Magnet for the Needle in Haystack: "Crystal Structure First" Fragment Hits Unlock Active Chemical Matter Using Targeted Exploration of Vast Chemical Spaces

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.

Novel Fragment Inhibitors of PYCR1 from Docking-Guided X-ray Crystallography

The proline biosynthetic enzyme Δ1-pyrroline-5-carboxylate (P5C) reductase 1 (PYCR1) is one of the most consistently upregulated enzymes across multiple cancer types and central to the metabolic rewiring of cancer cells. Herein, we describe a fragment-based, structure-first approach to the discovery of PYCR1 inhibitors. Thirty-seven fragment-like carboxylic acids in the molecular weight range of 143-289 Da were selected from docking and then screened using X-ray crystallography as the primary assay. Strong electron density was observed for eight compounds, corresponding to a crystallographic hit rate of 22%. The fragments are novel compared to existing proline analog inhibitors in that they block both the P5C substrate pocket and the NAD(P)H binding site. Four hits showed inhibition of PYCR1 in kinetic assays, and one has lower apparent IC50 than the current best proline analog inhibitor. These results show proof-of-concept for our inhibitor discovery approach and provide a basis for fragment-to-lead optimization.

Fragment-to-Lead Medicinal Chemistry Publications in 2022

This Perspective is the eighth in an annual series that summarizes successful fragment-to-lead (F2L) case studies published each year. A tabulated summary of relevant articles published in 2022 is provided, and features such as target class, screening methods, and ligand efficiency are discussed both for the 2022 examples and for the combined examples over the years 2015-2022. In addition, trends and new developments in the field are summarized. In 2022, 18 publications described successful fragment-to-lead studies, including the development of three clinical compounds (MTRX1719, MK-8189, and BI-823911).

"You've got the Body I've got the Brains" - Could the current AI-based tools replace the human ingenuity for designing new drug candidates?

The emergence of artificial intelligence (AI) tools has transformed the landscape of drug discovery, providing unprecedented speed, efficiency, and cost-effectiveness in the search for new therapeutics. From target identification to drug formulation and delivery, AI-driven algorithms have revolutionized various aspects of medicinal chemistry, significantly accelerating the drug design process. Despite the transformative power of AI, this perspective article emphasizes the limitations of AI tools in drug discovery, requiring inventive skills of medicinal chemists. However, the article highlighted that there is a need for a harmonious integration of AI-based tools and human expertise in drug discovery. Such a synergistic approach promises to lead to groundbreaking therapies that address unmet medical needs and benefit humankind. As the world evolves technologically, the question remains: When will AI tools effectively design and develop drugs? The answer may lie in the seamless collaboration between AI and human researchers, unlocking transformative therapies that combat diseases effectively.

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search

Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment-protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC₅₀ values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search.

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library

Fragment-based drug discovery has played an important role in medicinal chemistry and pharmaceutical research. Despite numerous demonstrated successes, the limited diversity and overrepresentation of planar, sp²-rich structures in commercial libraries often hamper the full potential of this approach. Hence, the thorough design of screening libraries inevitably determines the probability for meaningful hits and subsequent structural elaboration. Against this background, we present the generation of an exclusive fragment library based on iterative entry nomination by a specifically designed computational workflow: "Fragtory". Following a pharmacophore diversity-driven approach, we used Fragtory in an interdisciplinary academic setting to guide both tailored synthesis efforts and the implementation of in-house compounds to build a curated 288-member library of sp³-enriched fragments. Subsequent NMR screens against a model protein and hit validation by protein crystallography led to the identification of structurally novel ligands that were further characterized by isothermal titration calorimetry, demonstrating the applicability of our experimental approach.

Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery

Within the past two decades, virtual combinatorial compound collections, so-called chemical spaces, became an important molecule source for pharmaceutical research all over the world. The emergence of compound vendor chemical spaces with rapidly growing numbers of molecules raises questions about their application suitability and the quality of the content. Here, we examine the composition of the recently published and, so far, biggest chemical space, "eXplore", which comprises approximately 2.8 trillion virtual product molecules. The utility of eXplore to retrieve interesting chemistry around approved drugs and common Bemis Murcko scaffolds has been assessed with several methods (FTrees, SpaceLight, SpaceMACS). Further, the overlap between several vendor chemical spaces and a physicochemical property distribution analysis has been performed. Despite the straightforward chemical reactions underlying its setup, eXplore is demonstrated to provide relevant and, most importantly, easily accessible molecules for drug discovery campaigns.

Navigating large chemical spaces in early-phase drug discovery

The size of actionable chemical spaces is surging, owing to a variety of novel techniques, both computational and experimental. As a consequence, novel molecular matter is now at our fingertips that cannot and should not be neglected in early-phase drug discovery. Huge, combinatorial, make-on-demand chemical spaces with high probability of synthetic success rise exponentially in content, generative machine learning models go hand in hand with synthesis prediction, and DNA-encoded libraries offer new ways of hit structure discovery. These technologies enable to search for new chemical matter in a much broader and deeper manner with less effort and fewer financial resources. These transformational developments require new cheminformatics approaches to make huge chemical spaces searchable and analyzable with low resources, and with as little energy consumption as possible. Substantial progress has been made in the past years with respect to computation as well as organic synthesis. First examples of bioactive compounds resulting from the successful use of these novel technologies demonstrate their power to contribute to tomorrow's drug discovery programs. This article gives a compact overview of the state-of-the-art.

Chemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6

Targeting RNA methyltransferases with small molecules as inhibitors or tool compounds is an emerging field of interest in epitranscriptomics and medicinal chemistry. For two challenging RNA methyltransferases that introduce the 5-methylcytosine (m⁵C) modification in different tRNAs, namely DNMT2 and NSUN6, an ultra-large commercially available chemical space was virtually screened by physicochemical property filtering, molecular docking, and clustering to identify new ligands for those enzymes. Novel chemotypes binding to DNMT2 and NSUN6 with affinities down to K_D,app = 37 µM and K_D,app = 12 µM, respectively, were identified using a microscale thermophoresis (MST) binding assay. These compounds represent the first molecules with a distinct structure from the cofactor SAM and have the potential to be developed into activity-based probes for these enzymes. Additionally, the challenges and strategies of chemical space docking screens with special emphasis on library focusing and diversification are discussed.

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

Crystal polymorphism in fragment-based lead discovery of ligands of the catalytic domain of UGGT, the glycoprotein folding quality control checkpoint

None of the current data processing pipelines for X-ray crystallography fragment-based lead discovery (FBLD) consults all the information available when deciding on the lattice and symmetry (i.e., the polymorph) of each soaked crystal. Often, X-ray crystallography FBLD pipelines either choose the polymorph based on cell volume and point-group symmetry of the X-ray diffraction data or leave polymorph attribution to manual intervention on the part of the user. Thus, when the FBLD crystals belong to more than one crystal polymorph, the discovery pipeline can be plagued by space group ambiguity, especially if the polymorphs at hand are variations of the same lattice and, therefore, difficult to tell apart from their morphology and/or their apparent crystal lattices and point groups. In the course of a fragment-based lead discovery effort aimed at finding ligands of the catalytic domain of UDP-glucose glycoprotein glucosyltransferase (UGGT), we encountered a mixture of trigonal crystals and pseudotrigonal triclinic crystals-with the two lattices closely related. In order to resolve that polymorphism ambiguity, we have written and described here a series of Unix shell scripts called CoALLA (crystal polymorph and ligand likelihood-based assignment). The CoALLA scripts are written in Unix shell and use autoPROC for data processing, CCP4-Dimple/REFMAC5 and BUSTER for refinement, and RHOFIT for ligand docking. The choice of the polymorph is effected by carrying out (in each of the known polymorphs) the tasks of diffraction data indexing, integration, scaling, and structural refinement. The most likely polymorph is then chosen as the one with the best structure refinement R_free statistic. The CoALLA scripts further implement a likelihood-based ligand assignment strategy, starting with macromolecular refinement and automated water addition, followed by removal of the water molecules that appear to be fitting ligand density, and a final round of refinement after random perturbation of the refined macromolecular model, in order to obtain unbiased difference density maps for automated ligand placement. We illustrate the use of CoALLA to discriminate between H3 and P1 crystals used for an FBLD effort to find fragments binding to the catalytic domain of Chaetomium thermophilum UGGT.