# The IMS Library: from IN-Stock to Virtual

Teodora Djikic-stojsic, Guillaume Bret, Gaëlle Blond, Nicolas Girard, Clothilde Le Guen, Claire Marsol, Martine Schmitt, Séverine Schneider, Frederic Bihel, Dominique Bonnet, et al.

► To cite this version:

Teodora Djikic-stojsic, Guillaume Bret, Gaëlle Blond, Nicolas Girard, Clothilde Le Guen, et al. The IMS Library: from IN-Stock to Virtual. ChemMedChem, 2024, 19 (20), pp.e202400381. 10.1002/cmdc.202400381. hal-04727478v2

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# The IMS Library: from IN-Stock to Virtual

Teodora Djikic-Stojsic,[a] Guillaume Bret,[a] Gaëlle Blond,[a] Nicolas Girard,[a] Clothilde Le Guen,[a, b] Claire Marsol,[a] Martine Schmitt,[a] Séverine Schneider,[a] Frederic Bihel,[a] Dominique Bonnet,[a] Mihaela Gulea,[a] and Esther Kellenberger*[a]

A chemical library is a key element in the early stages of pharmaceutical research. Its design encompasses various factors, such as diversity, size, ease of synthesis, aimed at increasing the likelihood of success in drug discovery. This article explores the collaborative efforts of computational and synthetic chemists in tailoring chemical libraries for cost-effective and resource-efficient use, particularly in the context of academic research projects. It proposes chemoinformatics methodologies that address two pivotal questions: first, crafting a diverse panel of under 1000 compounds from an existing pool through synthetic efforts, leveraging the expertise of organic chemists; and second, expanding pharmacophoric

diversity within this panel by creating a highly accessible virtual chemical library. Chemoinformatics tools were developed to analyse initial panel of about 10,000 compounds into two tailored libraries: eIMS and vIMS. The eIMS Library comprises 578 diverse in-stock compounds ready for screening. Its virtual counterpart, vIMS, features novel compounds guided by chemists, ensuring synthetic accessibility. vIMS offers a broader array of binding motifs and improved drug-like characteristics achieved through the addition of diverse functional groups to eIMS scaffolds followed by filtering of reactive or unusual structures. The uniqueness of vIMS is emphasized through a comparison with commercial suppliers' virtual chemical space.

## 1. Introduction

The initial phase of rational drug discovery is, to a large extent, based on screening a collection of chemicals.[1] Both high-throughput and virtual approaches are commonly used to search for biologically active compounds (hits) through binding of the target protein. High-throughput screening (HTS) involves the direct *in vitro* testing of many in-stock compounds and provides empirical data. The HTS libraries usually contain diverse sets of drug-like compounds to ensure broad coverage of chemical structures and maximize the probability of finding a potential drug candidate.[2] On the other hand, virtual screening deals with both physical and theoretical compounds and relies on predictive modelling to prioritize candidates for further experimental testing, offering efficient approach for exploring a wider range of compounds in drug discovery. In other words, virtual screening is not limited to in-stock collections. Virtual libraries can be extremely large, containing billions of unique compounds, which is far beyond the scope of HTS libraries.[3]

Libraries of "make-on-demand" compounds that exist only in digital form are increasingly being used in computer-aided drug design.[1,4] They can contain over a trillion chemical products utilizing a large number of building blocks combined through a small number of common chemical reactions. Among the popular commercially available ultra-large chemical spaces are Enamine REAL Space,[5] the GalaXi Space,[6] and the CHEMriya Space.[7,8]

Although a larger library is more likely to deliver hits,[1,9] dealing with big data raises practical issues implying compromise between cost, size, and time, since acquisition, storage, and maintenance presents significant efforts and resources. It was estimated that the CO₂ emissions from IT sector could increase 10 fold in 10 years.[10] In addition, the quality of a large library can be uneven – some compounds having undesirable characteristics, leading to wasted efforts in screening.[11–13] Accordingly, diversity, novelty and drug-likeliness of the library are presenting themselves as more important than its size, in order to secure a better coverage, and to explore new areas of chemical space.[4,9] Therefore, the main goal of compound library assembly has evolved from library size to chemical/biological relevance, drug-likeness, originality, and diversity.[4]

Commercial suppliers offer a wide range of tailored chemical libraries, selecting compounds based on diverse criteria depending on the research purpose, such as a fragment-based approach, focus on natural products, bias towards specific target families, or the provision of versatile compounds.[14,15] Regardless of the selection criteria, the coverage and distribution within chemical space is crucial for enhancing the probability of identifying compounds with the potential to advance into clinical phases of drug development.[16] Noteworthy, the notion of chemical space defies simple definition,[17] since it encompasses a multitude of

<footnote>[a] Dr. T. Djikic-Stojsic, G. Bret, Dr. G. Blond, Dr. N. Girard, Dr. C. Le Guen, Dr. C. Marsol, Dr. M. Schmitt, S. Schneider, Dr. F. Bihel, Dr. D. Bonnet, Dr. M. Gulea, Prof. Dr. E. Kellenberger
Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS – Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, Illkirch-Graffenstaden 67400, France
E-mail: ekellen@unistra.fr</footnote>

<footnote>[b] Dr. C. Le Guen
Inovarion, 251 rue St Jacques, Paris 75005, France</footnote>

<footnote>Supporting information for this article is available on the WWW under
https://doi.org/10.1002/cmdc.202400381</footnote>

<footnote>© 2024 The Authors. ChemMedChem published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>
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descriptions, ranging from descriptors and fingerprints to
graphs,[15,18,19] and is visualized through various methods such as
principal component analysis, t-distributed stochastic neigh-
bour embedding, approximation and manifold projection uni-
form and generative topographic mapping.[19–21] From a
pharmacological point of view, describing this space requires
evaluating structural features in three dimensions (3D), since
the shape of bioactive molecules plays a crucial role in the
strength and selectivity of protein-ligand interactions.[14,22,23] The
exploration of chemical space may be constrained by drug-
ability prerequisites[24–26] or limitations in synthetic approaches
(e.g., one-step synthesis or restrictions on reaction types).[27]
These practices have sparked debate, being a potential barrier
to innovation, while as not all drugs meet such strict
criteria.[28,29]

Synthesis undoubtedly plays a pivotal role in the design of a chemical library, with an expectation that the compounds are pure, accurately identified, and stable under typical storage conditions.[2,30] Furthermore, the versatility of scaffolds within the library is a key element for optimizing hit/lead compounds, enabling the synthesis of a wide range of derivatives for further exploration and development. At this stage, focused virtual libraries provide a valuable tool to rapidly study the structure-activity relationship, provided that a computational pipeline is available to predict target binding or pharmacologic effect. It is also important to emphasise that virtual compounds can be synthesized with reasonable effort, which may require computational methods for a large-scale estimation beyond the expertise of individual chemists. These methods, reliant on known synthetic routes and reaction databases, may not be applicable to novel compounds,[31,32] prompting alternative strategies such as storing molecules as chemical reactions and building blocks to ensure accessibility while maintaining diversity and originality.[33]

Here we propose a method to create a diverse and original library with unique scaffolds synthesized using innovative synthetic routes, by combining the expertise of chemoinformaticians, organic synthetic chemists,[34-39] and medicinal chemists.[40-42] Starting with the collection of all chemicals stored at Laboratoire d'Innovation Thérapeutique (LIT) - University of Strasbourg, we have developed the Essential chemical library (elMS), a diverse set of 578 compounds which are plated and ready for HTS. Compounds were selected based on clustering using molecular fingerprints and focusing on originality as determined by comparison to purchasable drug-like compounds collected from 25 trusted suppliers. A companion Virtual chemical library (vIMS) was also proposed, aiming at facilitating hit optimization by computer-assisted method. The vIMS library consists of 821,070 virtual compounds derived from a selection of original scaffolds. Novel compounds were generated by adding different substituents on the specified connection points of the central scaffold. In order to guarantee their feasibility, these novel compounds have to adhere to guidelines established by medicinal chemists who originally conceptualized the core scaffold. Compounds were further filtered based on their chemical properties and substructures, to remove reactive and/or peculiar structures. Finally, we

compared our virtual library to the Enamine REAL Space, using
the scaffold similarity and concluded that 85% of generated
compounds could not be found there. Advantage of this library
is the fact that it is based on well-known synthetic pathways
and expert knowledge of medicinal chemists, which secures the
easy synthesis of novel derivatives and facilitates hit-to-lead
optimization.

## 2. Results and Discussion

The compounds initially collected had been synthesized during the past decade by organic chemists from three groups at the Laboratoire d'Innovation Thérapeutique (University of Strasbourg). Their design and synthetic strategies to access them originated from diverse contexts. Thus, some of these compounds were synthesized as a part of medicinal chemistry projects, while others were synthesized within fundamental research projects aiming original structures and innovative synthetic routes. Out of all compounds present in stock, our aim was to curate a small collection while exploiting the originality and maximizing the diversity. Given that the elMS library is intended mainly for academic use, with limited resources compared to industry, and should represent the smallest possible collection that retains the diversity and originality of the compounds. For this purpose, the chemists selected types of compounds sparsely (or not) reported, (poly) cyclic or heterocyclic, taking into account their three-dimensional nature, the globularity of their shape and their structural modularity. The second objective – the creation of the vIMS collection, was to augment the collection with virtual derivatives ready for a computing pipeline for hit identification and/or optimization.

2.1. IMS₀, the Complete In-Stock Collection

The complete in-stock collection was firstly filtered to remove
heavy or highly hydrophobic compounds (molecular weights >
800 or partition coefficient (AlogP) > 8), organometallic compo-
nents (e.g., Sn and Si), and toxic molecules containing reactive
groups (such as Michael acceptor) (Supplementary Materials 1).
We have not applied strict filtering rules, as many of the
compounds were not synthesized with medicinal chemistry
application as their primary objective but rather to introduce
unique synthetic pathways. For instance, compounds contain-
ing mainly hydrophobic groups could evolve towards more
polar derivatives by adding hydrophilic groups. Likewise, on
some compounds, protective groups such as the tert-butylox-
ycarbonyl protecting group were retained, which could be
readily removed. Lastly, we reintroduced after filtering 305
compounds showing an exceptional scaffold or obtained from
an innovative chemical route. IMS₀ contains 9,538 compounds.

We evaluated the originality of IMS₀ by comparing it to
commercially available compounds, utilizing Bioinfo-DB as
reference for this assessment. Bioinfo-DB is the in-house curated
database of screening compounds, gathered from 25 trustable
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chemical suppliers chosen for their proven track record in delivering compounds within 3–4 weeks, and good price-quality ratio (Supplementary Materials 2). We observed a significant overlap between IMS₀ collection and Bioinfo-DB. A total of 2,558 of the 3,978 Bemis-Murcko (BM) scaffolds in IMS₀ (64%) were found among the 2,878,027 BM scaffolds of Bioinfo-DB (Tanimoto similarity = 1). We also evaluated to what extent IMS₀ collection covered the ChEMBL-clinical dataset of approved compounds and compounds in clinical research. Only 230 BM scaffolds in IMS₀ (~6%) were identified among the 2,509 scaffolds of ChEMBL dataset.

We evaluated the diversity of IMS₀ by clustering based on the similarity of structural features. The 9,538 compounds were distributed into 633 clusters and 1,087 singletons.

## 2.2. eIMS, the Essential Library for Screening

IMS₀ was filtered to remove the compounds similar to those found in the commercial collection Bioinfo-DB. This reduced set consists in 1,754 compounds, representing 521 clusters and 1,229 BM scaffolds. Availability, purity and stability were further considered for the plating. In total, 587 compounds were selected to create eIMS.

As compared to IMS₀ and Bioinfo-DB, eIMS showed the highest diversity. The Area Under the Curve of cyclic system retrieval (CSR) for eIMS is 0.5, indicating a ratio of one compound per BM scaffold (Figure 1C), while for ChEMBL-clinical, Bioinfo-DB and IMS₀ were 0.6, 0.84, and 0.74, respectively. In ChEMBL-clinical, Bioinfo-DB, and IMS₀, a few BM scaffolds are overrepresented, notably simple ones including

structures such as benzene, pyrrole, and imidazole (Supplementary Materials 3).

Drug-likeness was assessed using typical molecular descriptors, including molecular weight (MW), partition coefficient (AlogP), polar surface area (PSA), and Quantitative Estimate of Druglikeness (QED). Notably, the Bioinfo-DB, comprising commercially available compounds, was shown to have many molecules with high MW and AlogP values. Some of the clinically relevant compounds sourced from ChEMBL-clinical dataset also exhibited high AlogP values, aligning with the concept of not strictly adhering to Lipinski's Rule of 5. In the case of eIMS, the majority of compounds fell within the MW range of 300 to 500 and AlogP between 1 and 5. However, there were exceptions, such as one compound with a significant molecular weight of 800 and another with an AlogP nearing 10 (Figure 2).

The 3D structure of compounds is an important factor in determining the strength and selectivity of their protein-ligand interactions. 3D structures of lowest energy conformation obtained by Corina were plotted on Normalized Principal Moment of Inertia ratios (NPR) plot. We can observe that the majority of compounds exhibit rod and disk shapes, while spherical compounds are relatively scarce. Spherical compounds are almost exclusively present in the Bioinfo-DB, with just a few representatives in other libraries (Figure 2D).

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Comparison of IMS₀ and eIMS with commercial compounds and drug candidates. A) Overlap between BM scaffolds of IMS₀, Bioinfo-DB and ChEMBL-clinical dataset (left), and eIMS, Bioinfo-DB and ChEMBL-clinical datasets (right). B) Distribution of maximal Tanimoto similarities of BM scaffolds from IMS₀ (left) and eIMS (right) to Bioinfo-DB. C) Cyclic System Retrieval curves of IMS₀, eIMS, Bioinfo-DB and ChEMBL-clinical datasets. D) The percentage of compounds covered by each scaffold calculated for the first 10 most populated scaffolds. In A–D, IMS₀ data is coloured in purple, eIMS in blue, Bioinfo-DB in pink and ChEMBL clinical dataset in green.</figcaption></figure>
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<figure><img src="image_8.png" /><figcaption>**Figure 2.** Properties of compounds in IMS₀, eIMS, Bioinfo-DB and ChEMBL clinical dataset, A) MW and AlogP B) main descriptors for drug-likelihood of compounds: MW, AlogP, PSA, and number of hydrogen bond donors, acceptors and rotatable bonds. For visibility reasons lower and upper limits are represented in red (MW: 200–500; AlogP: < 5, PSA < 140 Å²; HBD < 5; HBA < 10; NRot < 10). C) Distribution of QED values. D) NPR plots: depicts shape of the compounds. Darker shades indicate highly populated areas. In A–D, IMS₀ data is coloured in purple, eIMS in blue, Bioinfo-DB in pink and ChEMBL-clinical dataset in green.</figcaption></figure>

## 2.3. vIMS

### 2.3.1. Core Scaffolds

The 249 core scaffolds and substitution points were formulated by medicinal chemists who initially conceptualized them. Not all of the scaffolds originated from eIMS, and similarly, not all eIMS scaffolds are included among these core scaffolds. Together, these core scaffolds cover 50% of the eIMS, while 89 core scaffolds are absent from the eIMS molecules. It is important to note, that the core scaffolds didn't always align with the BM scaffolds. While in majority of cases, the core scaffolds consisted solely of the cyclic portion of the molecules, therefore matching the BM scaffolds, there were also cases where a few extra heavy atoms were included. Of these, 48 core scaffolds contained only one extra heavy atom (–CH₃, –OH, –Cl, –NH₂), while just two core scaffolds featured an additional six extra heavy atoms. For the remaining 59 core scaffolds, the average ratio between the number of heavy atoms in the core scaffold and the number of heavy atoms in BM scaffolds was

1.19. Among the core scaffolds, the majority have up to three substitution points. Approximately 80% have only one or two substitution points, with 8% having three substitution points. There is a single core scaffold with nine substitution points, while only five core scaffolds have eight. The core scaffolds are diverse in terms of molecular descriptors (Figure 3). They vary in size, ranging from seven to 38 non-hydrogen atoms, with molecular weights spanning from 100 up to 500 Da. Their AlogP values range from –2 to 7.5. Each of these core scaffolds contains heteroatoms, with the majority featuring heterocyclic rings. Furthermore, the shapes of the scaffolds are also diverse, ranging from flat, rod-like, or disc-like structures to almost spherical forms.

### 2.3.2. IMS R-Groups

The IMS R-group collection represents a set of 1,028 R-groups collected using the R-group decomposition on IMS₀ library, literature collection and Enamine REAL building blocks. The
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 3.** Properties of the vIMS core scaffolds, A) MW and AlogP; B) Proportion of heteroatoms (N, O, S) in the total number of non-hydrogen atoms; C) Shapes. Areas of higher density in NPR plots depicting are represented in darker shade; D) Number of rings and number of ring assemblies; E) Distribution of heterocyclic rings; F) Distribution of stereo centres, and G) Distribution of rotatable bonds.</figcaption></figure>

majority of R-groups were obtained through the R-group decomposition protocol. While many of these R-groups were commonly found in the literature, some of the more prevalent ones were absent (Figure 4A). These missing groups were then supplemented from literature sources. Additionally, four types of R-groups (carboxylic, sulfonyl, amino acid side chains and ethers) were obtained from the Enamine REAL building blocks collection. Although these particular R-group types were not common in the original collection, they were introduced because of their compatibility with the synthetic route. Majority (65%) of the unique R-groups in the dataset are coming from the R-groups decomposition protocol. About 33% originate from the Enamine REAL building-blocks, and only 2% from common substituents found in the literature (Figure 4B). Examining the set of R-group multiplicities, it is clear that hydrogen atoms have the highest occurrence – filling 33.3% of the total R-groups from a single, unique substituent. Bulky substituents (with more than 10 non-hydrogen atoms) were the least common, accounting for only 3% of the set, and mostly appearing as unique (non-duplicates). The most common substituents, both as unique (966 out of 1028) and as duplicates (1957 out of 4629), were those classified as hydrophilic, encompassing any substituents containing hydrogen bond donors and/or acceptors as oxygen and/or nitrogen atoms. This R-group class overlaps with heterocyclic, aromatic, and bulky substituents. To analyse the composition of each R-group class separately we could further divide them in sub-classes (Figure 4D). Among halogens, fluoride was the most prevalent, comprising nearly 50% of all halogens. This was followed by chloride (40%), bromide (7.5%), and finally iodide, which appeared in a smaller number of repetitions (less than 3% of halogens). The class of hydrophobic R-groups is composed of alkyl, alkenyl, alkynyl, and cycloalkyl substituents. Among these,

71.3% of the hydrophobic R-groups has only single bonds, majority being methyl and ethyl, followed by substituents with double bond (11.3%) cycloalkyl (10.3%) and triple bond (7%). The class of hydrophilic substituents is the most numerous and diverse. Some common substituents include: R-groups containing amide nitrogen (16.4%, including sulfonamides, imides, lactams, and carbamates), amines (14%), N-heteroaryl (10% of R-groups), carboxylic acid (3.8%), esters and lactones (2.6%). The remaining 53.2% consists of a combination of ethers, hydroxy, nitro-groups, sulfonyl or sulfinyl groups, etc. Heterocyclic R-groups can be described as those containing only N-atoms (53%), O-atoms (13%), S-atoms (11.5%), and combined heteroatoms (22.5%). Majority of bulky R-groups are heteroaromatic (65%) and aromatic substituents (making 31% of this subclass). Among the bulky ones, only few substituents are non-aromatic heterocycles (2.7%), and carbocycles (1.4%). Additionally, R-groups vary in size (ranging from 1 to 18 non-hydrogen atoms, with molecular weights spanning from 1 up to 260 Da), AlogP (values range from -3 to 4.5), numbers of heteroatoms, hydrogen-bond donors and acceptors, etc. (Supplementary Materials 4).

### 2.3.3. Generation of Novel Compounds

Using the scripts for the generation of novel compounds (explained in details in Materials and Methods section), a comprehensive set of over one million molecules was derived from 249 core scaffolds. As seen above, core scaffolds and R-groups varied in size, AlogP values, number of rings, rotatable bonds, etc. The combination of large compounds with one or two bulky R-groups resulted in molecules with significantly elevated molecular weights, while combining high AlogP
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 4.** Description of the IMS R-group collection A) Overlap between initial R-group decomposition collection of substituents and most common substituents from literature; B) Final proportion of unique R-groups from different sources. C) The number of R-groups, per class, with all multiplicities preserved (blue) and count of unique R-groups (turquoise); D) Percentage of different functional groups among different classes of R-groups. Halogens: fluoride – blue, chloride – turquoise, bromide – pink, iodide – red; Hydrophobic: alkyl – blue, alkenyl – turquoise, cycloalkyl – pink, alkynyl – red; Hydrophilic, amide nitrogen – turquoise, amines – pink, aromatic nitrogen – red, acidic oxygen – orange, ester oxygen – yellow and others – blue; Heterocyclic, N-atoms only – blue, O-atoms only – pink, sulphur atoms only – red, and combined – turquoise; Bulky: aromatic heterocyclic – blue, aromatic – turquoise, non-aromatic heterocyclic – pink, and non-heterocyclic non-aromatic – red.</figcaption></figure>

scaffolds with hydrophobic substituents led to compounds with elevated AlogP values. Moreover, scaffolds with substitution points on or near heteroatoms, combined with alkynyl or halogens, could become reactive and potentially toxic groups. In order to remove large, lipophilic, peculiar or reactive molecules specific filters were applied and substructures were incorporated into filtering rules using SMARTS notation. Our script for generating the compounds itself allows for the incorporation of conditions that would prioritize small substituents for large scaffolds, or hydrophilic substituents for hydrophobic scaffolds. However, in this case, we opted for a "post-generation" approach, applying a set of filtering rules to eliminate compounds with undesired characteristics (Supplementary Materials 5). Following this filtering step, **821,069** compounds were retained. The originality, diversity, and drug-likeness of newly generated compounds were evaluated using the same criteria as applied to the eIMS compounds.

To determine originality, BM scaffolds of vIMS were compared with REAL Space using SpaceMACS (Figure 5B). From

**821,069** molecules **437,441** Bemis-Murcko scaffolds were extracted. The comparison of BM scaffolds of vIMS molecules with REAL Space yielded 700,682 compounds with original scaffolds.

The scaffold diversity of vIMS was visualized using a CSR plot (Figure 5C). The CSR of vIMS (AUC = 0.74) exhibits lower enrichment compared to Bioinfo (AUC = 0.84) but higher than ChEMBL-clinical (AUC = 0.6), indicating a greater scaffold diversity in comparison to the Bioinfo-DB library collection and lower than ChEMBL-clinical. In Bioinfo-DB, one BM scaffold was notably overrepresented, accounting for 6% of the compounds in the library. Conversely, within ChEMBL-clinical, a single BM scaffold covered 9% of the compounds. In both databases, benzene is the most prevalent scaffold. Following benzene, the second most common scaffold in Bioinfo-DB is N-phenyl-benzene-sulfonamide, making up 1% of the compounds, whereas in ChEMBL-clinical, it is benzyl-benzene, also accounting for approximately 1% of the compounds (Supplementary Materials 3). In vIMS, the two most represented BM scaffolds are
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 5.** Comparison of vIMS with commercial compounds, drug candidates and on-demand library. A) Identity overlap between BM scaffolds of vIMS (orange), Bioinfo-DB (pink) and ChEMBL clinical dataset (green); B) Distribution of maximum MCS similarity of BM scaffolds of vIMS compounds to REAL space; C) CSR plot of vIMS (AUC = 0.75); D) Percentage of compounds per BM scaffold of vIMS for the 10 most populated scaffolds.</figcaption></figure>

encompassing slightly over 2% of the compounds (Figure 5D). Since the vIMS was filtered to only keep drug-like compounds, none of the compounds have molecular weights larger than 600 or AlogP values greater than 6 (Figure 6A and 6B). The distribution of QED values is shifted towards smaller values, which could be attributed to the uniqueness of the compound scaffolds (Figure 6C). It's worth noting that these are novel compounds with unique scaffolds, and as such, the application of the QED score may require careful consideration.

In terms of the shape of the newly generated virtual compounds, we can observe the increase in spherical molecules compared to IMS₀ and eIMS (Figure 6D). The distribution of spherical shapes within vIMS compounds is comparable to that seen in the Bioinfo-DB library. The creation of vIMS has yielded molecules with similar characteristics to eIMS but enhanced three-dimensionality. This enhancement could potentially translate to compounds with improved potency and selectivity during the optimization phase.

To visualize the coverage of the chemical space, BM scaffolds were projected using Uniform Manifold Approximation and Projection (UMAP), with compounds represented by circular, 1024 bits, Morgan fingerprints, with radius 2. In the Figure 7, compounds are color-coded based on the library. Due to the large size of the libraries only 25% of Bioinfo-DB and vIMS were used for the creation of the map. The map reveals areas of overlap between Bioinfo-DB and vIMS, but they generally occupy distinct regions. The even distribution of BM scaffolds from eIMS across the vIMS region reflects that vIMS compounds represent analogues of eIMS library.

ChEMBL clinical BM scaffolds predominantly occupy the region shared by both vIMS and Bioinfo-DB. However, some scaffolds project onto the vIMS region, suggesting potential similarities to certain vIMS BM scaffolds rather than those of Bioinfo-DB.

When comparing the number of possible three-point (3 pt) and four point (4 pt) pharmacophores we observed the significant increase in the number of possible pharmacophores and their combinations, especially 4 pt pharmacophores. The number of possible three point 3-pt point pharmacophores increased from 14,479 in eIMS to 24,370 in vIMS, while number of possible 4-pt pharmacophores increased from 91,647 to 748,280. The vIMS library has clearly the advantage of covering a broader pharmacophoric space, which may result in more potent and more selective compounds. This could be especially important in the process of hit-to-lead optimization. Three-point pharmacophores were distributed relatively evenly across all types and distances, except for charged pharmacophores, where just a few compounds exhibited more than one charged feature. In four-point pharmacophores, combinations of acceptor-donor-aromatic-and hydrophobic features prevailed. In particular, combinations of Acceptors (A), Donors (D), Aromatic (R), and/or Hydrophobic (H) moieties – such as AA | DA | RA | DA | RA | RD, AA | HA | RA | HA | RA | RH, and DA | RA | RA | RD | RD | RR – spanning all distances, collectively account for approximately 15% of all potential combinations. Similarly, to 3 pt pharmacophores, a small number of compounds exhibited two positive (P) or two negative (N) charges within their pharmacophores (Figure 8A). Moreover, within the vIMS library, 25% of compounds display over 100 potential 3 pt pharmacophores, while an even larger
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Figure 6.** Properties of compounds in vIMS. A) MW and AlogP; B) Main descriptors for drug-likeness of compounds: MW, AlogP, PSA, and number of hydrogen bond donors, acceptors and rotatable bonds. Lower and upper limits are represented in red (MW: 200–500; AlogP: < 5, PSA < 140 Å²; HBD < 5; HBA < 10; NRot < 10); C) Distribution of QED values; D) Shapes. Areas of higher density in NPR plots are represented in darker shade.</figcaption></figure>

percentage, 30%, expresses over 100 4 pt pharmacophores. This indicates a higher level of complexity among compounds in the vIMS library when compared with those in the eIMS and IMS₀ libraries.

The synthetic accessibility (SA) of the compounds was evaluated using the SA score, a complexity-based metric that ranges from 1 to 10, with 1 indicating the easiest synthesis. This score considers the frequency of molecular fragments in the PubChem database and the overall complexity of the compounds.[31] The majority of the compounds from the vIMS library had SA scores between 3 and 4, with an average score of 3.79, suggesting they are moderately difficult to synthesize. It's important to note that these molecules are designed to be synthesized in 5 to 7 steps, which increases their synthetic difficulty but also enhances their complexity, potentially improving their affinity and selectivity. Similarly to Bioinfo-DB and ChEMBL-clinical majority of the compounds in vIMS had SA score of 3, with just a few compounds with SA score 6. Additionally, the distribution of SA scores for vIMS compounds closely matches that of clinical compounds, supporting the idea that bioactive compounds tend to be more complex (Figure 9).

developed two original and diverse, in-house screening libraries from the initial in stock collection of **9,538** compounds. The essential library (eIMS) comprises **578** compounds arranged on plates, ready for High Throughput Screening (HTS). The virtual library (vIMS) contains **821,069** computationally-generated compounds, prepared for Virtual Screening (VS). These libraries are based on well-established synthetic pathways and the expert knowledge of medicinal chemists, to secure easy synthesis of novel derivatives and facilitate hit-to-lead optimization. Additionally, we have prepared scripts for the generation of new molecules along with an original collection of substituents (IMS R-groups), governed by rules for their combinations, based on the number of substituents on the core scaffold. The entire protocol is designed to be quick to use and easy to improve. With the ongoing synthesis of novel molecules and their derivatives, the collection of R-groups will be regularly updated. Furthermore, there is flexibility for refining filtering rules, allowing chemists to address any challenges related to synthesizability. This approach promises an efficient and adaptable framework for updating and refining the resources for drug discovery.

### 3. Conclusions

Through collaborative efforts between chemoinformaticians, medicinal, and synthetic chemists, we have successfully
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /><figcaption>**Figure 7.** UMAP projection of BM scaffolds in chemical space eIMS (blue), vIMS (orange), Bioinfo-DB (pink), ChEMBL-clinical (green) libraries.</figcaption></figure>

# 4. Material and Methods

## 4.1. eIMS Protocol

### 4.1.1. Collecting and Preparing Libraries

All the chemical structures of compounds that were synthesized and stored at Laboratoire d'Innovation Thérapeutique (LIT) – University of Strasbourg were collected and a database containing **10,814** compounds was created. A Pipeline Pilot (Dassault Systèmes, Paris, France) protocol was used to remove compounds with wrong topology, then standardize and correct the structures (e.g. pyridine and sulfonamides). Duplicates were detected based on canonical SMILES and removed. Finally, typical molecular descriptors (molecular weight (MW), partition coefficient (AlogP), polar surface area (PSA), hydrogen bond donor (HBD) and acceptor (HBA) counts, number of rotatable bonds, rings, true stereo centres, quantitative estimate of drug-likeness (QED),[24] and violations of Lipinski's rule-of-five) were calculated. This was followed by a soft filtering using **OpenEye** Filter v.2021.2.0 (OpenEye Scientific Software, 7 Santa Fe, U.S.A.), (Supplementary Materials 1). Typically, the very large

(MW > 750), highly flexible (> 15 rotatable bonds), and reactive compounds were removed, except for exceptional compounds that encompassed a very original scaffold, which were tagged with a warning sign. The filtered set, named IMS₀, contained **9,538** compounds.

#### 4.1.1.1. Comparison with known Compounds

**Known compounds.** The originality of in-stock compounds was assessed by comparison with the Bioinfo-DB database and ChEMBL-clinical dataset. Bioinfo-DB is an in-house curated database built from catalogues of in stock provided by chemical suppliers (Supplementary Materials 2). The ChEMBL-clinical dataset was collected from the ChEMBL website (https://www.ebi.ac.uk/chembl/). Both datasets were prepared using the same Pipeline Pilot Protocol and **OpenEye** Filter as described above. The final number of compounds was **13,236,203** in Bioinfo-DB, and **3,484** in ChEMBL-clinical dataset.

**Extracting Bemis-Murcko Scaffolds.** Bemis-Murcko (BM) scaffolds are commonly used in molecular analysis to simplify the comparison of large chemical sets.[12,43–45] BM scaffolds are
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /><figcaption>**Figure 8.** Pharmacophores in IMS₀ (purple), eIMS (blue), vIMS (orange) libraries. A) Number of combinations of different distances for 3-point (up) and 4-point (down) pharmacophores; B) Distribution of the percentage of 3 pt (up) and 4 pt (down) pharmacophores expressed by each compound.</figcaption></figure>

represented by all the ring systems and links between them, disregarding substituents on rings. The BM scaffolds of IMS₀, Bioinfo-DB and ChEMBL-clinical dataset were extracted using custom python RDKit script (https://www.rdkit.org/). The IMS₀ resulted in **3,978** BM scaffolds, Bioinfo-DB in **2,878,027** BM scaffolds, and ChEMBL-clinical in **2,509** BM scaffolds.

**Similarity based on Fingerprint.** Similarity based on chemical fingerprint is a fast method, allowing the all-against-all comparison of large dataset. Comparison was based on 2048 bits Morgan Fingerprints with radius 2 and Tanimoto metric, calculated using a custom Python RDKit script. Morgan fingerprints, which capture the specific circular substructures around each atom, were shown to describe important molecular features for the activity.[46,47]

**MCS between similar molecules.** Maximal Common Substructures (MCS) between similar compounds (Tanimoto similarity > 0.77) were extracted using FMCS algorithm implemented in Python RDKit.[48] A MCS score was obtained for each compound by dividing the number of non-hydrogen atoms forming the MCS it contains by the total number of non-hydrogen atoms of

the compound. The maximal MCS score is equal to 1 (MCS covers the full compound).

### 4.1.2. Determining the Diversity of the Library

The IMS₀ library was clustered using Butina algorithm[49] with Daylight fingerprints based on a python RDKit script. The homogeneity of clusters was assessed by considering MCS, thereby defining a dissimilarity cut-off of 0.3 – Tanimoto similarity > 0.7.

### 4.1.3. Compound Selection

In order to select the most original dataset, compounds with scaffold to scaffold-to-scaffold Tanimoto similarity to Bioinfo-DB less than 0.8 were selected. To ensure the diversity of eIMS, one to two compounds per cluster were selected based on their molecular characteristics. Typically, compounds with the lowest molecular weight were prioritized, as molecular weight is likely
<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /><figcaption>Figure 9. Distribution of SA scores in IMS₀ (purple), vIMS (orange), ChEMBL-clinical (green), and BioinfoDB (pink) libraries.</figcaption></figure>

to increase during compound optimization. Compounds with warning signs were avoided. For 13 clusters that contained versatile scaffolds, typically 2 to 3 compounds per cluster were chosen. However, for the 2 large clusters, a broader selection was made, with 14 and 10 compounds, respectively.

## 4.2. vIMS Protocol

### 4.2.1. Selection of the Core Scaffolds

From the eIMS compounds, **249** core scaffolds were chosen by chemists based on chemical accessibility and the potential to modulate structure. It is important to note that core scaffolds were not defined using the BM approach, but rather based on chemists' expertise in synthetic routes. Consequently, a core scaffold encompasses a substructure common to one or more compounds and is annotated with positions where substituents could be introduced. For the majority of the core scaffolds, any substituent from the R-groups collection (see below) was allowed. However, for a limited number of core scaffolds, types of substituents (such as alkyl, aryl, amino-acid side chain, etc.), were precisely specified by the chemists.

### 4.2.2. IMS R-Groups Collection

The IMS dataset of R-groups for the decoration of core scaffolds was prepared from IMS₀ using a Python RDKit script. First, each compound was aligned to its parent core scaffold, thereby identifying the unmatched moieties which were then stored as R-groups. R-groups were stored in the single list without the

information about their position. R-groups connected to the core scaffold with a double bond as well as R-groups with more than one connection point were removed. It is important to mention that hydrogens were considered as R-group and that all duplicates were kept to take into consideration the frequency of each R-group in the IMS₀ (Figure 10). The collection of R-groups was then supplemented with common substituents for drug development[50-52] and common Enamine REAL Building blocks (precisely amino-acids, sulfonyl halides, carboxylic acids, and alcohols) (Figure 11). The common substituents were duplicated as frequently as the average number of times the common substituents appear in the IMS R-group collection (excluding Hydrogen). The final number of R-groups was **4,629**, out of which **1,028** R-groups were unique.

### 4.2.3. Generating Possible Molecules

Novel molecules were generated from core scaffolds using two separate custom python RDKit scripts, depending whether substituents were chosen automatically from the R-groups collections or under the guidance of the chemists.

Using the IMS R-groups collection. R-groups were connected to substitution points of the core scaffolds. Several rules were set in order to avoid generating irrelevant molecules, such as poly-halogenated compounds or multi-charged functional compounds (Figure 12). Firstly, R-groups were classified into distinct classes based on their physicochemical properties: hydrogen (H), halogen (F, Cl, Br, and I), aromatic (substituents consisting of aromatic carbon atoms with fewer than 10 non-hydrogen atoms), heterocyclic (aromatic or non-aromatic substituents containing a heteroatom within the cycle, each with fewer than
<figure><img src="image_23.png" /></figure>

Input:

<figure><img src="image_24.png" /></figure>

Output:

<figure><img src="image_25.png" /><figcaption>**Figure 10.** The R-group decomposition protocol. Initially, molecules from the selected database that contain the core scaffold as a substructure are aligned. All decorations at all the points where substitutions occurred at least once are then extracted as R-groups and stored in a list. In addition to the R-group collection, the script generates the core scaffold with its potential substitution points.</figcaption></figure>

10 non-hydrogen atoms), hydrophobic (including alkyl, alkenyl, alkynyl, and cycloalkyl), hydrophilic (substituents with oxygen and/or nitrogen, such as hydroxyl, carbonyl, amine, amide groups, etc.), sulfur-containing substituents (such as sulfonamide, thiol, sulfonyl, etc.), and bulky substituents (representing any substituents containing more than 10 heavy atoms). Secondly, core scaffolds were divided into three groups according to the number of substitution points: (1) 1, (2) 2–4 and (3) 5–9. If there was only one substitution point, all unique R-groups were connected to the core scaffold, yielding 1028 derivatives. However, if there were more than one substitution point, a total of 1000 combinations of R-groups was enumerated, in agreement with the following rules:

* Core scaffolds with 2–4 substitution points: #hydrogen ≤ 1, #hydrophilic ≤ 2, #hydrophobic ≤ 3, #halogen ≤ 2; #sulfur ≤ 2, #heterocyclic ≤ 2; #aromatic ≤ 2; #bulky groups ≤ 1

* Core scaffolds with 5–9 substitution points: 2 ≤ #hydrogen ≤ 4; #hydrophilic ≤ 3; #hydrophobic ≤ 3, #halogen ≤ 2, #sulfur ≤ 2, #heterocyclic ≤ 2; #aromatic ≤ 2; #bulky groups ≤ 2

R-groups of each type were picked randomly. Note that more frequent R-groups are more likely to appear in vIMS. The script generated > 2000 molecules in SMILES format per second (Intel® Xeon®, W-1250 CPU, 3.30 GHz)

**Substituent type defined by medicinal chemists.** As indicated by chemists who conceptualized the core scaffolds, R-groups were categorized based on the atoms at the connection point into the following categories: alkyl (66 substituents), amino acids (35 substituents), aryl and benzyl (125 substituents),
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /><figcaption>**Figure 11.** Example of R-groups drawn from Enamine REAL building blocks.</figcaption></figure>

carbonyl (145 substituents), heterocyclic (193 substituents), sulfonyl (178 substituents), and small substituents (100 substituents containing up to 4 atoms such as halogens, amine, hydroxyl, etc.). The core scaffold and R-groups were combined based on the defined substituent types for each substitution point. If multiple R-group types were possible for the same substitution point, all members of these types were merged and shuffled. The total number of R-group combinations was set to 10,000, which is ten times greater than if R-groups were not defined by chemists. The combinations were randomly selected without additional filtering rules.

### 4.2.4. Filtering Generated Compounds

The set of virtual compounds generated from core scaffolds and R-groups was filtered using **OpenEye** Filter v.2021.2.0 to exclude poorly drug-like compounds based on simple molecular descriptors such as MW, AlogP, HBA, etc. In addition to these, additional filtering was applied to exclude undesirable substructures and/or limit the occurrence of specific substructures (alkynes, carboxylic acids, sulfonamides, etc.) (Supplementary Materials 5). Some rules were identified through visual inspection, while others were gathered from the literature.[54] All rules were applied using **OpenEye** Filter v.2021.2.0. Compounds that passed the filter were subsequently subjected to 3D conformer generation with **Corina** (Molecular Networks, GmBH, Germany) but they are discarded if no valid geometry could be produced.

In the next step, the compounds were submitted to a **Pipeline Pilot** (Dassault Systèmes, Paris, France) protocol to standardize and correct the structures, remove and calculate general molecular descriptors (MW, AlogP, PSA, HBD, HBA, number of rotatable bonds, rings, true stereo centres, QED, and violations of Lipinski's rule-of-five). Finally, compounds with AlogP less than -2 and larger than 6 were removed.

### 4.2.5. Originality, Diversity, Drug-Likeliness, and Synthetic Accessibility of Novel Libraries

As mentioned above, the originality of eIMS library was estimated by comparison of BM scaffolds with Bioinfo-DB in-house library and ChEMBL subset of clinically relevant molecules. The percentage of original BM scaffold in vIMS was obtained by comparison with REAL Space using SpaceMACS (version 1.0.0, M. Rarey, Hamburg, Germany).[55] Scaffold diversity was measured using cyclic system retrieval (CSR) curves. The fraction of Bemis-Murcko scaffolds was plotted against the cumulative fraction of the database. The most diverse library would have CSR plot closest to a diagonal (AUC = 0.5).[15]

The shape of molecules were represented on a Normalized Principal Moment of Inertia ratios (NPR) plot classifying compound shapes on a triangular plot into rods, discs, and spheres.[56,57]

Furthermore, the BM scaffolds of eIMS, vIMS, Bioinfo-DB and ChEMBL-clinical libraries were projected into the chemical space using the Uniform Manifold Approximation and Projection (UMAP) algorithm.[58] Due to the large number of scaffolds, only 25% of the Bioinfo-DB and vIMS were selected for analysis. We used UMAP to embed scaffolds into a 2D space for easy interpretation. UMAP simplifies data by creating a detailed graph of the dataset in high dimensions. Subsequently, it aims to recreate a simplified version of this graph in fewer dimensions while preserving the overall structure of the data at both local and global levels. Compared to other manifold learning techniques (such as tSNE), UMAP is faster and better preserves the global structure of the data.[59-61] The UMAP mapping custom script was prepared in RDKit, using 1024 bits Morgan 2 fingerprints, to describe molecules, and Tanimoto distance. The minimum distance between points was set to 0.3 (similarity 0.7). A smaller number of neighbours focuses on optimizing distances between similar compounds, ensuring that small differences are well represented. With a larger
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /><figcaption>**Figure 12.** Generation of new molecules. A. Overview of the protocol. A matrix is constructed from the list of R-groups and the number of substitution points on the core scaffold. R-group combinations are then randomly generated, and these combinations are subsequently filtered based on specified conditions. Following this, each R-group combination is connected to the core scaffold, and the resulting new molecule is enumerated. B. Example of new molecules generation on three selected, previously published core scaffold.[35,37,40,53]</figcaption></figure>

number of neighbours, the emphasis is on optimizing distances between less similar compounds. We found that 50 neighbours provided an optimal balance for our analysis.

Diversity can also be characterized by considering the potential number of pharmacophores that a compound could express. A 3 pt pharmacophore is defined by a set of three molecular 'features' (such as hydrophobic, aromatic, hydrogen bond donors, hydrogen bond acceptors, acidic, and basic groups) with 3 distances between them. Similarly, a 4-point pharmacophore is defined by 4 features and 6 distances.[62] To evaluate the enhancement in the number of potential pharmacophores within the initial IMS₀ collection, as well as eIMS and vIMS libraries, we calculated and compared counts of possible 3 pt and 4 pt pharmacophores per compound. Using Pipeline Pilot, we computed pharmacophore fingerprints based on

topological (bond) distances. These pharmacophore keys consist of triplets or quartets of molecular features and the corresponding bond distances among them, enabling the extraction of the number and types of possible 3-point and 4-point pharmacophores. We considered six types of pharmacophores: aromatic, hydrophobic, hydrogen bond donor, hydrogen bond acceptor, positive charge, and negative charge. For the perception of a hydrophobic ring, we allowed the presence of atoms with relatively low hydrophobic factors, provided that the overall hydrophobicity of the ring is sufficiently high. All combinations of features with distance ranges between 2 to 10 bonds were considered. The maximum allowable number of possible pharmacophores per compound was set to 100.

The synthetic accessibility of the compounds was assessed using the synthetic accessibility score (SA score). This complex-
<figure><img src="image_30.png" /></figure>

ity-based method assigns a score to each molecule, ranging from 1 to 10, with 1 indicating that a molecule is easy to synthesize. Unlike machine learning approaches, this algorithm breaks down the molecule into smaller fragments and checks the frequency of each fragment in the PubChem database. The underlying assumption is that molecules with frequently occurring fragments in the PubChem database are easier to synthesize compared to those with many rare or unique fragments. Additionally, the score accounts for the complexity of the molecule, increasing based on factors such as the number of heavy atoms or rings.[31]

## Author Contributions

T. D.-S., F. B, D. B., M. G. and EK designed the project. T.D.-S. implemented the approach, with the support of G. B. T. D.-S. performed the experiments and analysed the data. Writing – original draft preparation by T. D.-S., and E. K. Writing – review and editing by T. D.-S, G. B., N. G., M. G., and E. K. All authors have given approval to the final version of the manuscript.

## Acknowledgements

This research was supported by Centre National de la Recherche Scientifique (CNRS), and Université de Strasbourg. This work of the Interdisciplinary Thematic Institute IMS, the drug discovery and development institute, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, was supported by IdEx Unistra (ANR-10-IDEX-0002), and by SFRI-STRAT'US project (ANR-20-SFRI-0012) under the framework of the French Investments for the Future Program.

## Conflict of Interests

The authors declare no conflict of interest.

## Data Availability Statement

Bioinfo-DB and ChEMBL-clinical are accessible via an online repository (https://seafile.unistra.fr/d/f4b0c4105dfe411bb4b5/). The code to collect R-groups and to generate new molecules is available on https://github.com/LIT-CCM-lab/Scaffold-.

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Manuscript received: May 19, 2024

Revised manuscript received: June 18, 2024

Accepted manuscript online: June 21, 2024

Version of record online: August 5, 2024