reach 10^27·1.5 = 3.16 · 10⁴⁰ alkanes.

Yet, we can also look at the problem empirically: There exist different methods (the commercial MOLGEN, OMG, MAYGEN, and currently the fastest-tool-in-town, Surge) to generate molecular structures for a given molecular formula. For example, Ruddigkeit et al. generated “all” small molecule structures with up to 17 heavy atoms (CNOS and halogens). I put the “all” in apostrophes because the authors used highly aggressive filters (ad hoc rules on, does a molecule make sense chemically? etc.) to keep the numbers small. With that, they generated the GDB-17 database containing 166 443 860 262 (1.66 · 10¹¹) molecular structures. Unfortunately, only about 0.06% of that databases are publicly available. Also, the aggressive filtering is diametral to our question, how many molecular structures exit.

More interesting for us is a paper from 2005: That year, Kerber et al. published the paper “Molecules in silico: potential versus known organic compounds“. For the paper, the authors generated all small molecule structures with nominal mass up to 150 Dalton, over the set of elements CHNO, using MOLGEN. For example, MOLGEN constructs 615 977 591 molecular structures for mass 150 Dalton. This paper also contains the highly informative Figure 1, where the authors plot the number of (theoretical) molecular structure against nominal mass m. Looking at the plot, I could not resist to see exponential growth, which (if you use a logarithmic y-axis) comes out as a simple line. Remember that exponential growth is our friend.

So, let’s do that formally: I fitted a regression line to the empirical numbers (see Chapter 10 here), and what comes out of it is that we can approximate the number of small molecules between 80 and 150 Dalton as 2.0668257909 · 10⁻⁴ · 1.2106212044^m. Be warned that there is not the slightest guarantee that growth will continue like that for masses larger than 150 Dalton; it is highly unlikely that growth might slow down, but given that the number of simple graphs is growing superexponentially, maybe the same is true here? I cannot tell you, I am not that much into counting and enumerating graphs. But in any case, my Milchmädchenrechnung should make a much better estimate than anything we have done so far; to be precise, much better than anything else I have ever heard of. For nominal mass 1000 Dalton this number is 2.11 · 10⁷⁹ molecular structures.

But wait! The above formula tells us the (very approximate) number of all small molecules with mass exactly 1000 Dalton; what we want to know is the number of molecules with mass up to 1000 Dalton. Fun fact about exponential growth: These numbers are basically the same, with a small factor. Recall that the number of molecules with mass m equals a · x^m. Pretty much every math student has to prove, during his first year, the equation

To this end, if we want to know the number of molecules with mass up to 1000 Dalton, we simply use the number of molecules with mass exactly 1000 Dalton, and apply a small correction. In detail, the number of molecules with mass up to 1000 Dalton is (almost exactly) 1.21062 / (1.21062 – 1) = 5.74786 times the number of molecules with mass exactly 1000 Dalton. We reach an estimate of 1.21 · 10⁸⁰ small molecules over elements CHNO with mass up to 1000 Dalton. For mass up to 500 Dalton it is about 10⁴⁰ molecules (which tells us that the inital estimate was rather far off, and I do not expect that even a superexponential growth could correct that); for mass up to 1500 Dalton it is 10¹²⁰ small molecules.

But, alas, this estimate is for elements CHNO only; what if we add more elements to the mix? For halogens it is mainly fluorine that will substantially increase the number of molecular structures: Halogens are much heavier than hydrogen. If we replace a single H by F in a molecular structure, then the nominal mass of the molecule increases by 8. The above estimate tells us that there are only 1/4.61 = 0.216 times the molecular structures to consider if we decrease the mass by 8. On the “plus side” we can replace any H by F to reach a different molecule, if we gallantly ignore the problem of isomorphic graph structures. For more than one fluorine, the combinatorics of where we can add them growths polynomially; but at the same time, the number of structures we start from decreases exponentially. Hence, one should not expect a substantially higher number if we add fluorine to the mix; and an even smaller effect for the other halogens. More worrying, from a combinatorial standpoint, are the elements sulfur and phosporus. In particular sulfur with valence 6 could wreak havoc of our estimates. I don’t know what happens if you allow an arbitrary number of S and P; I also don’t know if we would even want such all-sulfur molecules. The only number that I can report is again from the Kerber et al. paper: They also generated all molecular structures for nominal mass 150 Dalton and elements CHNOSiPSFClBrI, using the (unrealistic) fixed valences 3 for phosphorus and 2 for sulfur, resulting in 1 052 647 246 structures, compared to 615 977 591 molecular structures over elements CHNO. Yet, this small increase in structures (merely doubled, phew) should not lull us into a false sense of security: Not only the unrealistic valences, even more so the relatively small mass could mean that in truth, the number of structures with sulfur and phosphorus is substantially higher. I suggest we stick with the number 10⁸⁰ and in the back of our minds, keep the note “it is likely even worse”.

It is not easy to grasp a number as large as 10⁸⁰, so a few words to get a feeling:

• The number 10⁸⁰ is 100 septemvigintillion, or 100 million billion billion billion billion billion billion billion billions. If we have a name for it, it cannot be that bad.
• The observable universe contains an estimated 10⁸⁰ atoms. If we had the technology to build a computer that uses every single atom of the observable universe as its memory, and where we need only a single atom to store the complete structure of a small molecule, then the observable universe would be “just right” to store all small molecules up to 1000 Dalton.
• Using the same technology, molecules up to 1010 Dalton would be absolutely impossible to store, and 1100 Dalton would be absurd. For molecules up to 2000 Dalton you would need 10⁸⁰ observable universes. Alternatively, a technology that puts a complete universe into a single atom, and does so for each and every atom of this universe. See the final scene of Men in Black.
• For comparison, the total amount of storage on planet Earth is, at the moment, something like 200 zettabytes, mostly on magnetic tapes. If we could store every molecular structure in 2 bytes, this would allow us to store 10²³ molecular structures. If we could now again fold all the storage into a single byte, and again, and again, we reach – after four iterations of folding – the required memory.
• If we have a computer enumerating molecular structures at a rate of 1 billion structures per second, then we could enumerate 3.15 · 10¹⁶ structures per year, and it would take 3.17 · 10⁶³ years to enumerate them all. For comparison, the estimated age of the universe is 1.38 · 10¹⁰ years. We would need more than 10⁵⁰ universes to finish our calculations, where in each universe we have to start our computations with the big bang.
• Maybe, it helps if we use all computers that exist on planet Earth? Not really; there are maybe 10 billion computers (including smartphones) on Earth, but let’s make it 1000 billion, to be on the safe side. The required time to enumerate all structures drops to 3.17 · 10⁵¹ years.
• But maybe, we can use a quantum computer? Grover’s algorithm brings down the running times to if we have to consider n objects. Again assuming that our quantum computer could do 1 billion such computations per second, we would end up with a running time of 10³¹ seconds, or 3.17 · 10²³ years. That is still a looong time. There might be smarter ways of processing all structures on a quantum computer than using Grover’s algorithm; but so far, few such algorithms for even fewer problems have been found.

Now, 10⁸⁰ is a large number, and you might thing, “this is the reason why dealing with small molecules is so complicated!” But that is a misconception. It is trivial to find much larger numbers that a computer scientist has to deal with every day; just a few examples from bioinformatics: The number of protein sequences of length 100 is 1.26 · 10¹³⁰.  Yet, we can easily work with longer protein sequences. The number of pairwise alignments for two sequences of length 1000 is 2.07 · 10⁶⁰⁰. Yet, every bioinformatics student learns during his/her first year how to find the optimal alignment between two sequences, sequences that may be much longer than 1000 bases. The thing is, sequences/strings are simple structures, and we have learned to look only for the interesting, i.e. optimal alignments. (Discrete optimization is great!) What makes dealing with small molecule structures complicated is that these structures are themselves rather complicated (graphs, when even trees can be a horror, ask someone from phylogenetics) plus at the same time, there are so many, which prevents us from simply enumerating them all, then doing the complicated stuff for every structure.

That’s all, folks! Hope it helps.

Hi all, I thought I put my blog post (singular) into its own category. Not sure about the title, that may change. Why another blog when there is already this great blog on metabolomics and mass spectrometry? Because here I may talk about computational stuff as well, such as failed evaluations of machine learning methods (that one never gets old). I hope to write something once every four fortnights, let’s see how that works out. :shrug:

I recently gave a talk at the conference of the Metabolomics society. After the talk, Oliver Fiehn asked, “when will we finally have FDR estimation in metabolite annotation?” This is a good question: False Discovery Rates (FDR) and FDR estimation have been tremendously helpful in genomics, transcriptomics and proteomics. In particular, FDRs have been extremely helpful for peptide annotation in shotgun proteomics. Hence, when will FDR estimation finally become a reality for metabolite annotation from tandem mass spectrometry (MS/MS) data? After all, the experimental setup of shotgun proteomics and untargeted metabolomics using Liquid Chromatography (LC) for separation, are highly similar. By the way, if I talk about metabolites below, this is meant to also include other small molecules of biological interest. From the computational method standpoint, they are indistinguishable.

My answer to this question was one word: “Never.” I was about to give a detailed explanation for this one-word answer, but before I could say a second word, the session chair said, “it is always good to close the session on a high note, so let us stop here!” and ended the session. (Well played, Tim Ebbels.) But this is not how I wanted to answer this question! In fact, I have been thinking about this question for several years now; as noted above, I believe that it is an excellent question. Hence, it might be time share my thoughts on the topic. Happy to hear yours! I will start off with the basics; if you are familiar with the concepts, you can jump to the last five paragraphs of this text.

The question that we want to answer is as follows: We are given a bag of metabolite annotations from an LC-MS/MS run. For every query spectrum, this is the best fit (best-scoring candidate) from the searched database, and will be called “hit” in the following. Can we estimate the fraction of hits that are incorrect? Can we do so for a subset of hits? More precisely, we will sort annotations by some score, such as the cosine similarity for spectral library search. If we only accept those hits with score above (or below, it does not matter) a certain threshold, can we estimate the ratio of incorrect hits for this particular subset? In practice, the user selects an arbitrary FDR threshold (say, one percent), and we then find the smallest score threshold so that the hits with score above the score threshold have an estimated FDR below or equal to the FDR threshold. (Yes, there are two thresholds, but we can handle that.)

Let us start with the basic definition. For a given set of annotations, the False Discovery Rate (FDR) is the number of incorrect annotations, divided by the total number of annotations. FDR is usually recorded as a percentage. To compute FDR, you have to have complete knowledge; you can only compute it if you know upfront which annotations are correct and which are incorrect. Sad but true. This value is often referred to as “exact FDR” or “true FDR”, to distinguish it from the estimates we want to determine below. Obviously, you can compute exact FDR for metabolite annotations, too; the downside is that you need complete knowledge. Hence, this insight is basically useless, unless your are some demigod or all-knowing demon. For us puny humans, we do not know upfront which annotations are correct and which are incorrect. The whole concept of FDR and FDR estimation would be useless if we knew: If we knew, we could simply discard the incorrect hits and continue to work with the correct ones.

To this end, a method for FDR estimation tries to estimate the FDR of a given set of annotations, without having complete knowledge. It is important to understand the part that says, “tries to estimate”. Just because a method claims it is estimating FDR, does not mean it is doing a good job, or even anything useful. For example, consider a random number generator that outputs random values between 0 and 1: This IS an FDR estimation method. It is neither accurate nor useful, but that is not required in the definition. Also, a method for FDR estimation may always output the same, fixed number (say, always-0 or always-1). Again, this is a method for FDR estimation; again, it is neither accurate nor useful. Hence, be careful with papers that claim to introduce an FDR estimation method, but fail to demonstrate that these estimates are accurate or at least useful.

But how can we demonstrate that a method for FDR estimation is accurate or useful? Doing so is somewhat involved because FDR estimates are statistical measures, and in theory, we can only ask if they are accurate for the expected value of the estimate. Yet, if someone presents a novel methods for FDR estimation, the minimum to ask for is a series of q-value plots that compare estimated q-values and exact q-values: A q-value is the smallest FDR for which a particular hit is part of the output. Also, you might want to see the distribution of p-values, which should be uniform. You probably know p-values; if you can estimate FDR, chances are high that you can also estimate p-values. Both evaluations should be done for multiple datasets, to deal with the stochastic nature of FDR estimation. Also, since you have to compute exact FDRs, the evaluation must be done for reference datasets where the true answer is known. Do not confuse “the true answer is known” and “the true answer is known to the method“; obviously, we do not tell our method for FDR estimation what the true answer is. If a paper introduces “a method for FDR estimation” but fails to present convincing evaluations that the estimated FDRs are accurate or at least useful, then you should be extremely careful.

Now, how does FDR estimation work in practice? In the following, I will concentrate on shotgun proteomics and peptide annotation, because this task is most similar to metabolomics. There, target-decoy methods have been tremendously successful: You transform the original database you search in (called target database in the following) into a second database that contains only candidates that are incorrect. This is the decoy database The trick is to make the candidates from the decoy database “indistinguishable” from those in the target database, as further described below. In shotgun proteomics, it is surprisingly easy to generate a useful decoy database: For every peptide in the target database, you generate a peptide in the decoy database for which you read the amino acid sequence from back to front. (In more detail, you leave the last amino acid of the peptide untouched, for reasons that are beyond what I want to discuss here.)

To serve its purpose, a decoy database must fulfill three conditions: (i) There must be no overlap between target and decoy database; (ii) all candidates from the decoy database must never be the correct answer; and (iii), false hits in the target database have the same probability to show up as (always false) hits from the decoy database. For (i) and (ii), we can be relatively relaxed: We can interpret “no overlap” as “no substantial overlap”. This will introduce a tiny error in our FDR estimation, which is presumably irrelevant in comparison to the error that is an inevitable part of FDR estimation. For (ii), this means that whenever a search with a query spectrum returns a candidate from the target database, this is definitely not the correct answer. The most important condition is (iii), and if we look more precisely, we will even notice that we have to demand more: That is, the score distribution of false hits from the target database is identical to the score distribution of hits from the decoy database. If our decoy database fulfills all three conditions, then we can use a method such as Target-Decoy Competition and utilize hits from the decoy database to estimate the ration of incorrect hits from the target database. Very elegant in its simplicity.

Enough of the details, let us talk about untargeted metabolomics! Can we generate a decoy database that fulfills the three requirements? Well — it is difficult, and you might think that this is why I argue that FDR estimation is impossible here: Looks like we need a method to produce metabolite structure that look like true metabolites (or, more generally, small molecules of biological interest — it does not matter). Wow, that is already hard — we cannot simply use random molecular structure, because they will not look like the real thing, see (iii). In fact, a lot of research is currently thrown at this problem, as it would potentially allow us to find the next super-drug. Also, how could we guarantee not to accidentally generate a true metabolite, see (ii)? Next, looks like we need a method to simulate a mass spectrum for a given (arbitrary) molecular structure. Oopsie daisies, that is also pretty hard! Again, ongoing research, far from being solved, loads of papers, even a NeurIPS competition coming up.

So, are these the problems we have to solve to do FDR estimation, and since we cannot do those, we also cannot do FDR? In other words, if — in a decade or so — we would finally have a method that can produce decoy molecular structures, and another method that simulates high-quality mass spectra, would we have FDR estimation? Unfortunately, the answer is, No. In fact, we do not even need those methods: In 2017, my lab developed a computational method that transforms a target spectral library into a decoy spectral library, completely avoiding the nasty pitfalls of generating decoy structures and simulating mass spectra. Also, other computational methods (say, naive Bayes) completely avoid generating a decoy database.

The true problem is that we are trying to solve a non-statistical problem with a statistical measure, and that is not going to work, no matter how much we think about the problem. I repeat the most important sentence from above: “False hits in the target database have the same probability to show up as hits from the decoy database.” This sentence, and all stochastic procedure for FDR estimation, assume that a false hit in the target database is something  random. In shotgun proteomics, this is a reasonable assumption: The inverted peptides we used as decoys basically look random, and so do all false hits. The space of possible peptides is massive, and the target peptides lie almost perfectly separated in this ocean of non-occurring peptides. Biological peptides are sparse and well-separated, so to say. But this is not the case for metabolomics and other molecules of biological interest. If an organism has learned how to make a particular compound, it will often also be able to synthesize numerous compounds that are structurally extremely similar. A single hydrogen replaced by a hydroxy group, or vice versa. A hydroxy group “moving by a single carbon atom”. Everybody who has ever looked at small molecules, will have noticed that. Organisms invest a lot of energy to make proteins for exactly this purpose. But this is not only a single organism; the same happens in biological communities, such as the microbiota in our intestines or on our skin. It even happens when no organisms are around. In short: No metabolite is an island.

Sadly, this is the end of the story. Let us assume that we have identified small molecule A for a query MS/MS when in truth, the answer should be B. In untargeted metabolomics and related fields, this usually means that A and B are structurally highly similar, maybe to the point of a “moving hydroxy group”. Both A and B are valid metabolites. There is nothing random or stochastic about this incorrect annotation. Maybe, the correct answer B was not even in the database we searched in; potentially, because it is a “novel metabolite” currently not known to mankind. Alternatively, both compounds were in the database, and our scoring function for deciding on the best candidate simply did not return the correct answer. This will happen, inevitably: Otherwise, you again need a demigod or demon to build the scoring function. Consequently, speaking about the probability of an incorrect hit in the target database, cannot catch the non-random part of such incorrect hits. There is no way to come up with an FDR estimation method that is accurate, because the process itself is not stochastic. Maybe, some statisticians will develop a better solution some day, but I argue that there is no way to ever “solve it for good”, given our incomplete knowledge of what novel metabolites remain to be found out there.

Similar arguments, by the way, hold true for the field of shotgun metaproteomics: There, our database contains peptides from multiple organisms. Due to homologous peptide sequences in different organisms, incorrect hits are often not random. In particular, there is a good chance that if PEPTIDE is in your database, then so is PEPITDE. Worse, one can be in the database you search and one in your biological sample. I refrain from discussing further details; after all, we are talking about metabolites here.

But “hey!”, you might say, “Sebastian, you have published methods for FDR estimation yourself!” Well, that is true: Beyond the 2017 paper mentioned above, the confidence score of COSMIC is basically trying to estimate the Posterior Error Probability of a hit, which is a statistical measure again closely related to FDRs and FDR estimation. Well, you thought you got me there, did you? Yet, do you remember that above, I talked about FDR estimation methods that are accurate or useful? The thing is: FDR estimation methods from shotgun proteomics are enviably accurate, with precise estimates at FDR 1% and below. Yet, even if our FDR estimation methods in metabolomics can never be as accurate as those from shotgun proteomics, that does not mean they cannot be useful! We simply have to accept the fact that our numbers will not be as accurate, and that we have to interpret those numbers with a grain of salt.

The thing is, ballpark estimates can be helpful, too. Assume you have to jump, in complete darkness, into a natural pool of water below. I once did that, in a cave in New Zealand. It was called “cave rafting”, not sure if they still offer that type of organized madness. (Just checked, it does, highly recommended.) Back then, in almost complete darkness, our guide told me to jump; that I would fall for about a meter, and that the water below was two meters deep. I found this information to be extremely reassuring and helpful, but I doubt it was very accurate. I did not do exact measurements after the jump, but it is possible that I fell for only 85cm; possibly, the water was 3m deep. Yet, what I really, really wanted to know at that moment, was: Is it an 8m jump into a 30cm pond? I would say the guide did a good job. His estimates were useful.

I stress that my arguments should not be taken as an excuse to do a lazy evaluation. Au contraire! As a field, we must insist that all methods marketed as FDR estimation methods, are evaluated extensively as such. FDR estimations should be as precise as possible, and evaluations as described above are mandatory. Because only this can tell us how for we can trust the estimates. Because only this can convince us that estimates are indeed useful. Trying to come up with more accurate FDR estimates is a very, very challenging task, and trying to do so may be in vain. But remember: We choose to do these things, not because they are easy, but because they are hard.

I got the impression that few people in untargeted metabolomics and related fields are familiar with the concept of FDR and FDR estimation for annotation. This strikes me as strange, given the success of these concepts in other OMICS fields. If you want to learn more, I do not have the perfect link or video for you, but I tried my best to explain it in my videos about COSMIC, see here. If you have a better introduction, let me know!

Funny side note: If you were using a metascore, your FDR estimates from target-decoy competition would always be 0%. As noted, a method for FDR estimation must not return accurate or useful values, and a broken scoring can easily also break FDR estimation. Party on!