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11 Ultra-Large-Scale Virtual Screening 325
Finally, some recent work revived more classical models: For instance, the Lean
docking protocol by Berenger et al. [84] lowers the required docking by about 75%
with the help of a linear support vector regressor. Training takes only about 80 s on a
single core, and the trained model achieves a throughput of around 5800 compounds
per second per computing core. However, Lean docking has so far not been applied
to an ultra-large docking scenario. The recent work of Marin et al. [85] relies on
ensembles of fingerprint-based linear regression models to accelerate docking-based
VS in one-shot and active learning scenarios. Their study utilized the previously
published 99 million AmpC dataset [17]. While recent MPNN-based methods
achieved higher recalls, their linear regressor ensemble surpassed the performance
of more shallow approaches like RF models. Additionally, those simple models are
often several times faster in training and prediction cycles than more advanced ML
and DL approaches, although their memory footprint remains a potential bottleneck
[85]. Importantly, these examples highlight that, while not necessarily with optimal
results, ultra-large VS efforts can be feasible in settings with limited access to
computational resources and, in particular, GPUs, when relying upon more simple
models.
Table 11.5 summarizes implementation and training strategies of the discussed
ultralarge ML- and DL-boosted screening approaches. Table 11.6 additionally
summarizes application examples on the ultra-large scale. Repositories of opensource tools are summarized in Table 11.10 in the Appendix.
It is important to note that all tools aim to partially replace brute-force docking
with a faster stand-in. At the same time, the approaches do not modify the docking
and scoring procedure itself. Generally, these strategies rely on the ability of the
chosen docking approach to achieve a meaningful enrichment in true actives among
the top-scoring compounds. Therefore, the same validation measures that apply for
default brute-force docking should be taken into account when utilizing
ML-accelerated docking whenever possible (see, e.g., Chap. 7 for detailed validation
procedures).
Most approaches discussed herein abstain from exhaustive (hyper)parameter
optimization. While one can argue that optimization should always be done when
utilizing ML or DL models, most tools demonstrated good performance with default
parameters across diverse targets. Optimizing the often thousands or even millions of
hyperparameter combinations can be unfeasible or, at best, represents a major
additional time investment when speed-up is the primary objective. Further, any
optimization needs to reflect the scale of the intended production VS. Even though
more optimal parameters for specific systems may exist, relying on the same defaults
as method authors or published examples is often the preferable choice. However, users should, at the very least , infor m every available parameter choice in the
selected tool by small-scale pre-studies or a retrospective analysis of recalls.
326 I. Pöhner et al.
Fig. 11.5 Schematic illustration of a fragment-/reagent-based approach to ultra-large-scale screening. Instead of fully elaborated compounds, the docking inputs are fragments, fragment-sized
reagents, or minimal representative scaffolds, sometimes with designated R-groups for enumeration
(indicated by the yellow sphere). Following conventional docking, only the most promising starting
scaffolds are selected and elaborated into fully qualified small molecules, which are docked into the
target receptor in a final evaluation step
6.2 Reagent- or Fragment-Based Approaches to Screen
Combinatorial Libraries
Ultra-large make-on-demand chemical libraries are often combinatorial libraries
created from reagents and predefined synthesis protocols to interconnect the building
blocks. Several methods seek to reduce the computational cost of screening an
ultralarge library by relying on the evaluation of fragment-like reagents or building
block composites, as schematically illustrated in Fig. 11.5. In such approaches, only
the most promising scaffolds get enumerated, thus substantially reducing the number
of compounds to consider. Similar to ML-driven QSAR solutions, the concept of
elaborating compounds from virtual fragments is equally not a new development
[86, 87] but has recently seen a surge linked to the emerging ultra-large combinatorial libraries and spaces.
With Thompson sampling (TS), Klarich et al. [35] propose a probabilistic search
method operating in the reagent space. TS is applicable to various ligand- and
receptor-based VS approaches, where the respective method’s scoring function
informs the belief distribution underlying TS. For each available reaction, reagents
are randomly selected, and fully elaborated molecules are generated in silico.
Iteratively, the molecules are scored, and the belief distribution is updated. The
authors applied TS to a Tanimoto-2D similarity search, a 3D similarity search, and a
docking study. They also compared TS with an active learning RF approach. Recalls
between 57% and 90% were achieved for different tasks when evaluating no more
than 1% of an ultra-large combinatorial library. The application of TS to VS
represents a novel approach, and the performance in finding top-scoring hits may
require further optimization. Nonetheless, TS achieved 100 to 48,000 times speedup, and can significantly reduce computing expenses for screening ultra-large
combinatorial libraries.
11 Ultra-Large-Scale Virtual Screening 327
Another approach relies on the combinatorial nature of the Enamine REAL Space
with more than 11 billion compounds. In V-SYNTHES (Virtual Synthon Hierarchical Enumeration Screening), the library is first decomposed into all possible combinations of scaffolds and synthons for all reactions [22]. A synthon can be pictured as
a minimal capping R-group or common substructure, from which a fully elaborated
molecule can be grown. In the V-SYNTHES approach, all scaffold–synthon combinations are docked, and only the most promising fragments will have their
R-groups enumerated until the molecules are fully elaborated. Docking reduction
to only 1.5 million compounds of the 11-billion compound library corresponds to a
5000-fold speed-up of the screen. With a hit rate of 33% (compared to 15% for a
smaller-scale classical VS approach), V-SYNTHES helped to identify
14 submicromolar ligands of a cannabinoid receptor [22]. Its application to the
ROCK1 kinase achieved a 28.5% hit rate and resulted in several nanomolar
ligands [22].
ROCK1 was also the target of Beroza et al. [88] and their largely manual
workflow termed Chemical Space Docking, which combines several modeling,
docking, and chemoinformatics tools. The workflow uses the building blocks and
synthesis protocols underlying combinatorial make-on-demand chemical libraries
like the Enamine REAL Space to define the searchable chemical space. In a “greedy”
optimization strategy, 500 unique, promising starting fragments were selected from
the top-scoring FLExX docking results by properties including their interaction
pattern, internal strain, logP, and chemical diversity. For ROCK1, the full enumeration of these starting fragments yielded around 5.2 million compounds, which
represented a significant reduction compared to the around 858 million possible
virtual product molecules in the considered Enamine REAL subspace. The approach
altogether identified 27 actives with K
values below 10 μM, including three
i
compounds with a novel hinge-binding motif not previously observed in ROCK1
[88]. While the authors acknowledge that a more exhaustive sampling of the
chemical space may be preferable, their approach showcases how ultra-large chemical spaces can be utilized in VS even with very limited computational resources.
In their “crystal structure first” approach, Müller et al. [89] combine Chemical
Space Docking with X-ray crystallography-based fragment screening. Instead of
docking building block fragments , this approach starts from existing crystallographic evidence: Four fragment co-crystal structures of protein kinase A were
selected, and fragments from the Enamine REAL Space with 2.6 billion virtual
products were matched onto their position with FlexX docking. Not only does the
approach rely on experimentally confirmed interaction patterns, but such templatebased docking approaches with partially predefined atom positions are also significantly faster than regular approaches that require a more exhaustive sampling
during pose generation. After that, the approach resembles Chemical Space
Docking, where the most promising matches are enumerated and docked again.
Within only 9 weeks, this crystal-based approach achieved a hit rate of around 40%
and concluded with a set of additional crystal structures of the novel compounds to
confirm the predicted binding modes. The approach identified several low micromolar to submicromolar inhibitors with novel scaffolds, the most potent of which
showed a K
of 744 nM.
i
328 I. Pöhner et al.
Gahbauer et al. [54] likewise complemented ultra-large VS efforts with X-ray
crystallographic evidence: To find inhibitors of the SARS-CoV-2 nonstructural
protein 3 (NSP3), they first utilized existing crystal structures with bound fragments
of up to 180 μMaffi nities. The crystallographic fragments were linked, and the
resulting drug-like molecules served as search templates for a 2D similarity search
with the SmallWorld server. After screening 22 billion synthesizable molecules in
the Enamine REAL database, eight of the 13 prioritized compounds were confirmed
as NSP3 binders by crystallographic soaking experiments [54]. In addition to these
initial mic romolar hits, the authors aimed to expand the hit diversity in an unbiased
fashion: They additionally carried out a conventional docking-based screen of
around 400 unique molecules with DOCK3.7. In addition to the default scoring
parameters, they also ran a calculation with optimized scoring parameters to better
reflect the crystallographic evidence for NSP3. This effort added five additional
classes of previously unknown low micromolar inhibitory scaffolds to the hit pool.
Most virtual hits had an anionic nature, but the authors finalized their study with a
structure-based optimization that successfully replaced anionic groups with neutral
groups, opening up a route toward better membrane permeability for the novel NSP3
inhibitor classes [54].
Finally, another strategy for chemical space docking, termed SpaceDock, was
recently published by Sindt et al. [90]. Their approach uses a 5.5 billion-compound
subset of the Enamine REAL Space with no more than two synthetic steps selected
from a small set of robust chemical reactions. SpaceDock relies on docking the
unmodified fragment-sized reagents before linking them into fully enumerated
compounds. The authors first used GOLD to dock a combinatorial space of around
98 million compounds to estrogen receptor beta in a retrospective analysis. Only
about 0.6% of their screening space constituted true agonist scaffolds, while the
remainder were decoys. Encouragingly, true binders were significantly enriched by
SpaceDock, making up 84% of the resulting virtual hits [90]. After establishing the
ability of the method to generate known ligands or their close analogs, SpaceDock
was applied to the docking of 670 million compounds against the dopamine D
receptor. A known ligand and several compounds that could be the results of scaffold
hopping from known ligands were again among the virtual hits. The final hits
included binders with submicromolar to low micromolar K
a novel scaffold that had not previously been observed in D
, four of which contained
i
receptor antagonists.
3
Table 11.7 summarizes the featured examples that exploit the combinatorial
nature of many state-of-the-art ultra-large screening libraries to speed up VS and
reduce the associated computational burden.
3
7 Challenges and Future Perspectives
We have, in previous sections, hinted at some practical challenges for ultra-large
VS. In this final part of the chapter, we will address a final key issue for ultra-largescale screening: The process of selecting hit compounds for further work-up, or hit
11 Ultra-Large-Scale Virtual Screening 329
Table 11.7 Summary of ultra-large reagent-/fragment-based screening campaigns
Compounds
screened Target(s) Tool Mock throughput
22 billion NSP3 Fragment linking + 2D sim-
11 billion Cannabinoid
5.5 billion Not specified SpaceDock 1360 cpds/min Sindt et al.
2.6 billion PKA Chemical Space Docking
858 million ROCK1 Chemical space docking
335 million JNK3 TS (OEDocking) 350 cpds/min Klarich
234 million Random query
Approx.
100 million
94 million Random query
The reported “mock” throughput takes the total number of input compounds divided by the total
computing time for a single computing unit. Note that most methods utilize conventional bruteforce docking with the corresponding low throughput, but accelerate the approach by the significant
reduction of required brute-force docking
NSP3 SARS-CoV-2 nonstructural protein 3, PKA protein kinase A, NA not available (authors did
not provide total run-time or speed information)
receptor,
ROCK1
molecule
Not specified SpaceDock (GOLD) 2170 cpds/min Sindt et al.
molecule
ilarity search
V-SYNTHES (ICM-Pro) NA Sadybekov
“crystal structure first”
(FlexX)
(FlexX)
TS (OEShape ROCS) >120,000 cpds/
Tanimoto 2D
Similarity search
NA Gahbauer
7200 cpds/min Müller
NA Beroza
min
522 million cpds/
min
Reference
(s)
et al. [54]
et al. [22]
[90]
et al. [89]
et al. [88]
et al. [35]
Klarich
et al. [35]
[90]
Klarich
et al. [35]
triage. Hit triage suffers from unreliable rank-scorin g, which escalates on this new
dimension. We aim to highlight the issue and possible solutions to this prime
counter-argument against ultra-large VS. Additionally, we address future direc tions
and our hopes for the adoption and extension of large-scale benchmarking data.
Finally, we discuss some recent developments, which may shape the future directions of ultra-large VS.
7.1 Hit Triage: An Old Problem on a New Dimension
The key objective of VS is to narrow down a large pool of compounds and retain a
few promising top-ranked virtual hits for further post-processing and validation, for
example, by more extensive computational studies or experimental assays. Ultralarge screening faces two main challenges, which also give rise to most counterarguments against ultra-large VS: First, we can reasonably define screening success
as the successful enrichment of (to-be-confirmed) true hits among the top-r anked
candidates.
330 I. Pöhner et al.
Table 11.8 Summary of purchased compounds and hit rates in recent ultra-large VS campaigns
Screening
input Target(s) Ordered Obtained
Actives (Hit
rate) Reference(s)
22 billion NSP3 16 13 8 (62%) Gahbauer et al.
[54]
>12.3
billion
11 billion Cannabinoid
Mpro 1700 1283 117 (9%) Gentile et al.
[34]
80 60 21 (35%) Sadybekov
receptor
et al. [22]
11 billion ROCK1 24 21 6 (29%) Sadybekov
et al. [22]
2.6 billion Protein kinase A 106 93 (75 soluble) 30 (40%) Müller et al.
[89]
1.40 billion PLpro 260 178 17 (10%) Garland et al.
[73]
1.40 billion A
adenosine
2A
39 39 2 (5%) Tang et al. [74]
receptor
1.30 billion KEAP1 590 590 69/23/12
(2–12%)
a
Gorgulla et al.
[7]
1.12 billion Lpd 103 88 6 (7%) Michino et al.
[21]
1.00 billion Lin28 163 163 15 (9%) Radaeva et al.
[75]
858 million ROCK1 77 69 27 (39%) Beroza et al.
[88]
670 million Dopamine D
3
16 15 10 (67%) Sindt et al. [90]
receptor
σ2 receptor 577 484 127 (26%) Alon et al. [19]
490 million
330 million NSP3 90 78 22/11/30
(14–38%)
246 million NSP3 54 46 25/5/8
(11-54%)
a
Gahbauer et al.
[54]
a
Gahbauer et al.
[54]
235 million Mpro 100 100 19 (19%) Luttens et al.
[57]
150 million MT
melatonin
1
40 38 15 (39%) Stein et al. [18]
receptor
138 million Dopamine D
4
589 549 122 (22%) Lyu et al. [17]
receptor
115 million CysLT1R,
CysLT2R
155 139 10/17
b
(7/12%)
Sadybekov
et al. [63]
99 million AmpC β-lactamase 51 44 5 (11%) Lyu et al. [17]
75 million Serotonin 5-HT
receptor
– 17 4 (24%) Kaplan et al.
2A
[64]
NSP3 SARS-CoV-2 nonstructural protein 3, Mpro SARS-CoV-2 main protease, PLpro SARSCoV-2 papain-like protease, KEAP1 Kelch-like ECH-associated protein 1, Lpd mycobacterial
lipoamide dehydrogenase, CysLT1R, CysLT2R cysteinyl leukotriene G protein-coupled receptors
a
Activities in different assays
b
Active on/binding to one or more of the targets.
11 Ultra-Large-Scale Virtual Screening 331
With often only very few of these candidates entering experimental validation
(see Table 11.8 for a summary of the discussed campaigns), a first caveat of ultralarge screening is that your initial set of virtual hits may still be rather large—for
instance, even if you consider only the top 0.1% of a billion-scale screen, this would
still leave one million potentially promising compounds for consideration in all
follow-up procedures [24]. This high numbe r, in connection with a well-known
shortcoming of ranking accuracy in VS, can turn hit selection into a dangerous
gamble. The scoring functions used in VS often predict a desirable activity profile
for compounds that represent inactives [1, 3]. Combining those two aspects, your
screen may leave you with a large number of virtual hit candidates, many of which
may well be false positives [1, 24].
With the inaccuracies of scoring approaches in mind, should you truly rely on
greedy selection and post-process only the very top of the list? One of the first ultralarge docking-based VS campaigns provided an argument in favor of this concept:
As part of their study of the dopamine D
receptor, Lyu et al. [17] investigated the
4
connection between docking rank and hit rate. They sought experimental confirmation not only for selected top-ranked virtual hits but also for several compounds with
less favorable docking scores. Strikingly, for their target, the authors found a clear
drop in the hit rate when moving to less favorable scoring ranges. The authors also
compared an automated, purely data-driven, selection of top-scoring compounds
with the selection by expert opinion. While expert-driven selection yielded higheraffinity compounds, the hit rate of both approaches remained the same. Thus, despite
false positives, drawing the final hit selection from the top-ranked compounds
appears to be a viable strategy at least for some targets. Consistently, evidence
suggests that both classical and recent ML-based scoring functions are capable of
identifying better hits from larger chemical spaces [6]. However, since scoring is
highly target- (or system-) dependent, these findings do not necessarily generalize.
For instance, Alon et al. [19] likewise selected their initial hits from different scoring
bins and observed a hit rate of 27% in the highest scoring bin. The second-highest
scoring bin, on the other hand, gave a 61% hit rate. However, the authors observed
the same general trend of dropping hit rates as a function of less favorable docking
scores.
In a follow-up effort, Lyu et al. [91] focuse d more on the general implications of
massively growing chemical libraries for VS. They extrapolated that with growing
libraries, not only can one expect better scores, but there is also a striking increase in
artifacts. Artificially high-ranked false positives are thus more likely to drown out
true positives in the top-ranking bin(s). Thus, when working with ultra-large chemical libraries, caution is advised when relying on a greedy top-ranking-only selection
logic. Instead, more lenient selection concepts which draw their hits also from lowerscoring bins, or rescoring strategies with alternative approaches should be considered [24, 91]. Bender et al. [24] provide a comprehensive overview of selection
criteria and further recommendations for the experimental validation and next steps.
Despite all their problems, state-of-the-art scoring functions, paired with the
appropriate caution, clearly can enable successful drug discovery campaigns, as
emphasized by many of the ultra-large screens discussed herein, albei t hit rates
332 I. Pöhner et al.
greatly varied between different campaigns (see Table 11.8). Various studies identified novel binders with previously unknown scaffolds. The odds of finding novelty
should improve for ultra-large screening approaches, which can, for example, benefit
a researcher’s ability to patent the discovered hits and any derived compounds
[8]. However, Grebner et al. [4] showed for their 3D shape screening approach
that the diversity of obtained hits no longer increased when exceeding library sizes of
10 billion. This is, in all likelihood, extremely target-, approach-, and screening
library-dependent, but should serve as a reminder that in ultra-large VS, we should
carefully weigh the cost–benefit, and not blindly consider “bigger” necessarily
always “ better.”
7.2 Future-Proofing VS Approaches
While we cannot be certain how the future of VS will develop in the long run, it is
reasonable to assume that VS on the ultra-large scale will become more common.
Given the massive resource requirements of standard brute-force approaches, it is
also likely that accelerated approaches, and those leveraging the combinatorial
nature of chemical libraries, will be more heavily relied upon. The examples
discussed in this chapter underline that many approaches are, indeed, already able
to proces s ultra-large libraries. However, currently, almost all screens rely on HPC or
cloud computing resources to do so. Only a few tools have been specifically
designed with a focus on state-of-the-art ultra-large libraries or benchmarked on
this scale.
We can anticipate developments in dedicated screening software for both HPC
and cloud computing, but also more limited computational resource settings. This
notion also underlines why we believe that ultra-large-scale benchmarking data is
crucial to the development of the field. As discussed, for example, in the work on
Thompson sampling for VS acceleration presented by Klarich et al. [35], completely
novel approaches or application domains require careful optimization to maximize
performance and recall. Such optimization should take the intended use-scale into
account, which requires datasets of suitable size. While many of the brute-force
efforts performed so far only released selected top-scoring compounds, if any data at
all, one of the first brute-force docking studies released their full 99 million AmpC
and 138 million D
gold standard for benchmarking in the field. With the Glide docking results released
by Yang et al. [81], two different ultra-large docking datasets for the same targets are
now available (see also Table 11.11 in the Appendix). More recently, two billionscale docking datasets were released into the public domain: The first covers docking
and rescoring results of 1.4 billion compounds against five SARS-CoV-2 target
proteins [56, 66], the second 1.56 billion Glide-HTVS results obtained with lead-like
compounds against two different targets [23, 65] (Table 11.11). These datasets
DOCK docking results [17]. This dataset has since become a
4
11 Ultra-Large-Scale Virtual Screening 333
should provide suitable ultra-large and billion-scale benchmarking data for future
approaches. If other ultra-large-scale brute-force approaches are undertaken in the
future, it would be desirable if authors released their results to broaden and diversify
benchmarking data for ultra-large VS.
In addition to newly emerging approaches and more rigorous benchmarking, the
libraries themselves will likely continue to grow. If one considers estimates of the
total number of hypothetically possible drug-like molecules in chemical space,
which range from around 10
20
to over 1030, even the largest modern screening
libraries discussed herein remain tiny in perspective [3]. The emergence of even
larger enumerated libraries may be questionable, as their data sizes have already
become impractical. Instead, one can anticipate a further surge of nonenumerated
combinatorial spaces that store building blocks and synthesis rules rather than the
elaborated compounds [15]. This chapter already discussed several approaches that
rely on such combinatorial libraries to speed up conventional screening. In ligh t of
the likely trends for virtual chemical libraries, we can assume a stronger presence of
reagent-based approaches in future VS. If libraries indeed keep growing, combinatorial searches and 2D approaches will become critical components of VS when
aiming for the largest spaces, as classical approaches appear to fall short of achieving
sufficient throughput to scale beyond current state-of-the-art library sizes [9].
7.3 Other Trends That Might Impact the Future
of Ultra-Large VS
An alternative way to find entirely novel scaffolds without relying on existing
libraries utilizes generative AI [92]. First examples of combining docking with
generative AI models have already begun to appear a few years back, for instance,
MORLD (Molecule Optimization by Reinforcement Learning and Docking)
[93]. The tool was showcased on discoidin domain receptor 1 kinase and the
dopamine D
receptor as employed in the ultra-large screening campaign by Lyu
4
et al. [17] discussed herein.
One drawback of generative models, potentially limiting their adoption in VS, has
been that the molecules produced in silico may not be synthesizable or even always
feasible. This issue was partly addressed by training networks specifically on
synthetically accessible compounds [94]. However, in an age of searchable ultralarge make-on-demand spaces, generative models may gain more ground in future
VS. Rather than seeding docking approaches directly from ultra-large librarie s,
generative models could create virtual hits de novo, which can then serve as
templates for ultra-large library searches. Indeed, a very recent publication by
Popov et al. [95] showcases the combination of generative AI, docking, and ultralarge library searches with their tool HIDDEN GEM (HIt Discovery using docking
ENriched by GEnerative Modeling). With the present and future advances in ultrafast searches of nonenumerated spaces in mind, this may represent a first blueprint of
a future VS strategy, which would partially disentangle docking and ultra-large
library searches.
334 I. Pöhner et al.
DNA-encoded libraries (DELs) represent a new trend in experimental screening
that may shake up the role of VS in hit identification. DELs allow for largely
unbiased, unprecedented throughput in wet lab settings, venturing into the billionscale and beyond [9, 96, 97]. Those libraries rely on combinatorial chemistry to
generate small molecules with a DNA “barcode.” A protein target can be exposed to
the full library in solution, and any nonbinders get washed off. Finally, collecting the
bound compounds and amplifying their DNA labels by PCR enables their identification [8]. The ability to test an entire library in a single tube significantly reduces the
amount of required protein. Thus, e.g., mammalian expression vectors or the isolation and purification of a specific protein target from a patient’s organ might become
feasible [96]. DELs could further enable the prospective elucidation of SAR profiles
and hit identification for particularly challenging, even “undruggable” targets
[96, 97].
Collie et al. [97] discuss novel binding sites and previously unobserved enzyme
conformations, which were indirectly discovered by DELs. However, despite their
great potential, larger libraries suffer from higher false negative rates and increasing
noise [96]. Furthermore, constructing DELs and analyzing the results of DEL assays
is not without challenges, and to date, those libraries tend to cover different and
rather narrow chemical spaces compared to most small-molecule libraries discussed
herein: They are often rich in large, flexible, peptide-like compounds—thus not
being the first choices for medicinal chemistry and hit-to-lead optimization
[9]. Taken together, DELs can uncover interesting novel information and provide
starting points for drug discovery campaigns. As experience with DELs grows and
protocols to overcome their probl ems are established, their application, alone and
likely also in combination with VS approaches, will likely impact the future of drug
discovery.
8 Conclusions
Over the past 5 years, as demonstrated by the various examples in this chapter, the
growth of chemical libraries into the ultra-large domain has inspired several ultralarge VS campaigns. An overview of all covered studies in the context of chemical
library sizes is shown in Fig. 11.6. As can be seen, in principle, most VS approaches
can be applied to state-of-the-art ultra-large libraries, although often with considerable investment of computing power. Reagent-/fragment-based approaches represent novel and very recent developments in response to combinatorial libraries and
spaces that appear on the rise. One can further appreciate that approaches so far did
rarely exceed 10 billion input compounds. Ligand-based and accelerated approaches
have been applied on that scale, hinting at the VS concepts with the highest potential
of adaptation to even larger library sizes.
Many highly potent compounds, often with novel scaffolds, were discovered with
the help of those screens for a variety of targets. Typically, the final hit selections
were not data-driven but relied on expert opinion. As such, the in part astounding