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5.5 Computational Tools for the Exploration of Chemical Space 105
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itself [85]. Unfortunately, the exact features relevant to the activity of interest need
to be elucidated from proximations, such as quantitative structure–activity relationship studies (QSAR), or more generalized rules identied from large data mining.
With the breadth of tools available for similarity searches and dimensionality
reduction, a deep understanding of existing chemical space can be gained with the
goal of informing the next generation of RNA-targeting small molecules.
5.5.2 Additional Machine-Learning Tools
Machine-learning-assisted drug discovery is a valuable tool in understanding
RNA:small-molecule recognition, particularly because of the inherent RNA
dynamics that make characterizing small-molecule-binding modes a challenge
[40, 42, 86, 87]. Indeed, machine-learning approaches have been increasingly utilized to facilitate ligand discovery for targeting RNA. These methods aim to model
the complex structure–activity relationships between small molecules and RNA to
enable more ecient identication of active compounds. One general strategy is to
train classication/regression models to distinguish RNA-binding molecules from
non-binders based on calculated molecular features. For example, naive Bayesian
models have been applied to determine key chemical properties of molecules likely
to interact with RNA [40]. The knowledge from such models can then guide the
design of focused screening libraries enriched in putative RNA-binding molecules.
QSAR models can also guide the design of new RNA-targeting molecules. To expand
the applicability domain of conventional QSAR models beyond a single scaold,
a set of structurally diverse RNA-binding molecules was incorporated [88]. With
representative sampling, QSAR models eectively predicted anity and kinetics
for novel chemotypes binding to a target RNA. Beyond learning information
from the experimental readout, predictive modeling of RNA–ligand interactions
directly from RNA structure has been explored. Graph-based representations
of RNA-binding sites in known structures were used as input for graph neural
networks or other machine-learning architectures to predict suitable interacting
ligands [89, 90]. By learning from atomic details of known RNA–ligand complexes,
such computational methods can suggest likely binders without performing a
screen. Overall, machine-learning strategies have demonstrated signicant promise
to enhance the discovery of RNA-targeted ligands.
It is worth noting that recent advances in deep learning (DL) have triggered
several seminal works using generative models for de novo molecular design,
where deep neural networks are employed to resemble the molecular generation
process and full property optimization [91–94]. Though current eorts are made
to target protein structures, these approaches could be easily extended to target
RNAs. Commonly used generative DL architectures include recurrent neural
networks, generative adversarial networks, and autoencoders [91]. Using various
types of molecular representations from simple 1D sequences to the 2D molecular
graph, and even 3D spatial points as the input, the molecular generating process
can be learned eciently, and novel molecules are continuously sampled. The
automatic chemical design combined with machine-learning models mapping
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the structure–activity landscape has great potential for ecient generation of
RNA-targeted small molecules, speeding up the discovery of small-molecule-based
chemical probes.
5.5.3 Structure-Based Ligand Design
Structure-based ligand design such as molecular docking is hindered by a lack of
determined RNA structures and appropriate scoring functions, though research
interests keep growing in the eld [95]. The scarcity of high- or even low-resolution
RNA 3D structures deposited in the Protein Data Bank (PDB) database when
compared to the tremendous number of protein:ligand structures poses a significant limitation. In 2020 there were nearly 300× more unique high resolution
protein:small-molecule structures deposited in the PDB than RNA:small-molecule
structures [96, 97]. Despite this limited number of RNA:ligand structures, these data
have become a valuable source for modeling RNA-ligand binding. One challenge
in RNA docking is the dynamic nature of RNA structures, which can lead to
dierent bound state conformations with dierent small molecules. To account for
RNA dynamics in docking, Stelzer et al. generated an ensemble of experimentally
informed RNA conformers via NMR and molecular dynamics and found that the
binding energies of small-molecule interactions could be predicted with comparable results to when the bound structure was available [70]. Such ensemble-based
docking further identied several novel TAR binders, including DMA, which
evolved as an important RNA-binding scaold as previously mentioned. The
knowledge from protein-based docking and scoring can be transferred to RNA
targets after addressing some common pitfalls, including target dynamics, ligand
protomer, and tautomer correction, and solvation eect upon ligand binding. Kallert
and coworkers transferred protein-based docking protocols to preQ1-riboswitch
virtual screening and successfully identied six active compounds [98]. These hit
molecules re-capitulated most of the important interactions based on generated
docking poses, though with conned structural diversity and novelty, suggesting the
utility of such protein-centric docking methods in the evaluation of RNA-binding
ligands. It is anticipated that future progress requires continued advancement of
computational techniques as well as growth of structural databases and measurement data for RNA–ligand complexes. Physics-based and knowledge-based scoring
functions have shown promising success for modeling RNA–ligand binding, but
machine-learning methods, though still new in this eld, hold great potential to
further improve prediction performance [99].
5.6 Case Studies in Examining and Expanding
RNA-Targeted Chemical Space
With the ever-growing number of discovered RNA-targeting ligands and libraries
designed around RNA-privileged chemical space or scaolds, and the advancement
of tools such as those described in this chapter, there is an opportunity to leverage
5.6 Case Studies in Examining and Expanding RNA-Targeted Chemical Space 107
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the existing knowledge to better guide future design or studies. Here we present a
few case studies for how information on the similarities and dierences between
libraries can be examined, and how this may help to inform future eorts.
5.6.1 Using QSAR to Probe RNA-Targeting Small-Molecule Properties
QSAR can be used to bridge ligand structural parameters and bioactivity or binding
proles. The application of QSAR models can be employed from early hit identication stages to the later-stage lead optimization. Due to the statistical nature
of the approach, the quality and size of the modeling data directly determines
the ability of the model to accurately reveal the underlying distribution of the
structure–activity landscape, i.e. the propensity for overtraining a model with
insucient or low-quality data is high, while the algorithm used to build the model
impacts the outcome slightly due to dierent inherent assumptions.
The use of QSAR studies for RNA targets is in its infancy, and few examples in the
eld directly full the goal of ligand discovery. More often, the QSAR model provides
a lens to help chemists decipher the molecular descriptors that contribute to RNA
binding. For instance, Maciagiewicz et al. synthesized 53 oxazolidinone ligands and
tested their binding to T-box riboswitch antiterminator RNA via a FRET assay. The
ligand-induced uorescence change was correlated with ligand descriptors to aord
the QSAR model, which revealed several key physicochemical properties that contributed mostly to the tting. The study provided quantitative evidence that dipole
moment, hydrogen-bonding capacity, and hydrophobicity played important roles in
oxazolidinones recognizing T-box antiterminator RNA [68].
Additionally, QSAR models could provide an ecient tool that allows fast
evaluation of the virtual library within its applicable domain before any experiment
is done. For instance, Patwardhan and coworkers prioritized synthesis of one DMA
compound out of many others from a virtual library, based on the QSAR predictions
[72]. The compound was discovered to be one of the tightest binders in their DMA
library against HIV-1 exonic splicing silencer of Vpr (ESSV). Many QSAR studies
struggle with limited applicable domain, which means only a specic subset of
compounds can be used for predictions. Toestablish a broadly applicable framework
for RNA-targeted ligand discovery, Cai and coworkers developed QSAR models that
connect the molecular properties of structurally diverse small molecules to their
binding behaviors against HIV-1 TAR [88]. In this study, the authors applied multiple linear regression with feature selection to build predictive and interpretable
models that quantitatively correlate physicochemical and structural descriptors of
ligands across multiple chemical scaolds with their experimentally determined
binding parameters. Through rigorous validation on new molecules, the study
revealed accurate prediction of binding strength and kinetic rate constants [88].
Overall, this work puts forth a generalizable approach leveraging QSAR modeling
to elucidate structure–activity relationships and rationally guide the design of
RNA-targeted ligands. With the number of small-molecule–RNA interactions being
annotated from experiments, QSAR will be increasingly important to discover more
bioactive RNA-targeted ligands and assist decision-making during hit-to-lead triage.
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5.6.2 Evaluating the Chemical Space of Natural, Synthetic,
and Commercial Ligands
In this chapter, we have described the types of ligands developed from nature,
synthetic eorts and those existing in commercial libraries. To better understand
the commonalities and diversity between these groups, we performed a PCA on 3
datasets. The natural ligands were composed of natural products with no synthetic
modications from the data set of RNA targeted ligands reported by Aboul-Ela,
originally collected by Bodoor and coworkers [13, 100]. The synthetic ligands are
represented by molecules from R-BIND small molecules reported to be a part of
synthetic libraries, and the commercial ligands are a subset of 80 molecules from the
DRTL that were selected using a Kennard–Stone algorithm as previously reported
[5, 10, 88, 101]. There was no duplication among the selected ligands. With these
three libraries a PCA was performed on 20 normalized cheminformatic parameters,
following previously reported procedures [3]. Along PCs 1 and 2, there is a strong
separation between commercial and natural ligands, and we see synthetic ligands
covering space in both of the natural and commercial libraries (Figure 5.5a). There
is a bit more separation for synthetic ligands when looking along PCs 1 and 3, as
well as PCs 2 and 3 (Figure 5.5b,c). Polar surface area and hydrogen bond donors
and acceptors strongly contribute to spread along PC1, while ring parameters
contribute to PC2, and nitrogen and oxygen account for a signicant portion of the
spread along PC3 (Figure 5.5d,e, key parameters in bold). The spread along PC 3
for synthetics may be explained by large nitrogen counts compared to low oxygen
counts observed in R-BIND [5] compared to natural ligands, which are enriched
with alcohols from aminoglycosides.
In addition to the PCA we evaluated the shape of the molecules in the various
libraries through calculating principal moments of inertia (PMI). As one may
expect, natural ligands were the most sphere-like due to the inclusion of aminoglycosides and other large exible ring systems, while commercial ligands were
the most rod-like, although this was not a statistically signicant dierence when
compared to the synthetic ligands, as evaluated by a Kolmogorov–Smirnov test
(Figure 5.6). For all analyses in the Kolmogorov–Smirnov test the commercial
and natural libraries were statistically signicant, while the only other dierence
observed was between commercial and synthetic ligands in the distance to sphere.
This data appears consistent with the results from PCA with the largest spread
between commercial and natural ligands and synthetics sharing some similarities
with both libraries. Understanding the dierences between these libraries and the
propensities of dierent classes of molecules to bind RNA more promiscuously
or selectively can inform future ligand design by biasing new libraries toward the
properties of a specic class, such as natural products where we know a high degree
of selectivity has often been evolved.
(a)
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(b)
5.6 Case Studies in Examining and Expanding RNA-Targeted Chemical Space 109
(d)
(c)
Figure 5.5 Principal component analysis (PCA) of libraries. (a) Graph of PC 1 and 2.
(b) Graph of PC 2 and 3. (c) Graph of PC 1 and 3. (d) Loading plot depicting contribution of
cheminformatic parameters to PC 1 and 2. (e) Loading plot depicting contribution of
cheminformatic parameters to PC 2 and 3. PCA performed in XLStat-Student (version
2021.4.1.1209) and PCA graphs generated in GraphPad Prism (version 9.4.1 for Mac).
Cheminformatic parameters were calculated using ChemAxon (Marvin version 21.4.6, 2021)
as previously described. Source: Adapted from Morgan et al. [3]. MW = Molecular Weight,
HBA = Hydrogen Bond Acceptors, HBD = Hydrogen Bond Donors, Log P = n-Octanol/Water
Partition Coefficient, RotB = Number of Rotatable Bonds, tPSA = Topological Polar Surface
Area, log D = n-Octanol/Water Partition Coefficient, N = Number of Nitrogen Atoms,
O = Number of Oxygen Atoms, Rings = Number of Rings, ArRings = Number of Aromatic
Rings, HetRings = Number of Heteroatom-Containing Rings, SysRings = Number of Ring
Systems, SysRR = Ring Complexity, Fsp3= Fraction of sp3-Hybridized Carbons, nStereo =
Number of Stereocenters, ASA = Accessible Surface Area, RelPSA = Relative Polar Surface
Area, TC = Total Charge, VWSA = Van der Waals Surface Area.
(e)
3
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/l
2
l
(a)
(c)
l1/l
2
(b)
Figure 5.6 Shape analysis of natural, commercial, and synthetic ligands. (a) Principal Moments of Inertia (PMI) analysis for natural, commercial, and
synthetic ligands. (b) Cumulative distributions for distances from rod, disc, and sphere vertices. PMI was calculated as previously reported. Source:
Adapted from Morgan et al. [4]. (c) Statistical comparison from Kolmogorov–Smirnov Test between libraries performed as previously reported [4]. Light
blue = P-value <0.05; Dark blue = P-value <0.001. Performed in R (version 4.1.2) Graphs were created in GraphPad Prism (version 9.4.1 for Mac).
References 111
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5.7 Conclusions and Outlook
In this chapter we have presented diverse ligands known to target RNA as well
as tools to evaluate libraries and further our understanding of chemical matter
that binds to RNA. Through investigating existing data and ligands, the eld has
identied trends in properties of RNA targeted ligands and through the use of
machine-learning techniques presented here, a deeper understanding of how
ligands recognize RNA may be achieved. By examining natural, commercial, and
synthetic ligands that target RNA in the case study presented here, we identied
signicant diversity in chemical space, showing the range of molecules and by
extension interactions that can be formed between ligands and RNA. With the
continuing adaptation of machine-learning algorithms to RNA targeting, we look
forward to a deep understanding of RNA recognition by small molecules and the
rational design of novel RNA-targeted probes.
Acknowledgments
The authors thank Dr. Greta Bagnolini and TinTin Luu for their help in editing this
chapter. Funding was provided by NSF CAREER (1750375).
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