ultra-large chemical space Archives - Pharmacelera | Pushing the limits of computational chemistry

New JCIM paper about ultralarge chemical space exploration

Enric Herrero — Wed, 11 Jun 2025 12:37:30 +0000

Enric Herrero

Chief Technology Officer

In our latest paper published in the Journal of Chemical Information and Modeling (JCIM) by the American Chemical Society, we introduce two innovative methods for exploring ultra-large chemical libraries using accurate 3D quantum mechanics-based descriptors.

The rapid expansion of ultra-large chemical libraries has revolutionized drug discovery, providing access to billions of compounds. However, this growth poses relevant challenges for traditional virtual screening (VS) methods. To address these limitations, synthon-based approaches have emerged as scalable alternatives, exploiting combinatorial chemistry principles to prioritize building blocks over enumerated molecules.

With current library sizes, synthon-based approaches have 100k times lower computational cost than brute force methods. Since chemical spaces are growing rapidly and synthon-based methods scale better, this gap will continuously grow.

In this work, we present exaScreen and exaDock, two novel synthon-based methodologies designed for ligand-based and structure-based VS, respectively.

exaScreen: Our 3D ligand-based tool for virtual screening exploits atomic descriptors to select the optimal synthons through similarity measurements of the 3D molecular fields generated by the synthons and the reference fragment. For the set of reference compounds considered, the overall accuracy of exaScreen compares with the results obtained with brute force, as noted in the similar recovery of actives and the significant degree of identity between the actives selected by the two methods but at a significantly lower computational cost. exaScreen explores 70 billion compounds in less than 7 hours in a single workstation and requires less than 6 GB of disk storage.

exaDock: Our new structure-based method for ultra-large libraries exploits a restrained docking to explore hybrid compounds (fragment reference + synthon) recurrently for the distinct reference fragments, which ultimately leads to the selection of the optimal synthon-based compounds built from the best ranked synthons identified for the reference fragments. The exaDock methodology can perform a search of EnamineREAL with a 50K lower computational cost than brute force.

We evaluated both approaches across 11 diverse biological targets, including kinases and GPCRs. We show that exaScreen and exaDock achieve recovery rates comparable to brute-force screening strategies, offering computationally efficient strategies for VS in ultra-large chemical spaces using accurate 3D descriptors.

One particularly interesting observation is the difference in scaffold overlap between our proposed methods and brute-force methods. In ligand-based, there is a significant overlap among the recovered active scaffolds while, in structure-based, the overlap is lower. This difference is explained by the influence of the geometrical and physicochemical properties of the binding site, particularly to the compactness of the pocket and to the solvent exposure of the reference fragments.

Join our webinar

If you are interested in knowing more, register to our joint webinar with Enamine on June 19th at 4PM CET, 10AM ET!

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Pharmacelera extends the current partnership with Enamine for the screening of ultra-large chemical libraries

Fernando Martín — Tue, 07 May 2024 08:06:19 +0000

Barcelona, Spain, and Kyiv, Ukraine, 07 May 2024. Pharmacelera, the leading provider of computational tools for hit discovery, and Enamine, the developer of the world’s largest and most reputable virtual space: REAL, have announced the extension of their current partnership to explore an extraordinary magnitude of compounds, that has been extended by a 10 fold factor – when compared to the early version -. Ultra-large chemical libraries constitute a key paradigm to tap into new and still unexplored chemical spaces, increasing the probability for the researcher to find new and chemically diverse potent hits for Discovery Programs. Efficient handling of the ultra-large compound libraries still remains the main challenge.

In 2022, Pharmacelera and Enamine started their collaboration with the plug-in of Enamine Real Database – over 165 well-validated parallel synthesis protocols applied to over 138,000 qualified reagents and building blocks at this stage- to the new version of Pharmacelera’s virtual screening flagship tool. The resulting software product was named exaScreen® . exaScreen® is harnessing the power of Artificial Intelligence (AI) and Quantum-Mechanics (QM) algorithms. The success of this initial phase prompted both partners to create an efficient approach to give their customers an access to more REAL Compounds to find new diverse starting points for drug discovery by allowing the screening of Enamine’s REAL Space consisting today of 48 billion compounds. The resulting hits can be synthesized by Enamine within only a 3-4 weeks period with an 80% success rate. More analogues for hit follow-up activities are accessible for Pharmacelera’s customers,with Enamine offering an access to several trillions of REAL Compounds, and an even of make-on-demand (“MADE”) Building Blocks.

As part of this extended partnership, Enamine will receive a license to use exaScreen® for their internal library research work.

“Screening of ultra-large virtual chemical libraries has shown to be a powerful approach that can give a quick access to potent IP-free hits for a wide variety of targets. We are really delighted to count on Pharmacelera among our armada of talented deep -tech partners, and to extend our current collaboration with Pharmacelera to use their exaScreen® technology to extract the needles from our exciting expandable 3D space environment, but also being glad to use it in-house for our own screening library development work.», said Michael Bossert, Head of Strategic Alliances at Enamine.

“This agreement is fully aligned with Pharmacelera’s strategy to work with leading institutions in the field of Drug Discovery that have complementary technology and expertise”, says Rémy Hoffmann, Chief Business Development Officer at Pharmacelera. “We are thrilled to expand the current collaboration started on December 2022 with Enamine, the prominent compound provider, to apply our accurate Quantum-Mechanics (QM) and Machine Learning (ML) algorithms to mine the Enamine’s REAL Space and deliver synthesizable compounds that can be further evaluated in biological assays”, said Enric Gibert, Pharmacelera’s CEO.

About Enamine

Enamine is a scientifically driven integrated discovery Contract Research Organisation with unique partnering opportunities in exploring new chemical space. The company combines access to the inhouse produced screening compounds (4.2M in stock) and building blocks (300K in stock) with a comprehensive platform of integrated discovery services to advance and accelerate the efforts in Drug Discovery. For more information visit: https://enamine.net

About Enamine REAL Space

Enamine REAL Space contains 48 billion make-on-demand molecules that can be synthesized at Enamine extremely fast (3-4 weeks), with high feasibility (over 80%), and inexpensive. The REAL compounds are created by parallel chemistry through the compilation of 424,490 building blocks via more than 164 well-validated parallel synthesis protocols, underlying Enamine’s approach to design make-on-demand compounds to maximize synthesis success rate.

About Pharmacelera

Pharmacelera develops advanced computational tools for the discovery of novel hits using accurate Quantum-Mechanics (QM), Artificial Intelligence (AI), and High-Performance Computing (HPC). The company’s products PharmScreen®, exaScreen® and PharmQSAR® use 3D molecular descriptors derived from Quantum-Mechanics (QM) calculations to mine an unexplored chemical space and to identify diverse hits uncovered by traditional algorithms. Pharmacelera is a private company founded in 2015 and based in Barcelona, Spain. The company works with several big pharma and biotech organizations across Europe and the United States.

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How can we screen 31 billion compounds? Divide and conquer!

Fernando Martín — Tue, 30 May 2023 13:28:37 +0000

Enric Herrero

May 30th, 2023

Commercial chemical libraries have witnessed remarkable growth in recent years, resulting in an unprecedented increase in size and diversity. With advancements in high-throughput synthesis and combinatorial chemistry techniques, compound providers like Enamine have expanded their collections of small organic molecules to meet the escalating demands of the pharmaceutical industry. This exponential growth has provided researchers worldwide with access to an extraordinary wealth of chemical diversity, facilitating the discovery and development of novel therapeutic agents.

However, the significant growth in size and diversity of commercial chemical libraries has rendered previous methods of virtual screening impractical, especially for accurate 3D methods. The sheer volume of compounds amassed within these libraries presents immense challenges in terms of storage and computational costs. Taking as a reference a compressed SD file containing multiple stereoisomers and conformers of a molecule of 48KB, the fully enumerated library of Enamine REAL (31 billion compounds) would require a storage capacity of 1.36 PB of data! This is almost 1400 hard drives like the one that you have in your laptop! Following the same example, if we assume that processing this single molecule requires 3.6 ms in your laptop, this will mean 3.5 years of calculations to perform a screening!

Luckily, several methods have been proposed that rely on the way these huge libraries are created, which is combining a set of building blocks to generate new molecules. Figure 1 shows an example of the value of using building blocks to perform a screening, for simplicity we will assume that each building block is a synthon and that all of them can be combined. In this example we have a building block library of 3 building blocks that can generate a chemical space of 9 molecules (combining all against all). If we apply the traditional approach (Brute force) we would compare our reference structure against each of the molecules of the library, this is 9 comparisons. However, if we perform a smart enumeration taking advantage of how this library has been created, we can reduce the computing cost. In this case what we would do is to partition the reference structure in two fragments and instead of comparing against all the enumerated library we perform the comparison against the building block library, this is 3 comparisons. Since we have two reference fragments we need to perform this operation twice, resulting in 6 comparisons, 3 less than in the brute force approach. This difference in the number of comparisons does not seem large but if we translate this example to a building block library of 100K building blocks, in the brute force approach we would need 10 million comparisons vs 200 thousand for the smart enumeration approach.

If we plot the computational cost projections of both methods for different library sizes (Figure 2) we can see how the scalability of the smart enumeration approach is much better than the brute force approach and, therefore, is much more suitable for the chemical libraries of the future.

Overall, we have seen that the rapid growth of commercial chemical libraries represents a challenge for virtual screening tools. The size and diversity of these libraries have made traditional screening methods impractical due to storage and computational costs. However, the use of smart enumeration based on building blocks offers a more efficient approach. By leveraging the way these libraries are created, researchers can significantly reduce the number of comparisons needed for screening. This smart enumeration approach shows better scalability and is considered more suitable for future chemical libraries, offering computational efficiency compared to brute force methods.

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General Inception and Pharmacelera Partner to Advance Drug Discovery with Exponential Screening Capabilities

Fernando Martín — Tue, 09 May 2023 14:27:06 +0000

3D quantum-mechanics, molecular descriptors, and artificial intelligence provide unique capabilities in the chemical space to identify and qualify novel hits

PALO ALTO, Calif and BARCELONA, Spain: General Inception (GI), a global company Igniter, and Pharmacelera, a Barcelona-based deep-tech company, announced its partnership to use exaScreen® for novel drug discovery. The platform technology, developed by Pharmacelera, enables exponential screening capabilities of in-silico compound libraries, increasing the exploration of chemical space to identify hits. Traditional technologies can only mine about 10 million diverse molecules versus exaScreen®’s over 30 billion. The collaboration will focus on the application of exaScreen®’s state-of-the-art virtual screening technology for General Inception’s therapeutics companies looking for novel, diverse and synthesizable hits. exaScreen® uses accurate 3D Quantum-Mechanics (QM) molecular descriptors and Artificial Intelligence (AI) to mine efficiently a humongous chemical space and potentially boost the Intellectual Property (IP) of drug discovery projects.

“This is a step forward in our aim to retrieve and discover new chemical matter, which is a fundamental pillar in early drug discovery,” said Dr. Venkat Reddy, Chief Scientific Officer of General Inception. “Pharmacelera has already demonstrated that their proprietary technology is able to identify novel yet feasible scaffolds that are totally missed by traditional screening methodologies. We look forward to applying this technology and believe it will provide a meaning benefit to the drug discovery efforts of our companies.”

“We are very excited to establish this collaboration with General Inception, a fast-growing company Igniter in the United States and Europe,” said Dr. Enric Gibert, Pharmacelera’s CEO. “We appreciate getting in on the ground-floor of collaborations to best leverage the power of exaScreen®. GI’s business model enables us to reach a diverse group of innovative companies at the start of their journey and collaborate with pharma executives to resolve key challenges in drug discovery.”

ABOUT GENERAL INCEPTION

General Inception is pioneering company creation as an Igniter company. General Inception partners with extraordinary scientific founders at the inception of their journey to efficiently translate their groundbreaking innovations into transformational companies that address humanity’s grand challenges. As a business co-founder, GI brings together domain and functional expertise, executive talent, infrastructure and development resources, and capital to ignite, nurture and scale the company journey.

For more information, please visit www.generalinception.com

ABOUT PHARMACELERA

Pharmacelera develops advanced computational tools for the discovery of novel hits using accurate Quantum-Mechanics (QM), Artificial Intelligence (AI) and High-Performance Computing (HPC). The company’s products use 3D molecular descriptors to mine an unexplored chemical space and to identify hits uncovered by traditional algorithms. Pharmacelera is a private company founded in 2015 and based in Barcelona, Spain. The company works with several big pharma and biotech organizations across Europe and the United States.

For more information, please visit www.pharmacelera.com and www.pharmacelera.com/exascreen/

CONTACTS:

General Inception

Rebecca Galler – rebecca.galler@generalinception.com

Pharmacelera

Rémy Hoffmann – remy.hoffmann@pharmacelera.com

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Exploring an ultra-large chemical space

Fernando Martín — Wed, 03 May 2023 12:37:51 +0000

Fernando Martin

May 4th, 2023

An exponential growth of the accessible chemical space

In the last years, there has been an exponential growth in commercial chemical libraries from millions to billions. To put some numbers: ZINC database has increased its size 37.000-fold since its 2012 version and Enamine REAL size is now over the 36 billion of compounds. Traditional computational chemistry approaches might not be usable anymore with these libraries due to computational and timing costs.

Enric Herrero, CTO at Pharmacelera, had the chance to talk about this topic at the Boston Area Group for Informatics and Modeling (BAGIM) last march. The presentation pointed out the need for novel tools that help exploring this novel ultra-large available chemical space and why current methods struggle to deal with it. During the presentation, different methods were listed, focusing in alternatives for ligand-based methods: brute-force (full enumeration), sampling methods and combinatorial search. Special emphasis was applied to the two latest, since they offer an alternative to screen larger chemical spaces than brute force using fewer computing resources. In this post we will quickly introduce approaches: MolPAL, based on sampling methods; and combinatorial search using 3D hydrophobic descriptors.

Sampling virtual screening powered by AI: MolPAL

This sampling method, based on David E. Graff paper, establishes that only a small subset (around 2.4%) of a full library must be evaluated with a slow screening method (i.e. docking or 3D ligand-based similarity) to obtain the same results than a brute-force method.

This is done in an iterative way, sampling first a random subset (~0.4%), evaluating the score with the slow method and then using the results to train a machine learning (ML) model. This model would be later applied to predict the score of all the ligands in the full library and select a new subset to be evaluated with the slow method.

To evaluate the capabilities of the method, our team has applied PharmScreen as scoring method to train a machine learning model. One of the main advantages observed when comparing MolPAL with brute-force screening (here, run PharmScreen for a full library), is the reduction in terms library storage (1B of compounds will suppose 44TB with brute force while MolPAL method will require only 96MB) as well as the computing speed.

An important aspect of sampling methods is that their performance will be linked to the speed ratio of the method used to mine the library vs the ML model training and prediction: the slowest or more computationally intensive the method is with respect to the ML training and prediction, the better in terms of speed gain.

Combinatorial search

Combinatorial search methods take advantage of the combinatorial chemistry concept, where the chemical space is explored using building block libraries and reaction information. By partitioning a reference molecule in fragments, screening software tools based on this method can explore libraries to find similar build blocks and enumerate only those compounds that are more similar. Since the reactivity of these building blocks is considered, one can easily reconstruct novel and synthesizable compounds that can be easily tested in the laboratory.

These methods can also take advantage of 3D information and the derived physicochemical properties when assessing the building block similarity. We have observed how the application of 3D hydrophobic molecular descriptors can help finding more diverse compounds with similar physicochemical properties than 2D methods.

Combinatorial search methods provide the best scalability among all the evaluated methods and, therefore, are a good alternative for the screening of multi-billion sized libraries. To put an example, using 3D methods as mentioned above and a library of 137K building blocks, we can explore a potential space of 31B of synthesizable molecules.

Conclusions

The exponential growth of the accessible chemical space is driving the generation of new methods that will help screening it. Contrary to full enumeration methods, that explore large libraries in exchange of using more computational resources, these new approaches can explore huger chemical libraries with more feasible hardware configurations.

Sampling methods, such as MolPAL, represent a good choice when screening large libraries using computing-demanding methods, such as docking or 3D ligand-based similarity. These methods are also interesting when storage capabilities suppose a problem.

Similarly, combinatorial search methods are a smart solution when screening ultra-large libraries, such as Enamine REAL. The use of building block libraries while considering their reactivity maximizes the synthesizability of novel compounds.

Pharmacelera is focused on offering novel solutions to explore this ultra-large chemical space. If you want to learn more about it, contact our team. They will inform you about our new services in this field and new technologies to come.

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