Fragment-Based Drug Discovery

Ligandability at the membrane interface of GPx4 revealed through a reverse micelle fragment screening platform

While they account for a large portion of drug targets, membrane proteins (MPs) present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of proteins that bind reversibly to membranes, are also difficult targets, particularly those that function only while bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, interfaces are underexplored spaces in inhibitor design and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability in the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase which is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of non-covalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 in bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from > 200 μM to ~15 μM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing non-covalent inhibitors of GPx4.

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

Synthesis and Chemoinformatic Analysis of Fluorinated Piperidines as 3D Fragments for Fragment-Based Drug Discovery

The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).

Molecular glue degraders: exciting opportunities for novel drug discovery

INTRODUCTION

Molecular Glue Degraders (MGDs) is a concept that refers to a class of compounds that facilitate the interaction between two proteins or molecules within a cell. These compounds act as bridge that enhances specific Protein-Protein Interactions (PPIs). Over the past decade, this technology has gained attention as a potential strategy to target proteins that were traditionally considered undruggable using small molecules.

AREAS COVERED

This review presents the concept of cellular homeostasis and the balance between protein synthesis and protein degradation. The concept of protein degradation is concerned with molecular glues, which form part of the broader field of Targeted Protein Degradation (TPD). Next, pharmacochemical strategies for the rational design of MGDs are detailed and illustrated by examples of Ligand-Based (LBDD), Structure-Based (SBDD) and Fragment-Based Drug Design (FBDD).

EXPERT OPINION

Expanding the scope of what can be effectively targeted in the development of treatments for diseases that are incurable or resistant to conventional therapies offers new therapeutic options. The treatment of microbial infections and neurodegenerative diseases is a major societal challenge, and the discovery of MGDs appears to be a promising avenue. Combining different approaches to discover and exploit a variety of innovative therapeutic agents will create opportunities to treat diseases that are still incurable.

Discovery of Alternative Binding Poses through Fragment-Based Identification of DHODH Inhibitors

Dihydroorotate dehydrogenase (DHODH) is a mitochondrial enzyme that affects many aspects essential to cell proliferation and survival. Recently, DHODH has been identified as a potential target for acute myeloid leukemia therapy. Herein, we describe the identification of potent DHODH inhibitors through a scaffold hopping approach emanating from a fragment screen followed by structure-based drug design to further improve the overall profile and reveal an unexpected novel binding mode. Additionally, these compounds had low P-gp efflux ratios, allowing for applications where exposure to the brain would be required.

A Novel NMR-Based Protocol to Screen Ultralow Molecular Weight Fragments

Fragment-based lead discovery has emerged as one of the most efficient screening strategies for finding hit molecules in drug discovery. Recently, a novel strategy based on a class of fragments characterized by an ultralow molecular weight (ULMW) has been proposed. These fragments bind to the target with a very low affinity, requiring reliable biophysical methods for detection. The most notable application of ULMW used a set of 81 fragments, named MiniFrags, and screened them by X-ray crystallography. We extended the utilization of this novel class of fragments to another gold standard technique for fragment-based screening: nuclear magnetic resonance (NMR). Here, we present a novel NMR protocol to detect and analyze such weak interactions in a challenging real-world scenario: a flexible target with a flat, water-exposed binding site. We identified a subset of 69 highly water-soluble MiniFrags that were screened against the antiapoptotic protein human Bfl-1.

Molecular fragmentation as a crucial step in the AI-based drug development pathway

The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

Fragment Merging, Growing, and Linking Identify New Trypanothione Reductase Inhibitors for Leishmaniasis

Trypanothione reductase (TR) is a suitable target for drug discovery approaches against leishmaniasis, although the identification of potent inhibitors is still challenging. Herein, we harnessed a fragment-based drug discovery (FBDD) strategy to develop new TR inhibitors. Previous crystallographic screening identified fragments 1-3, which provided ideal starting points for a medicinal chemistry campaign. In silico investigations revealed critical hotspots in the TR binding site, guiding our structure- and ligand-based structure-actvity relationship (SAR) exploration that yielded fragment-derived compounds 4-14. A trend of improvement in Leishmania infantum TR inhibition was detected along the optimization and confirmed by the crystal structures of 9, 10, and 14 in complex with Trypanosoma brucei TR. Compound 10 showed the best TR inhibitory profile (Ki = 0.2 μM), whereas 9 was the best one in terms of in vitro and ex vivo activity. Although further fine-tuning is needed to improve selectivity, we demonstrated the potentiality of FBDD on a classic but difficult target for leishmaniasis.

Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulation for the Discovery of Novel SARS-CoV-2 NSP3 Mac1 Domain Inhibitors

Since the emergence of SARS-CoV-2, many genetic variations within its genome have been identified, but only a few mutations have been found in nonstructural proteins (NSPs). Among this class of viral proteins, NSP3 is a multidomain protein with 16 different domains, and its largest domain is known as the macrodomain or Mac1 domain. In this study, we present a virtual screening campaign in which we computationally evaluated the NCI anticancer library against the NSP3 Mac1 domain, using Molegro Virtual Docker. The top hits with the best MolDock and Re-Rank scores were selected. The physicochemical analysis and drug-like potential of the top hits were analyzed using the SwissADME data server. The binding stability and affinity of the top NSC compounds against the NSP3 Mac1 domain were analyzed using molecular dynamics (MD) simulation, using Desmond software, and their interaction energies were analyzed using the MM/GBSA method. In particular, by applying subsequent computational filters, we identified 10 compounds as possible NSP3 Mac1 domain inhibitors. Among them, after the assessment of binding energies (ΔGbind) on the whole MD trajectories, we identified the four most interesting compounds that acted as strong binders of the NSP3 Mac1 domain (NSC-358078, NSC-287067, NSC-123472, and NSC-142843), and, remarkably, it could be further characterized for developing innovative antivirals against SARS-CoV-2.

Pyrrole-thiazolidinone hybrids as a new structural class of broad-spectrum anti-infectives

A series of pyrrole-thiazolidinone hybrids was designed, synthesized and evaluated for activities against ESKAP bacteria panel and mycobacterial pathogens. From the series, compound 9d showed prominent activity against S. aureus (MIC = 0.5 μg/mL) and compound 9k showed the most promising activity against M. tuberculosis H37Rv (MIC = 0.5 μg/mL). Potent derivatives were found to be non-toxic when tested against Vero cells. Compound 9d upon evaluation in vitro against several MRSA and VRSA strains produced activity comparable or better than standard drugs. In the anti-biofilm assay, 9d reduced S. aureus biofilm by >11% at 10x MIC. The dual inhibitory effect exhibited by pyrrole-thiazolidinone hybrids confirms their potential as new class of promising anti-infective agents.

An electrophilic fragment screening for the development of small molecules targeting caspase-2

Recent Alzheimer's research has shown increasing interest in the caspase-2 (Casp2) enzyme. However, the available Casp2 inhibitors, which have been pentapeptides or peptidomimetics, face challenges for use as CNS drugs. In this study, we successfully screened a 1920-compound chloroacetamide-based, electrophilic fragment library from Enamine. Our two-point dose screen identified 64 Casp2 hits, which were further evaluated in a ten-point dose-response study to assess selectivity over Casp3. We discovered compounds with inhibition values in the single-digit micromolar and sub-micromolar range, as well as up to 32-fold selectivity for Casp2 over Casp3. Target engagement analysis confirmed the covalent-irreversible binding of the selected fragments to Cys320 at the active site of Casp2. Overall, our findings lay a strong foundation for the future development of small-molecule Casp2 inhibitors.

Expanding Bioactive Fragment Space with the Generated Database GDB-13s

Identifying innovative fragments for drug design can help medicinal chemistry address new targets and overcome the limitations of the classical molecular series. By deconstructing molecules into ring fragments (RFs, consisting of ring atoms plus ring-adjacent atoms) and acyclic fragments (AFs, consisting of only acyclic atoms), we find that public databases of molecules (i.e., ZINC and PubChem) and natural products (i.e., COCONUT) contain mostly RFs and AFs of up to 13 atoms. We also find that many RFs and AFs are enriched in bioactive vs inactive compounds from ChEMBL. We then analyze the generated database GDB-13s, which enumerates 99 million possible molecules of up to 13 atoms, for RFs and AFs resembling ChEMBL bioactive RFs and AFs. This analysis reveals a large number of novel RFs and AFs that are structurally simple, have favorable synthetic accessibility scores, and represent opportunities for synthetic chemistry to contribute to drug innovation in the context of fragment-based drug discovery.

Novel hybrid motifs of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazoles: Synthesis, crystal structure, anticancer evaluations, and molecular docking study

4-((4-(1-benzyl-2-methyl-4-nitro-1H-imidazole-5-yl)piperazine-1-yl)methyl)-1-substituted-1H-1,2,3-triazole motifs are designed and synthesized via click chemistry. The reaction of 1-(N1-benzyl- 2-methyl-4-nitro-1H-imidazole- 5-yl)-4-(prop-2-yn-1-yl) piperazine 5 as new scaffold with diverse primary azides to selectively produce 1,4-disubstituted-1,2,3-triazoles 9a-k, 10a-c and 11a-q. Physicochemical methods: when 1H NMR, 13C NMR, and HRMS are utilized to fully characterize all synthesized compounds. X-ray structural determination and analysis for compound 9a is also performed. The newly designed chromophores are assessed for their anti-proliferative potency against three selected human cancer cell lines (MCF-7, HepG2, and PC3), and one normal cell line (Dermal/Fibroblast). Compounds 9g and 9k have shown potent activities against the MCF-7 cell line with IC50 values of (2.00 ± 0.03 μM) and (5.00 ± 0.01 μM) respectively. ADMET studies and Molecular docking investigations are performed on the most active hybrid nitroimidazole derivatives 9g and 9k with 4-hydroxytamoxifen (4-OHT) at the human estrogen receptor alpha (hER) during binding active sites to study the ligand-protein interactions and free binding energies at atomic levels. The triazole ring in the 9g derivative forms a hydrogen bond with Asp58 with distance 3.2 Å. And it is found that polar contact with His231 amino acid residue. In silico assessment of the compounds showed very good pharmacokinetic properties based on their physicochemical values, also the ADMET criteria of the most active hybrid systems are within the acceptable range.

Fragment expansion with NUDELs - poised DNA-encoded libraries

Optimisation of the affinity of lead compounds is a critical challenge in the identification of drug candidates and chemical probes and is a process that takes many years. Fragment-based drug discovery has become established as one of the methods of choice for drug discovery starting with small, low affinity compounds. Due to their low affinity, the evolution of fragments to desirable levels of affinity is often a key challenge. The accepted best method for increasing the potency of fragments is by iterative fragment growing, which can be very time consuming and complex. Here, we introduce a paradigm for fragment hit optimisation using poised DNA-encoded chemical libraries (DELs). The synthesis of a poised DEL, a partially constructed library that retains a reactive handle, allows the coupling of any active fragment for a specific target protein, allowing rapid discovery of potent ligands. This is illustrated for bromodomain-containing protein 4 (BRD4), in which a weakly binding fragment was coupled to a 42-member poised DEL via Suzuki-Miyaura cross coupling resulting in the identification of an inhibitor with 51 nM affinity in a single step, representing an increase in potency of several orders of magnitude from an original fragment. The potency of the compound was shown to arise from the synergistic combination of substructures, which would have been very difficult to discover by any other method and was rationalised by X-ray crystallography. The compound showed attractive lead-like properties suitable for further optimisation and demonstrated BRD4-dependent cellular pharmacology. This work demonstrates the power of poised DELs to rapidly optimise fragments, representing an attractive generic approach to drug discovery.

Fragment-based screening by protein-detected NMR spectroscopy

Fragment-based drug discovery (FBDD) identifies low molecular weight compounds that can be developed into ligands with high affinity and selectivity for therapeutic targets. Screening fragment libraries (<10,000 molecules) with biophysical techniques against macromolecules provides information about novel chemical spaces that bind the macromolecule and scaffolds that can be modified to increase potency. A fragment-screening pipeline requires a standardized protocol for target selection, library assembly and maintenance, library screening, and hit validation to ensure hit integrity. Herein, the fundamental aspects of a fragment screening pipeline-focusing on protein-detected NMR data collection and analysis-are discussed in detail for researchers to use as a resource in their FBDD projects. Selected screening targets must undergo rigorous stability and buffer testing by NMR spectroscopy to ensure the protein structure is stable for the entire screen. Biophysical instrumentation that rapidly measures protein thermostability is helpful in buffer screening. Molecules in fragment libraries are analyzed computationally and physically, stored at appropriate temperatures, and multiplexed in well plates for library conservation. The screening protocol is streamlined using liquid handling robotics for sample preparation and customized Python scripts for protein-detected NMR data analysis. Molecules identified from the screen are titrated to determine their binding site(s) and Kd values and confirmed with an orthogonal biophysical assay. This detailed FBDD screening pipeline developed by the Program in Chemical Biology at the Medical College of Wisconsin has successfully screened many unrelated target proteins to identified novel molecules that selectively bind to these target proteins.

Design, Synthesis and Evaluation of Praziquantel Analogues and New Molecular Hybrids as Potential Antimalarial and Anti-Schistosomal Agents

Malaria and schistosomiasis are two of the neglected tropical diseases that persistently wreak havoc worldwide. Although many antimalarial drugs such as chloroquine are readily available, the emergence of drug resistance necessitates the development of new therapies to combat this disease. Conversely, Praziquantel (PZQ) remains the sole effective drug against schistosomiasis, but its extensive use raises concerns about the potential for drug resistance to develop. In this project, the concept of molecular hybridization was used as a strategy to design the synthesis of new molecular hybrids with potential antimalarial and antischistosomal activity. A total of seventeen molecular hybrids and two PZQ analogues were prepared by coupling 6-alkylpraziquanamines with cinnamic acids and cyclohexane carboxylic acid, respectively. The synthesised compounds were evaluated for their antimalarial and antischistosomal activity; while all of the above compounds were inactive against Plasmodium falciparum (IC₅₀ > 6 µM), many were active against schistosomiasis with four particular compounds exhibiting up to 100% activity against newly transformed schistosomula and adult worms at 50 µM. Compared to PZQ, the reference drug, the activity of which is 91.7% at 1 µM, one particular molecular hybrid, compound 32, which bears a para-isopropyl group on the cinnamic acid moiety, exhibited a notable activity at 10 µM (78.2% activity). This compound has emerged as the front runner candidate that might, after further optimization, hold promise as a potential lead compound in the fight against schistosomiasis.

Two-Step Validation Approach for Tools To Study the DNA Repair Enzyme SNM1A

We report a two-step validation approach to evaluate the suitability of metal-binding groups for targeting DNA damage-repair metalloenzymes using model enzyme SNM1A. A fragment-based screening approach was first used to identify metal-binding fragments suitable for targeting the enzyme. Effective fragments were then incorporated into oligonucleotides using the copper-catalysed azide-alkyne cycloaddition reaction. These modified oligonucleotides were recognised by SNM1A at >1000-fold lower concentrations than their fragment counterparts. The exonuclease SNM1A is a key enzyme involved in the repair of interstrand crosslinks, a highly cytotoxic form of DNA damage. However, SNM1A and other enzymes of this class are poorly understood, as there is a lack of tools available to facilitate their study. Our novel approach of incorporating functional fragments into oligonucleotides is broadly applicable to generating modified oligonucleotide structures with high affinity for DNA damage-repair enzymes.

Three-Dimensional Neodymium Metal-Organic Framework for Catalyzing the Cyanosilylation of Aldehyde and the Synthesis of 2,3-Dihydroquinazolin-4(1H)-one Derivatives

In this work, a novel 3D lanthanide metal-organic framework (Ln-MOF) Nd-cdip (H₄cdip = 5,5'-carbonyldiisophthalic acid) was successfully synthesized, which could be used as an efficient heterogeneous catalyst for cyanosilylation and the synthesis of 2,3-dihydroquinazolin-4(1H)-one derivatives at room temperature based on the Lewis acid sites in the channels of the MOF. Moreover, Nd-cdip had an excellent turnover number (500) for catalyzing cyanosilylation in no solvent condition. Nd-cdip could be reused in both of the above-mentioned reactions at least five times without a significant decrease in yield. The possible mechanism of cyanosilylation catalyzed by Nd-cdip was studied by using the luminescence properties of Tb-cdip, which has the same structure and functions as Nd-cdip. Furthermore, both reactions catalyzed by Nd-cdip were fitted to zero-order dynamics.

E3 Ligases Meet Their Match: Fragment-Based Approaches to Discover New E3 Ligands and to Unravel E3 Biology

Ubiquitination is a key post-translational modification of proteins, affecting the regulation of multiple cellular processes. Cells are equipped with over 600 ubiquitin orchestrators, called E3 ubiquitin ligases, responsible for directing the covalent attachment of ubiquitin to substrate proteins. Due to their regulatory role in cells, significant efforts have been made to discover ligands for E3 ligases. The recent emergence of the proteolysis targeting chimera (PROTAC) and molecular glue degrader (MGD) modalities has further increased interest in E3 ligases as drug targets. This perspective focuses on how fragment based lead discovery (FBLD) methods have been used to discover new ligands for this important target class. In some cases these efforts have led to clinical candidates; in others, they have provided tools for deepening our understanding of E3 ligase biology. Recently, FBLD-derived ligands have inspired the design of PROTACs that are able to artificially modulate protein levels in cells.

Proteome-Wide Fragment-Based Ligand and Target Discovery

Chemical probes are invaluable tools to investigate biological processes and can serve as lead molecules for the development of new therapies. However, despite their utility, only a fraction of human proteins have selective chemical probes, and more generally, our knowledge of the "chemically-tractable" proteome is limited, leaving many potential therapeutic targets unexploited. To help address these challenges, powerful chemical proteomic approaches have recently been developed to globally survey the ability of proteins to bind small molecules (i. e., ligandability) directly in native systems. In this review, we discuss the utility of such approaches, with a focus on the integration of chemoproteomic methods with fragment-based ligand discovery (FBLD), to facilitate the broad mapping of the ligandable proteome while also providing starting points for progression into lead chemical probes.

Discovery of inhibitors against SARS-CoV-2 main protease using fragment-based drug design

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease 2019 (COVID-19), in which the main protease (Mpro) plays an important role in the virus's life cycle. In this work, two representative peptide inhibitors (11a and PF-07321332) were selected, and their interaction mechanisms of non-covalently bound with Mpro were firstly investigated by means of molecular dynamical simulation. Then, using the fragment-based drug design method, some fragments from the existing SARS-CoV and SARS-CoV-2 inhibitors were selected to replace the original P2 and P3 fragments, resulting in some new molecules. Among them, two molecules (O-74 and N-98) were confirmed by molecular docking and molecular dynamics simulation, and ADMET properties prediction was employed for further verification. The results shown that they presented excellent activity and physicochemical properties, and had the potential to be new inhibitors for SARS-CoV-2 main protease.

Identification of specific carbonic anhydrase inhibitors via in situ click chemistry, phage-display and synthetic peptide libraries: comparison of the methods and structural study

The development of highly active and selective enzyme inhibitors is one of the priorities of medicinal chemistry. Typically, various high-throughput screening methods are used to find lead compounds from a large pool of synthetic compounds, and these are further elaborated and structurally refined to achieve the desired properties. In an effort to streamline this complex and laborious process, new selection strategies based on different principles have recently emerged as an alternative. Herein, we compare three such selection strategies with the aim of identifying potent and selective inhibitors of human carbonic anhydrase II. All three approaches, in situ click chemistry, phage-display libraries and synthetic peptide libraries, led to the identification of more potent inhibitors when compared to the parent compounds. In addition, one of the inhibitor-peptide conjugates identified from the phage libraries showed greater than 100-fold selectivity for the enzyme isoform used for the compound selection. In an effort to rationalize the binding properties of the conjugates, we performed detailed crystallographic and NMR structural analysis, which revealed the structural basis of the compound affinity towards the enzyme and led to the identification of a novel exosite that could be utilized in the development of isoform specific inhibitors.

Discovery of structural diverse reversible BTK inhibitors utilized to develop a novel in vivo CD69 and CD86 PK/PD mouse model

For the past two decades, BTK a tyrosine kinase and member of the Tec family has been a drug target of significant interest due to its potential to selectively treat various B cell-mediated diseases such as CLL, MCL, RA, and MS. Owning to the challenges encountered in identifying drug candidates exhibiting the potency block B cell activation via BTK inhibition, the pharmaceutical industry has relied on the use of covalent/irreversible inhibitors to address this unmet medical need. Herein, we describe a medicinal chemistry campaign to identify structurally diverse reversible BTK inhibitors originating from HITS identified using a fragment base screen. The leads were optimized to improve the potency and in vivo ADME properties resulting in a structurally distinct chemical series used to develop and validate a novel in vivo CD69 and CD86 PD assay in rodents.

Integrating Protein Interaction Surface Prediction with a Fragment-Based Drug Design: Automatic Design of New Leads with Fragments on Energy Surfaces

Protein-protein interactions (PPIs) have emerged in the past years as significant pharmacological targets in the development of new therapeutics due to their key roles in determining pathological pathways. Herein, we present fragments on energy surfaces, a simple and general design strategy that integrates the analysis of the dynamic and energetic signatures of proteins to unveil the substructures involved in PPIs, with docking, selection, and combination of drug-like fragments to generate new PPI inhibitor candidates. Specifically, structural representatives of the target protein are used as inputs for the blind physics-based prediction of potential protein interaction surfaces using the matrix of low coupling energy decomposition method. The predicted interaction surfaces are subdivided into overlapping windows that are used as templates to direct the docking and combination of fragments representative of moieties typically found in active drugs. This protocol is then applied and validated using structurally diverse, important PPI targets as test systems. We demonstrate that our approach facilitates the exploration of the molecular diversity space of potential ligands, with no requirement of prior information on the location and properties of interaction surfaces or on the structures of potential lead compounds. Importantly, the hit molecules that emerge from our ab initio design share high chemical similarity with experimentally tested active PPI inhibitors. We propose that the protocol we describe here represents a valuable means of generating initial leads against difficult targets for further development and refinement.

Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery

We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used recurrent neural network (RNN) assumes monotonically decaying correlations in strings, PixelCNN captures a periodicity among characters of SMILES. Thus, PixelCNN provides us with a novel solution for the analysis of chemical space by extracting the periodicity of molecular structures that will be buried in SMILES. Moreover, this characteristic enables us to generate molecules by combining several simple building blocks, such as a benzene ring and side-chain structures, which contributes to the effective exploration of chemical space by step-by-step searching for molecules from a target fragment. In conclusion, PixelCNN could be a powerful approach focusing on the periodicity of molecules to explore chemical space for the fragment-based molecular design.

Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking

We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.

Design of a Structurally Novel Multipotent Drug Candidate by the Scaffold Architecture Technique for ACE-II, NSP15, and Mpro Protein Inhibition: Identification and Isolation of a Natural Product to Prevent the Severity of Future Variants of Covid 19 and a Colorectal Anticancer Drug

Scaffold architecture in the sectors of biotechnology and drug discovery research include scaffold hopping and molecular modelling techniques and helps in searching for potential drug candidates containing different core structures using computer-based software, which greatly aids medicinal and pharmaceutical chemistry. Going ahead, the computational method of scaffold architecture is thought to produce new scaffolds, and the method is capable of helping search engines toward producing new scaffolds that are likely to represent potent compounds with high therapeutic applications, which is a possibility in this case as well. Here we probate a different interactive design by natural product hopping, molecular modelling, pharmacophore modelling, modification, and combination of the phytoconstituents present in different medicinal plants for developing a pharmacophore-guided good drug candidate for the variants of SARS-CoV-2 or Covid 19. In the modern era, these approaches are carried out at every level of development of scaffold queries, which are increasingly summarized from chemical structures. In this context, we report on a successfully designed drug-like candidate having a high-binding-affinity "compound SLP" by understanding the relationships between the compounds' pharmacophores, scaffold functional groups, and biological activities beyond their individual applications that abide by Lipinski's rule of five, Ghose rule, Veber rule etc. The new scaffold generated by altering the core of the known phyto-compounds holds a good predicted ADMET profile and is examined with iMODS server to check the molecular dynamics simulation with normal mode analysis (NMA). The scaffold's three-dimensional (3D) structure yields a searchable natural product koenimbine from a conformer database having good ADMET property and high availability in spice Murraya koenigii leaves. M. koenigii leaves are easily available in the market, and might ensure the immunity, good health, and well-being of people if affected with any of the variants of Covid 19. The cell viability studies of koenimbine on murine colorectal carcinoma cell line (CT-26) showed no toxicity on normal mice lymphocyte cells (MLCs). The anticancer mechanism of koenimbine was displayed by its enhanced capacity to produce intercellular reactive oxygen species (ROS) in the colorectal carcinoma cell line.

Fragment-Based Drug Discovery against Mycobacteria: The Success and Challenges

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.

Biphenyl-based small molecule inhibitors: Novel cancer immunotherapeutic agents targeting PD-1/PD-L1 interaction

The immune checkpoint proteins are those key to the body's immunity which can either boost the immune system to protect the body from pathogens; or suppress the body's immunity system for the goal of self-tolerance. Cancer cells have evolved some mechanisms to boost the immuno-inhibitory checkpoints to bypass the immune system of the body. The binding of Programmed Cell Death-1 (PD-1) protein with its ligand Programmed Cell Death Ligand-1 (PD-L1) promotes this kind of immune-inhibitory signal. The discovery of immune checkpoint inhibitors was started in the early 21st century; with some success through monoclonal antibodies, peptides, and small molecules. Being the most reliable and safest way to target immune checkpoints, the scientific community is exploring possibilities to develop small molecule inhibitors. Among the different scaffolds of the small molecule, the most exposed and researched core molecule is Biphenyl-based scaffolds. We have described all of the possible biphenyl-based small molecules in this article, as well as their interactions with various amino acids in the binding cavity. The link between the in silico, in vitro, and in vivo activities of the PD-1/PD-L1 inhibitors are well connected. The Tyr56, Met115, Ala121, and Asp122 were detected as the crucial amino acids of the PD-1/PD-L1 inhibition. Additionally, a detailed binding pocket analysis of the PD-L1 receptor was carried out, where it was observed and confirmed that the binding pocket is tunnel-shaped and hydrophobic in nature. Finally, the structure-activity relationship of the biphenyl-based small molecule inhibitors was developed based on their activity and the binding interactions.

Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

In the last 20 years, fragment-based drug discovery (FBDD) has become a popular and consolidated approach within the drug discovery pipeline, due to its ability to bring several drug candidates to clinical trials, some of them even being approved and introduced to the market. A class of targets that have proven to be particularly suitable for this method is represented by kinases, as demonstrated by the approval of BRAF inhibitor vemurafenib. Within this wide and diverse set of proteins, protein kinase CK1δ is a particularly interesting target for the treatment of several widespread neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Computational methodologies, such as molecular docking, are already routinely and successfully applied in FBDD campaigns alongside experimental techniques, both in the hit-discovery and in the hit-optimization stage. Concerning this, the open-source software Autogrow, developed by the Durrant lab, is a semi-automated computational protocol that exploits a combination between a genetic algorithm and a molecular docking software for de novo drug design and lead optimization. In the current work, we present and discuss a modified version of the Autogrow code that implements a custom scoring function based on the similarity between the interaction fingerprint of investigated compounds and a crystal reference. To validate its performance, we performed both a de novo and a lead-optimization run (as described in the original publication), evaluating the ability of our fingerprint-based protocol to generate compounds similar to known CK1δ inhibitors based on both the predicted binding mode and the electrostatic and shape similarity in comparison with the standard Autogrow protocol.

Computational method for the systematic alignment of analogue series with structure-activity relationship transfer potential across different targets

Lead optimization focuses on the generation of analogue series (ASs) with sustainable structure-activity relationship (SAR) progression. If roadblocks are encountered during multi-property optimization, it is often desirable to replace an AS with another containing a different core structure but having similar SAR characteristics for a given target. This process represents target-based SAR transfer. A previously unexplored question is to what extent AS-based SAR transfer events might also occur across different targets. To address this question, we have developed and applied a new computational approach to systematically search for ASs with SAR transfer potential and align qualifying series in a chemically intuitive way. The methodology relies on fragment similarity scoring in combination with dynamic programming. Our large-scale analysis has revealed that SAR transfer events across different targets are more frequently observed than one might expect, providing many opportunities for the design of new SAR transfer analogues for evolving series.

DEPACT and PACMatch: A Workflow of Designing De Novo Protein Pockets to Bind Small Molecules

Engineering of new functional proteins such as enzymes and biosensors involves the design of new protein pockets for the specific binding of small molecules. Here, we report a workflow composed of two new computational methods to execute this task. The DEPACT (Design Pocket as a Cluster based on Templates) method is a data-driven approach to design and evaluate small-molecule-binding pockets as isolated clusters, while the PACMatch method is a computational approach to match pocket residues in a cluster model to positions on given protein scaffolds. Using DEPACT and its scoring function, pocket clusters of natural-pocket-like chemical compositions and protein-ligand interaction strength can be designed. DEPACT can design pocket clusters containing water- or metal-ion-mediated protein-ligand interactions. While being able to efficiently treat relatively large pocket cluster models (e.g., of around 10 pocket residues), PACMatch outperforms previous methods in test cases of recovering the native positions of pocket residues in natural enzyme-substrate complexes.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.

Fragment based drug design

Rational approaches towards drug development have emerged as one of the most promising ways among the tedious conventional procedures with the aim of redefining the drug discovery process. The need of current medical system is demanding a much precise, faster and reliable approaches in parallel to faster growing technology for development of drugs with more intrinsic action and fewer side effects. Systematic and well-defined diagnostic studies have revealed the specific causes of disease and related targets for drug development. Designing a drug as per the specific target, studying it in-silico prior to its development has been proved as an added benefit to the studies. Many approaches like structure based drug design, fragment based drug design and ligand based drug design are been in practice for the drug discovery and development with the similar fundamental theory. Fragment based drug design utilizes a library of fragments designed specifically for the concerned target and these fragments are studied further before screening with virtual methods as well as with biophysical methods. The process follows a well-defined pathway which moulds a fragment into a perfect drug candidate. In this chapter we have tried to cover all the basic aspects of fragment based drug design and related technologies.

Discovery of Cofactor Competitive Inhibitors against the Human Methyltransferase Fibrillarin

Fibrillarin (FBL) is an essential and evolutionarily highly conserved S-adenosyl methionine (SAM) dependent methyltransferase. It is the catalytic component of a multiprotein complex that facilitates 2'-O-methylation of ribosomal RNAs (rRNAs), a modification essential for accurate and efficient protein synthesis in eukaryotic cells. It was recently established that human FBL (hFBL) is critical for Nipah, Hendra, and respiratory syncytial virus infections. In addition, overexpression of hFBL contributes towards tumorgenesis and is associated with poor survival in patients with breast cancer, suggesting that hFBL is a potential target for the development of both antiviral and anticancer drugs. An attractive strategy to target cofactor-dependent enzymes is the selective inhibition of cofactor binding, which has been successful for the development of inhibitors against several protein methyltransferases including PRMT5, DOT1L, and EZH2. In this work, we solved crystal structures of the methyltransferase domain of hFBL in apo form and in complex with the cofactor SAM. Screening of a fluorinated fragment library, via X-ray crystallography and 19F NMR spectroscopy, yielded seven hit compounds that competed with cofactor binding, two of which resulted in co-crystal structures. One of these structures revealed unexpected conformational variability in the cofactor binding site, which allows it to accommodate a compound significantly different from SAM. Our structural data provide critical information for the design of selective cofactor competitive inhibitors targeting hFBL, and preliminary elaboration of hit compounds has led to additional cofactor site binders.

Selection of Promising Novel Fragment Sized S. aureus SrtA Noncovalent Inhibitors Based on QSAR and Docking Modeling Studies

Sortase A (SrtA) of Staphylococcus aureus has been identified as a promising target to a new type of antivirulent drugs, and therefore, the design of lead molecules with a low nanomolar range of activity and suitable drug-like properties is important. In this work, we aimed at identifying new fragment-sized starting points to design new noncovalent S. aureus SrtA inhibitors by making use of the dedicated molecular motif, 5-arylpyrrolidine-2-carboxylate, which has been previously shown to be significant for covalent binding SrtA inhibitors. To this end, an in silico approach combining QSAR and molecular docking studies was used. The known SrtA inhibitors from the ChEMBL database with diverse scaffolds were first employed to derive descriptors and interpret their significance and correlation to activity. Then, the classification and regression QSAR models were built, which were used for rough ranking of the virtual library of the synthetically feasible compounds containing the dedicated motif. Additionally, the virtual library compounds were docked into the “activated” model of SrtA (PDB:2KID). The consensus ranking of the virtual library resulted in the most promising structures, which will be subject to further synthesis and experimental testing in order to establish new fragment-like molecules for further development into antivirulent drugs.

Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands.

The Fellowship of Privileged Scaffolds-One Structure to Inhibit Them All

Over the past few years, the application of privileged structure has emerged as a powerful approach to the discovery of new biologically active molecules. Privileged structures are molecular scaffolds with binding properties to the range of different biological targets. Moreover, privileged structures typically exhibit good drug-like properties, thus assuring more drug-like properties of modified compound. Our main objective is to discuss the privileged structures used for the development of antiviral agents.

Utilizing graph machine learning within drug discovery and development

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.

Fucosyltransferase 2 inhibitors: Identification via docking and STD-NMR studies

Fucosyltransferase 2 (FUT2) catalyzes the biosynthesis of A, B, and H antigens and other important glycans, such as (Sialyl Lewisx) sLex, and (Sialyl Lewisy) sLey. The production of these glycans is increased in various cancers, hence to design and develop specific inhibitors of FUT2 is a therapeutic strategy. The current study was designed to identify the inhibitors for FUT2. In silico screening of 300 synthetic compounds was performed. Molecular docking studies highlighted the interactions of ligands with critical amino acid residues, present in the active site of FUT2. The epitope mapping in ligands was performed using the STD-NMR experiments to identify the interactions between ligands, and receptor protein. Finally, we have identified 5 lead compounds 4, 5, 26, 27, and 28 that can be studied for further development as cancer therapeutic agents.

Exploring and mapping chemical space with molecular assembly trees

The rule-based search of chemical space can generate an almost infinite number of molecules, but exploration of known molecules as a function of the minimum number of steps needed to build up the target graphs promises to uncover new motifs and transformations. Assembly theory is an approach to compare the intrinsic complexity and properties of molecules by the minimum number of steps needed to build up the target graphs. Here, we apply this approach to prebiotic chemistry, gene sequences, plasticizers, and opiates. This allows us to explore molecules connected to the assembly tree, rather than the entire space of molecules possible. Last, by developing a reassembly method, based on assembly trees, we found that in the case of the opiates, a new set of drug candidates could be generated that would not be accessible via conventional fragment-based drug design, thereby demonstrating how this approach might find application in drug discovery.

Sulfonated β-cyclodextrins: efficient supramolecular organocatalysts for diverse organic transformations

The present review summarizes various organic transformations carried out by using sulfonated β-cyclodextrins such as β-cyclodextrin sulfonic acid, β-cyclodextrin propyl sulfonic acid, and β-cyclodextrin butyl sulfonic acid as an efficient, supramolecular reusable catalyst under diverse reaction conditions.

Controlling Surface Wettability for Automated In Situ Array Synthesis and Direct Bioscreening

The in situ synthesis of biomolecules on glass surfaces for direct bioscreening can be a powerful tool in the fields of pharmaceutical sciences, biomaterials, and chemical biology. However, it is still challenging to 1) achieve this conventional multistep combinatorial synthesis on glass surfaces with small feature sizes and high yields and 2) develop a surface which is compatible with solid-phase syntheses, as well as the subsequent bioscreening. This work reports an amphiphilic coating of a glass surface on which small droplets of polar aprotic organic solvents can be deposited with an enhanced contact angle and inhibited motion to permit fully automated multiple rounds of the combinatorial synthesis of small-molecule compounds and peptides. This amphiphilic coating can be switched into a hydrophilic network for protein- and cell-based screening. Employing this in situ synthesis method, chemical space can be probed via array technology with unprecedented speed for various applications, such as lead discovery/optimization in medicinal chemistry and biomaterial development.

Protein-Small Molecule Interactions in Native Mass Spectrometry

Small molecule drug discovery has been propelled by the continual development of novel scientific methodologies to occasion therapeutic advances. Although established biophysical methods can be used to obtain information regarding the molecular mechanisms underlying drug action, these approaches are often inefficient, low throughput, and ineffective in the analysis of heterogeneous systems including dynamic oligomeric assemblies and proteins that have undergone extensive post-translational modification. Native mass spectrometry can be used to probe protein-small molecule interactions with unprecedented speed and sensitivity, providing unique insights into polydisperse biomolecular systems that are commonly encountered during the drug discovery process. In this review, we describe potential and proven applications of native MS in the study of interactions between small, drug-like molecules and proteins, including large multiprotein complexes and membrane proteins. Approaches to quantify the thermodynamic and kinetic properties of ligand binding are discussed, alongside a summary of gas-phase ion activation techniques that have been used to interrogate the structure of protein-small molecule complexes. We additionally highlight some of the key areas in modern drug design for which native mass spectrometry has elicited significant advances. Future developments and applications of native mass spectrometry in drug discovery workflows are identified, including potential pathways toward studying protein-small molecule interactions on a whole-proteome scale.

Rational Design of Potent Inhibitors of a Metallohydrolase Using a Fragment-Based Approach

Metallohydrolases form a large group of enzymes that have fundamental importance in a broad range of biological functions. Among them, the purple acid phosphatases (PAPs) have gained attention due to their crucial role in the acquisition and use of phosphate by plants and also as a promising target for novel treatments of bone-related disorders and cancer. To date, no crystal structure of a mammalian PAP with drug-like molecules bound near the active site is available. Herein, we used a fragment-based design approach using structures of a mammalian PAP in complex with the Maybridge^TM fragment CC063346, the amino acid L-glutamine and the buffer molecule HEPES, as well as various solvent molecules to guide the design of highly potent and efficient mammalian PAP inhibitors. These inhibitors have improved aqueous solubility when compared to the clinically most promising PAP inhibitors available to date. Furthermore, drug-like fragments bound in newly discovered binding sites mapped out additional scaffolds for further inhibitor discovery, as well as scaffolds for the design of inhibitors with novel modes of action.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Focused Libraries for Epigenetic Drug Discovery: The Importance of Isosteres

Epigenetic drug discovery provides a wealth of opportunities for the discovery of new therapeutics but has been hampered by low hit rates, frequent identification of false-positives, and poor synthetic tractability. A key reason for this is that few screening collections consider the unique requirements of epigenetic targets despite significant medicinal chemistry interest. Here we analyze the suitability of some commercially available screening collections in the context of epigenetic drug discovery, with a particular focus on lysine post-translational modifications, and show that even privileged motifs found in U.S. Food and Drug Administration (FDA)-approved drugs are not present in these collections. We propose that the incorporation of epigenetic bioisosteres should become central in the design of new focused screening collections and highlight some opportunities for the development of synthetic methods which may improve the tractability of hit molecules.