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Chapter 11
Ultra-Large-Scale Virtual Screening
Ina Pöhner, Toni Sivula, and Antti Poso
Abstract Recently, make-on-demand chemical libraries and spaces began to grow
into the billion scale, resulting in a surge of ultra-large ligand- and receptor-based
virtual screening (VS) approaches. This chapter introduces state-of-the-art screening
compound resources with up to 290 trillion compounds and provides examples of
ultra-large VS campaigns performed between 2019 and 2024. We empha size key
considerations for the planning stage of your own ultra-large VS and the choice of
the computing environment. Some of the covered highlights include 2D similarity
searches in nonenumerated spaces, 3D shape screening, giga-scale brute-force
docking, and screening acceleration strategies either leveraging machine learning
and deep learning or relying on the reagent-space of recent make-on-demand
combinatorial libraries. To scrutinize whether and when “bigger is better,” we
discuss the critical challenge of hit triage on the ultra-large scale and conclude
with future perspectives for VS in the face of continuing library growth.
1 Background
The early-stage hit identification stage of drug discovery efforts frequently features
in silico methods, which are typically considered cheaper and faster than their
in vitro equivalents [1]. In silico screening enables the timely high-throughput
evaluation of large numbers of potential candidate molecules. This computational
prioritization of initial virtual hits is commonly termed virtual screening (VS).
Traditionally, VS campaigns have either been ligand-centric or receptor-based [2].
I. Pöhner (✉) · A. Poso
School of Pharmacy, University of Eastern Finland, Kuopio, Finland
e-mail: ina.pohner@uef.fi
T. Sivula
School of Pharmacy, University of Eastern Finland, Kuopio, Finland
CSC — IT Center for Science Ltd., Life Science Center Keilaniemi, Espoo, Finland
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug
Design, Computer-Aided Drug Discovery and Design 3,
https://doi.org/10.1007/978-3-031-76718-0_11
299
300 I. Pöhner et al.
Ligand-based VS uses structural information and physicochemical properties of
known active compounds and screens a compound library for virtual hits that share
some of the actives’ features. For example, classical quantitative structure–activity/
property relationships (QSAR/QSPR) and fingerprint-based searches rely solely on
2D structure and topology, which allows for their exceptionally high throughput.
However, especially in data-sparse scenarios, such models have limitations and often
generalize poorly [1, 3, 4]. Approaches that utilize the 3D structural information of
the small molecules, such as shape-matching algorithms or pharmacophore searches,
are often superior at capturing the intricacies of biological action in our 3D world
[3, 4]. They are, however, generally slow er than their 2D-only counterparts [4].
When starting from known active scaffolds, ligand-based approaches are attractive for performing scaffold hopping [5]. However, their inbuilt dependence on
known actives (or ligands with the desired properties) is also their biggest drawback:
In prospective screens, where such prior knowledge does not exist, ligand-based VS
cannot be applied.
If a molecular target, for instance, an enzyme, can be defined, receptor-based VS
approaches can be used (instead)—even when no prior knowledge about possible
binding partners exists. The most common technique to discover virtual hits for a
target receptor is molecular docking. Docking first fits ligand 3D structures from
large databases into the receptor binding pocket. It then assigns a docking score,
aiming to quantify the formed interactions and rank the ligands [1, 2].
It is a ge neral notion that larger compound library sizes increase the probability of
finding a close-to-optimal match with the desired properties during a VS campaign
[3, 6, 7]. Thus, unsurprisingly, in step with a growth trend observed for screening
compound libraries, ligand- and receptor-based ultra-large VS approaches have
seamlessly evolved from their smaller-scale counterparts.
Before we dive deeper into the evolution of ultra-large VS, let us define what the
“ultra-large scale” actually is. The term itself is fuzzy and used in the literature for
screens of varying sizes, to date typi cally ranging from around 10
7
to 1010processed
small molecules. Herein, we define our lower limit for considering a library or VS
approach as “ultra-large” arbitrarily at 50 million compounds.
Ultra-large libraries themselves are not a new phenomenon: Efforts to enumerate
much of the hypothetically possible chemical space gave rise to virtual ultra-large
libraries already several years back (see, e.g., references [3, 8, 9]. for a comprehensive overview). However, the organic synth esis of a virtual hit compound from such
a library was often time-consuming and costly, potentially hampering or preventing
the in vitro validation of the VS predictions. A tight project timeframe and budget or
the lack of medicinal chemistry facilities wer e therefore arguments in favor of
smaller libraries with commercially available compounds [1].
The recent popularity of ultra-large VS is owed to advances in robust parallel
organic synthesis and the emergence of ultra-large make-on-demand libraries. These
combinatorial libraries rely on predefined chemical reactions to link large collections
of off-the-shelf building blocks. As a result, numerous make-on-demand compounds
with a high probability of synthesis success can be ordered and readily purchasable
in-stock libraries have also grown in size. For instance, the popular screening
11 Ultra-Large-Scale Virtual Screening 301
resource ZINC grew from 20 million commercially available compounds in 2012 to
more than 37 billion by 2022 [10–12]. Likewise, the make-on-demand library
Enamine REAL started off on the million scale and to date contains around 48 billion
compounds [13, 14 ].
Previously, their high throughput was among the strongest arguments in favor of
VS methods. However, as library sizes hit the billion scale, VS throughput became a
limiting factor. For instance, the fastest docking methods can typically screen about
30–60 compounds per minute per computing core [3]. Consequently, a docking
study of one million compounds on a workstation computer with 8 CPUs takes
around 1.5–3 days. Processing 1 billion co mpounds with the same resources would
require 4–8 years of nonstop docking for a single target. To tackle ultra-large
screening projects, one thus needs to either massively scale up on computational
resources or find strategies to avoid the bulk of the time- and resource-consuming
computation steps.
In many cases, the basic operation of individual VS approaches is not altered
when they are applied on the ultra-large scale. Therefore, this chapter will assume a
basic familiarity of the reader with different VS methods and focus on challenges
faced specifically on the ultra-large scale and the emerging solutions. For more
information on the individual VS methods, such as QSAR, pharmacophores, and
docking, the reader is, for examples, referred to Chaps. 6 and 7 of this book.
The chapter will introduce ultra-large screen ing databases and recent examples of
ultra-large-scale ligand- and receptor-based screening campaigns with an emphasis
on the past 5 years (2019–2024). As most ultra-large-scale screening campaigns
utilize high-performance computing (HPC) or cloud computing resources, we will
discuss some characteristics of such computing environments, important considerations for their use, and tools designed to cope with the growing numbers of
screening compounds. As we enter an era where ultra-large screening campaigns
will likely become more prominent and strive to govern even large r chemical spaces,
we are only beginning to comprehend the associated pitfalls of “Big Data” in drug
discovery. This chapter will also present some arguments in favor of and against
ultra-large VS, flag so far unmet needs, and provide a perspective of what lies ahead
for in silico screen ing campaigns in the future.
2 Ultra-Large Screening Libraries and Chemical Spaces
As indicated above, ultra-large VS has little to do with technological advances in
screening methodology. Rather, it represents a development in response to the
emergence of billion-scale make-on-demand chemical libraries. Thus, before diving
deeper into the screening process and specific examples, we will look at the culprits
that force us to rethink our approaches to VS.
We can distinguish two major types of ultra-large libraries: enumerated chemical
libraries, where each compo und is stored explicitly, and nonenumerated spaces
[9, 15]. Enumeration of all possible combinations of building blocks is deemed
302 I. Pöhner et al.
inefficient when working with billion-scal e combinatorial libraries. Therefore,
nonenumerated spaces rely on storing the building blocks and all chemical reactions
that can be used to interconnect them instead of the fully elaborated possible virtual
products [15].
One famous example of a freely accessible ultra-large compound library is ZINC.
ZINC was initially created as a compound research database focused on docking and
offered downloadable precomputed 3D conformers for many of its compounds. In its
2012 release, ZINC already contained around 20 million purchasable compounds
from different vendors [11]. By 2015, the library had become an ultra-large resource
with around 120 million purchasable drug-like compounds [16]. ZINC15 offered
enhanced programmatic access and downloadable ligand 3D coordinates in various
formats, such as sdf, mol2, and pdbqt. It is this version of ZINC that would be
prominent in many of the recent ultra-large receptor-based VS works (see Sect. 5.1)
[17–19]. Meanwhile, ZINC continued to grow into the giga-scale: ZINC20, owing to
the addition of compounds from make-on-demand suppliers, reached a total size of
roughly 1.4 billion compounds, of which around 1.3 billion were purchasable
[20]. The latest version ZINC22 added molecules from additional make-on-demand
chemical spaces, increasing its tota l size to around 37 billion [12]. Currently, over
4 billion compo unds can be downloaded from ZINC in 3D ready-to-dock formats.
Enamine is one of the vendors that allowed ZI NC to grow into the billion scale.
Their REAL (REadily AccessibLe) make-on-demand compounds can be typically
obtained with a success rate of at least 80% within less than 1 month [13–
15]. Enamine maintains a large collection of over 100,000 in-stock building blocks
and relies on more than 167 robust validated synthesis protocols [13, 14]. REAL
compounds represent another popular compound resource of recent ultra-large
screening campaigns [21–23]. To date (Mar ch 2024), the REA L database contains
about 6.75 billion enumerated compounds. Of the several available subsets, some are
likewise ultra-large: The Enamine REAL lead-like set comprises 3.93 billion compounds, and Enamine also offers a library of nearly 158 million natural product-like
compounds. Enamine’s by far largest collection of compounds is an example of a
nonenumerated space: the REAL Space currently contains over 48 billion virtual
products, stored primarily as building blocks and connecting chemical reactions
(although an enumerated version is available from Enamine upon request [13]).
Another vendor that facilitated ZINC’s tremendous growth is WuXi AppTec
[24, 25]. Their billion-scale make-on-demand library, GalaXi, likewise relies on
predefined reaction schemes and a large in-stock building block collection. Currently, the enumerated virtual Gal aXi library contains 16 billion compounds [25].
While working with enumerated ultra-large libraries can be achieved by scaling
up conventional ligand- or receptor-based screening approaches, nonenumerated
spaces require specialized technologies to search within their building blocks and
the connecting reactions to enumerate relev ant virtual products on the fly.
Approaches for working with nonenumerated spaces will be discussed in detail
later in the text (see, e.g., Sects. 4 and 6.2). Suffice it to say that nonenumerated
spaces like the Enamine REAL Space or the nonenumerated variant of GalaXi, the
GalaXi Space, can be searched with tools like the chemical space navigation
11 Ultra-Large-Scale Virtual Screening 303
Table 11.1 Summary of discussed publicly available/searchable enumerated ultra-large screening
libraries and nonenumerated chemical spaces
Library/Space Approx. number of compounds File format(s)
Enumerated
CHIPMUNK 95 million sdf (2D)
Enamine REAL
database
ER lead-like 3.93 billion CXSMILES
ER natural product-
like
GalaXi database 16 billion SMILES
SAVI 1.75 billion SMILES, sdf (2D)
ZINC22 37 billion (2D), 4.5 billion (3D) SMILES, mol2, sdf,
ZINC20 1.4 billion, 1.3 billion purchasable SMILES, mol2, sdf,
ZINC15 750 million (purchasable, 2D), 230 million
Nonenumerated
CHEMriya 12 billion –
Enamine REAL
space
eXplore 4.9 trillion –
GalaXi space 16 billion –
KnowledgeSpace 290 trillion –
For enumerated libraries, available formats for compound download are reported. Corresponding
URLs can be found in Table 11.9 in the Appendix. ER: Enamine REAL; Billion = 10
lion = 10
12
; db2: format for use with the docking tool DOCK
6.75 billion CXSMILES, sdf (2D)
157.7 million CXSMILES
pdbqt, db2
pdbqt, db2
(purchasable, 3D)
48 billion –
SMILES, mol2, sdf,
pdbqt, db2
9
; tril-
platform infiniSee [26]. Some vendors do not offer (part of) thei r enumerated
collection for download but instead rely on its integration in chemical space search
tools. With infiniSee and related approaches, additional billion-scale synthesis-ondemand spaces become searchable, for example, eMolecules’ eXplore with 4.9
trillion virtual products or Otava’s 12 billion on-demand space CHEMriya [27–29].
In light of the gigantic sizes of nonenumerated chemical spaces, the reader may
wonder if the ability to search multiple spaces is even required. Interestingly,
extensive efforts to compare chemical spaces have revealed surprisingly limited
overlap between the different chemical spaces [15, 28].
Libraries and spaces discussed this far constituted commercially available compounds and make-on-demand vendor collections (see Table 11.1 for a summary).
While the direct purchase of virtual hits can be an attractive option, other efforts cater
more to the needs of those who wish to perform the compound synthesis themselves:
While still relying on commercially available building blocks, reactions are derived
from literature to ensure the synthetic feasibility of the virtual products.
For example, NCI’s 1.75 billion enumerated compound library SAVI (Synthetically Accessible Virtual Inventory) relies on building blocks from Enamine and
304 I. Pöhner et al.
integrates expert knowledge into the definition of virtual synthesis rules [30]. Compounds contained within SAVI can be ranked by their synthetic accessibility, with
the most synthesizable class currently encompassing 1.09 billion compounds
[30]. Literature-derived chemical reactions and commercially available building
blocks are also used to define KnowledgeSpace, a virtual chemical space created
by BioSolveIT, the company behind infiniSee. KnowledgeSpace currently comprises 10
14
virtual molecules and can be searched with infiniSee [15, 26, 31].
Many of the recently published ultra-large-scale VS efforts discussed in this
Chapter stem from academic context and utilize the mentioned public and commercial libraries and spaces. However, ultra-large chemical spaces are also a very
prominent sight in industrial drug development: Over the past years, many pharma
companies have developed their own proprietary ultra-large chemical spaces, for
instance, BICLAIM of Boehringer-Ingelheim and Merck’s MASSIV (10
products), or GlaxoSmithKlin e’s GSK XXL (10
26
virtual products) [9, 15]. There-
20
virtual
fore, tools and protocols to work with and screen ultra-large chemical spaces are of
great interest to academic and industrial drug discovery alike.
All libraries and spaces mentioned so far mainly comprise drug-like small
molecules. Compounds targeting protein–protein interactions (PPIs) typically
exhibit a different property profile that may be underrepresented in libraries focused
on drug-likeness. This motivated the author s of CHIPMUNK (CHemically feasible
In silico Public Molecular UNiverse Knowledge base) to propose a specialized
ultralarge library: They used commercially available building blocks and selected
virtual reactions together with targeted property filtering to generate a library of
95 million synthetically accessible compounds with PPI modulator properties [32].
Table 11.1 summarizes the discussed chemical libraries and spaces, and
Table 11.9 in the Appendix lists the corresponding URLs to obtain the different
compound collections.
3 How Ultra-Large Chemical Libraries Alter VS
Workflows
After familiarizing ourselves with the state-of-the-art ultra-large libraries and spaces,
we may ponder how working on the ultra-large scale affects VS workflows in
practice. Before we discuss specific examples of ultra-large ligand-and receptorbased screens, we first aim to establish important key considerations for ultra-largescale screening workflows and discuss what sets them apart from smaller-scale
approaches.