Concepts and Core Principles of Fragment-Based Drug Design

Assessment of fragment docking and scoring with the endothiapepsin model system

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Fragment-based drug design of novel inhibitors targeting lipoprotein (a) kringle domain KIV-10-mediated cardiovascular disease

Cardiovascular diseases (CVDs) are the leading cause of death globally, attributed to a complex etiology involving metabolic, genetic, and protein-related factors. Lipoprotein(a) (Lp(a)), identified as a genetic risk factor, exhibits elevated levels linked to an increased risk of cardiovascular diseases. The lipoprotein(a) kringle domains have recently been identified as a potential target for the treatment of CVDs, in this study we utilized a fragment-based drug design approach to design a novel, potent, and safe inhibitor for lipoprotein(a) kringle domain. With the use of fragment library (61,600 fragments) screening, combined with analyses such as MM/GBSA, molecular dynamics simulation (MD), and principal component analysis, we successfully identified molecules effective against the kringle domains of Lipoprotein(a). The hybridization process (Breed) of the best fragments generated a novel 249 hybrid molecules, among them 77 exhibiting superior binding affinity (≤ -7 kcal/mol) compared to control AZ-02 (-6.9 kcal/mol), Importantly, the top ten molecules displayed high similarity to the control AZ-02. Among the top ten molecules, BR1 exhibited the best docking energy (-11.85 kcal/mol ), and higher stability within the protein LBS site, demonstrating the capability to counteract the pathophysiological effects of lipoprotein(a) [Lp(a)]. Additionally, principal component analysis (PCA) highlighted a similar trend of motion during the binding of BR1 and the control compound (AZ-02), limiting protein mobility and reducing conformational space. Moreover, ADMET analysis indicated favorable drug-like properties, with BR1 showing minimal violations of Lipinski's rules. Overall, the identified compounds hold promise as potential therapeutics, addressing a critical need in cardiovascular medicine. Further preclinical and clinical evaluations are needed to validate their efficacy and safety, potentially ushering in a new era of targeted therapies for CVDs.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

ChemoDOTS: a web server to design chemistry-driven focused libraries

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300 Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model

Rapid advancement in the computational methods of structure-based drug design has led to their widespread adoption as key tools in the early drug development process. Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a significant impact on the success of early drug design efforts. However, screening-based methods are limited in their scalability due to computational limits and the sheer scale of drug-like space. An approach within the quickly evolving field of artificial intelligence (AI), deep generative modeling, is extending the reach of molecular design beyond classical methods by learning the fundamental intra- and inter-molecular relationships in drug-target systems from existing data. In this work we introduce DrugHIVE, a deep hierarchical structure-based generative model that enables fine-grained control over molecular generation. Our model outperforms state of the art autoregressive and diffusion-based methods on common benchmarks and in speed of generation. Here, we demonstrate DrugHIVEs capacity to accelerate a wide range of common drug design tasks such as de novo generation, molecular optimization, scaffold hopping, linker design, and high throughput pattern replacement. Our method is highly scalable and can be applied to high confidence AlphaFold predicted receptors, extending our ability to generate high quality drug-like molecules to a majority of the unsolved human proteome.

Targeting imidazole-glycerol phosphate dehydratase in plants: novel approach for structural and functional studies, and inhibitor blueprinting

The histidine biosynthetic pathway (HBP) is targeted for herbicide design with preliminary success only regarding imidazole-glycerol phosphate dehydratase (IGPD, EC 4.2.1.19), or HISN5, as referred to in plants. HISN5 catalyzes the sixth step of the HBP, in which imidazole-glycerol phosphate (IGP) is dehydrated to imidazole-acetol phosphate. In this work, we present high-resolution cryoEM and crystal structures of Medicago truncatula HISN5 (MtHISN5) in complexes with an inactive IGP diastereoisomer and with various other ligands. MtHISN5 can serve as a new model for plant HISN5 structural studies, as it enables resolving protein-ligand interactions at high (2.2 Å) resolution using cryoEM. We identified ligand-binding hotspots and characterized the features of plant HISN5 enzymes in the context of the HISN5-targeted inhibitor design. Virtual screening performed against millions of small molecules not only revealed candidate molecules but also identified linkers for fragments that were experimentally confirmed to bind. Based on experimental and computational approaches, this study provides guidelines for designing symmetric HISN5 inhibitors that can reach two neighboring active sites. Finally, we conducted analyses of sequence similarity networks revealing that plant HISN5 enzymes derive from cyanobacteria. We also adopted a new approach to measure MtHISN5 enzymatic activity using isothermal titration calorimetry and enzymatically synthesized IGP.

The allure of targets for novel drugs

The challenges of bringing new medicines to patients have been extensively discussed and debated, including consideration of the contribution that academic laboratories can make. At the University of Strathclyde, drug discovery has been a continuing focal activity since the 1960s, and in the past 30 years, the author has led or contributed to many projects of different character and for diverse diseases. A feature common to these projects is the extension of concepts of molecular and biological targets in drug discovery research. In mechanistic terms, these have included compounds that are activators and not inhibitors, and in particular multitargeted compounds. With respect to relevance to disease, schizophrenia, pulmonary disfunction, autoimmune, and infectious disease are most relevant. These projects are discussed in the context of classical medicinal chemistry and more recent concepts in and approaches to drug discovery.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Kinetic solubility: Experimental and machine-learning modeling perspectives

Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter-laboratory reproducibility because of its sensitivity to protocol parameters [1]. Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility. The kinetic solubility QSPR model developed in this study is freely accessible through the Predictor web service of the Laboratory of Chemoinformatics (https://chematlas.chimie.unistra.fr/cgi-bin/predictor2.cgi).

Structural Insight into Geranylgeranyl Diphosphate Synthase (GGDPS) for Cancer Therapy

Geranylgeranyl diphosphate synthase (GGDPS), the source of the isoprenoid donor in protein geranylgeranylation reactions, has become an attractive target for anticancer therapy due to the reliance of cancers on geranylgeranylated proteins. Current GGDPS inhibitor development focuses on optimizing the drug-target enzyme interactions of nitrogen-containing bisphosphonate-based drugs. To advance GGDPS inhibitor development, understanding the enzyme structure, active site, and ligand/product interactions is essential. Here we provide a comprehensive structure-focused review of GGDPS. We reviewed available yeast and human GGDPS structures and then used AlphaFold modeling to complete unsolved structural aspects of these models. We delineate the elements of higher-order structure formation, product-substrate binding, the electrostatic surface, and small-molecule inhibitor binding. With the rise of structure-based drug design, the information provided here will serve as a valuable tool for rationally optimizing inhibitor selectivity and effectiveness.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Decision tree-based identification of important molecular fragments for protein-ligand binding

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank

While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.

A multi-reservoir extruder for time-resolved serial protein crystallography and compound screening at X-ray free-electron lasers

Serial crystallography at X-ray free-electron lasers (XFELs) permits the determination of radiation-damage free static as well as time-resolved protein structures at room temperature. Efficient sample delivery is a key factor for such experiments. Here, we describe a multi-reservoir, high viscosity extruder as a step towards automation of sample delivery at XFELs. Compared to a standard single extruder, sample exchange time was halved and the workload of users was greatly reduced. In-built temperature control of samples facilitated optimal extrusion and supported sample stability. After commissioning the device with lysozyme crystals, we collected time-resolved data using crystals of a membrane-bound, light-driven sodium pump. Static data were also collected from the soluble protein tubulin that was soaked with a series of small molecule drugs. Using these data, we identify low occupancy (as little as 30%) ligands using a minimal amount of data from a serial crystallography experiment, a result that could be exploited for structure-based drug design.

Introducing covalent warheads on spirocyclic sp2-sp3 fragments by innate C-H functionalization

Sp2-sp3 fragments play a vital role in fragment-based drug design (FBDD). Strategies to chemically modify them and efficiently access libraries of these compounds have been goals of the highest priority in the last decades. In this work, a series of sp2-sp3 fragments was synthesized and validated for that purpose, based on their measured physical-chemical properties. Selective C-H cyanation and allylation of these fragments was demonstrated by simple heating in presence of an appropriate hydrogen-atom transfer reagent and a radical acceptor. These conditions enabled a streamlined access to covalent fragments in a single step, by direct introduction of the desired covalent binder. Preliminary results on vinylation, as well as late-stage functionalization of a drug analogue were disclosed.

Synthesis and vectorial functionalisation of pyrazolo[3,4-c]pyridines

Heterocycles are a cornerstone of fragment-based drug discovery (FBDD) due to their prevalence in biologically active compounds. However, novel heterocyclic fragments are only valuable if they can be suitably elaborated to compliment a chosen target protein. Here we describe the synthesis of 5-halo-1H-pyrazolo[3,4-c]pyridine scaffolds and demonstrate how these compounds can be selectively elaborated along multiple growth-vectors. Specifically, N-1 and N-2 are accessed through protection-group and N-alkylation reactions; C-3 through tandem borylation and Suzuki-Miyaura cross-coupling reactions; C-5 through Pd-catalysed Buchwald-Hartwig amination; and C-7 through selective metalation with TMPMgCl.LiCl followed by reaction with electrophiles or transmetalation to ZnCl2 and Negishi cross-coupling. Linking multiple functionalisation strategies emulates a hit-to-lead pathway and demonstrates the utility of pyrazolo[3,4-c]pyridines to FBDD.

Construction and Evaluation of Novel Dual-function Antifungal Inhibitors and Covalent Organic Framework Carriers Based on the Infection Microenvironment

In this study, PD-L1 and CYP51 were selected as key dual-target enzymes, which play an important role in the process of fungal proliferation and immune suppression. A series of novel bifonazole dual-target compounds were designed through the method of fragment combination. Their chemical structure was synthesized, characterized, and evaluated. Among them, the compounds (10c-1, 14a-2, 17c-2) exhibited excellent antifungal and antidrug-resistant fungal activity in vitro. In particular, the preferred compound 14a-2 with high-efficiency dual-target inhibitor ability could block the fungal proliferation and activate the organism's immune efficacy. Moreover, the corresponding covalent organic framework carrier was also successfully constructed to improve its bioavailability. This significantly accelerated the body's recovery process from fungal infection in vivo. In summary, this study expanded the scientific frontier of antifungal drugs and provided a feasible candidate pathway for clinical treatment of fungal infections.

TWN-FS method: A novel fragment screening method for drug discovery

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.

Discovery of rigid biphenyl Plasmodium falciparum DHFR inhibitors using a fragment linking strategy

Plasmodium falciparum dihydrofolate reductase (PfDHFR), a historical target for antimalarials, has been considered compromised due to resistance inducing mutations caused by pyrimethamine (PYR) overexposure. The clinical candidate P218 has demonstrated that inhibitors could efficiently target both PYR-sensitive and PYR-resistant parasites through careful drug design. Yet, P218 clinical development has been limited by its pharmacokinetic profile, incompatible with single dose regimen. Herein, we report the design of new PfDHFR inhibitors using fragment-based design, aiming at improved lipophilicity and overall drug-like properties. Fragment-based screening identified hits binding in the pABA site of the enzyme. Using structure-guided design, hits were elaborated into leads by fragment linking with 2,4-diaminopyrimidine. Resulting compounds display nM range inhibition of both drug-sensitive and resistant PfDHFR, high selectivity against the human isoform, drug-like lipophilicity and metabolic stability. Compound 4 and its ester derivative 3 kill blood stage TM4/8.2 parasite at nM concentrations while showing no toxicity against Vero cells.

Design of Tracers in Fluorescence Polarization Assay for Extensive Application in Small Molecule Drug Discovery

Development of fluorescence polarization (FP) assays, especially in a competitive manner, is a potent and mature tool for measuring the binding affinities of small molecules. This approach is suitable for high-throughput screening (HTS) for initial ligands and is also applicable for further study of the structure-activity relationships (SARs) of candidate compounds for drug discovery. Buffer and tracer, especially rational design of the tracer, play a vital role in an FP assay system. In this perspective, we provided different kinds of approaches for tracer design based on successful cases in recent years. We classified these tracers by different types of ligands in tracers, including peptide, nucleic acid, natural product, and small molecule. To make this technology accessible for more targets, we briefly described the basic theory and workflow, followed by highlighting the design and application of typical FP tracers from a perspective of medicinal chemistry.

Computational Fragment-Based Design of Phytochemical Derivatives as EGFR Inhibitors

Epidermal growth factor receptor (EGFR) is a potential target with disease modifying benefits against Alzheimer's disease (AD). Repurposing of FDA approved drugs against EGFR have shown beneficial effect against AD but are confined to quinazoline, quinoline and aminopyrimidines. Futuristically, the possibility of acquiring drug resistance mutation as seen in the case of cancer could also hamper AD treatment. To identify novel chemical scaffolds, we rooted on phytochemicals identified from plants such as Acorus calamus, Bacopa monnieri, Convolvulus pluricaulis, Tinospora cordifloia, and Withania somnifera that have well-established records in the treatment of brain disorders. The rationale was to mimic the biosynthetic metabolite extension process observed in the plants for synthesizing new phytochemical derivates. Thus, novel compounds were designed computationally by fragment-based method followed by extensive in silico analysis to pick potential phytochemical derivates. PCD1, 8 and 10 were predicted to have better blood brain barrier permeability. ADMET and SoM analysis suggested that these PCDs exhibited druglike properties. Further simulation studies showed that PCD1 and PCD8 stably interact with EGFR and have the potential to be used even in cases of drug-resistance mutations. With further experimental evidence, these PCDs could be leveraged as potential inhibitors of EGFR.

Inhibitor Trapping in N-Myristoyltransferases as a Mechanism for Drug Potency

Predicting inhibitor potency is critical in drug design and development, yet it has remained one of computational biology's biggest unresolved challenges. Here, we show that in the case of the N-myristoyltransferase (NMT), this problem could be traced to the mechanisms by which the NMT enzyme is inhibited. NMT adopts open or closed conformations necessary for orchestrating the different steps of the catalytic process. The results indicate that the potency of the NMT inhibitors is determined by their ability to stabilize the enzyme conformation in the closed state, and that in this state, the small molecules themselves are trapped and locked inside the structure of the enzyme, creating a significant barrier for their dissociation. By using molecular dynamics simulations, we demonstrate that the conformational stabilization of the protein molecule in its closed form is highly correlated with the ligands activity and can be used to predict their potency. Hence, predicting inhibitor potency in silico might depend on modeling the conformational changes of the protein molecule upon binding of the ligand rather than estimating the changes in free binding energy that arise from their interaction.

Linkers in fragment-based drug design: an overview of the literature

INTRODUCTION

In fragment-based drug design, fragment linking is a popular strategy where two fragments binding to different sub-pockets of a target are linked together. This attractive method remains challenging especially due to the design of ideal linkers.

AREAS COVERED

The authors review the types of linkers and chemical reactions commonly used to the synthesis of linkers, including those utilized in protein-templated fragment self-assembly, where fragments are directly linked in the presence of the protein. Finally, they detail computational workflows and software including generative models that have been developed for fragment linking.

EXPERT OPINION

The authors believe that fragment linking offers key advantages for compound design, particularly for the design of bivalent inhibitors linking two distinct pockets of the same or different subunits. On the other hand, more studies are needed to increase the potential of protein-templated approaches in FBDD. Important computational tools such as structure-based de novo software are emerging to select suitable linkers. Fragment linking will undoubtedly benefit from developments in computational approaches and machine learning models.

Aminopyridone-linked benzimidazoles: a fragment-based drug design for the development of CDK9 inhibitors

Aim: A fragment-based design and synthesis of three novel series of aminopyridone-linked benzimidazoles as potential anticancer candidates with significant CDK9 inhibition was implemented. Materials & methods: All synthesized compounds were submitted to National Cancer Institute, 60 cell lines and seven-dose cytotoxicity toward three cancer cells. Results: Compounds 2, 4a, 4c, 4d, 6a and 8a exhibited significant cytotoxicity and selectivity with IC50 range of 7.61-57.75 μM. Regarding the mechanism either in vitro or in silico, 4a, 6a and 8a displayed potent CDK9 inhibition with IC50 value of 0.424-8.461 μM. Compound 6a arrested the cell cycle at S phase and induced apoptosis in MCF-7 cells. Conclusion: Compound 6a is a promising CDK9 inhibitor that warrants additional research for cancer treatment.

The current role and evolution of X-ray crystallography in drug discovery and development

INTRODUCTION

Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery and structural bioinformatics. The integration of artificial intelligence (AI) into X-ray crystallography has shown great promise in automating and accelerating the analysis of complex structural data, further improving the efficiency and accuracy of structure determination.

AREAS COVERED

This review explores the relationship between X-ray crystallography and other modern structural determination methods. It examines the integration of data acquired from diverse biochemical and biophysical techniques with those derived from structural biology. Additionally, the paper offers insights into the influence of AI on X-ray crystallography, emphasizing how integrating AI with experimental approaches can revolutionize our comprehension of biological processes and interactions.

EXPERT OPINION

Investing in science is crucially emphasized due to its significant role in drug discovery and advancements in healthcare. X-ray crystallography remains an essential source of structural biology data for drug discovery. Recent advances in biochemical, spectroscopic, and bioinformatic methods, along with the integration of AI techniques, hold the potential to revolutionize drug discovery when effectively combined with robust data management practices.

Recent advances in Fragment-based strategies against tuberculosis

Tuberculosis remains one of the world's leading infectious disease killers, causing more than 1.5 million of deaths each year. It is therefore a priority to discover and develop new classes of anti-tuberculosis drugs to design new treatments in order to fight the increasing burden of resistant-tuberculosis. Fragment-based drug discovery (FBDD) relies on the identification of small molecule hits, further improved to high-affinity ligands through three main approaches: fragment growing, merging and linking. The aim of this review is to highlight the recent progresses made in fragment-based approaches for the discovery and development of Mycobacterium tuberculosis inhibitors in a wide range of pathways. Hit discovery, hit-to-lead optimization, SAR and binding mode when available are discussed.

Fragment-Based Approaches to Identify RNA Binders

Although fragment-based drug discovery (FBDD) has been successfully implemented and well-explored for protein targets, its feasibility for RNA targets is emerging. Despite the challenges associated with the selective targeting of RNA, efforts to integrate known methods of RNA binder discovery with fragment-based approaches have been fruitful, as a few bioactive ligands have been identified. Here, we review various fragment-based approaches implemented for RNA targets and provide insights into experimental design and outcomes to guide future work in the area. Indeed, investigations surrounding the molecular recognition of RNA by fragments address rather important questions such as the limits of molecular weight that confer selective binding and the physicochemical properties favorable for RNA binding and bioactivity.

Structure based drug design and fragment based approach to identify potential methotrexate analogues as dual inhibitors for management of psoriasis

Psoriasis is characterized as chronic inflammatory disorder of skin having unregulated hyperproliferation and shedding of plaques. As per first line treatment methotrexate is the most widely used cytotoxic drug for psoriasis. It shows anti-proliferative effect with hDHFR while anti-inflammatory and immunosuppressive action is due to AICART. Serious hepatotoxic effects are recognized with long-term treatment of methotrexate. In this study, in silico technique is used in this work to find Dual-Acting Methotrexate-like molecules with increased efficacy and decreased toxicity. Structure-based virtual screening assisted by a fragment-based method against a library of chemicals that are similar to methotrexate revealing 36 and 27 potential inhibitors of hDHFR and AICART respectively. Further, based on dock score, binding energy, molecular interactions, and ADME/T analysis compound 135565151 was chosen for dynamic stability evaluation. Overall, these findings provided information on possible methotrexate analogues for the treatment of psoriasis that had lower hepatotoxicity.Communicated by Ramaswamy H. Sarma.

Setmelanotide optimization through fragment-growing, molecular docking in-silico method targeting MC4 receptor

Obesity has emerged as a global issue, but with the complex structures of multiple related important targets and their agonists or antagonists determined, the mechanism of ligand-protein interaction may offer new chances for developing new generation agonists anti-obesity. Based on the molecule surface of the cryo-EM protein structure 7AUE, we tried to replace D-Ala3 with D-Met in setmelanotide as the linker site for fragment-growing with De novo evolution. The simulation results indicate that the derivatives could improve the binding abilities with the melanocortin 4 receptor and the selectivity over the melanocortin 1 receptor. The improved selectivity of the newly designed derivatives is mainly due to the shape difference of the molecular surface at the orthosteric peptide-binding pocket between melanocortin 4 receptor and melanocortin 1 receptor. The new extended fragments could not only enhance the binding affinities but also function as a gripper to seize the pore, making it easier to balance and stabilize the other component of the new derivatives. Although it is challenging to synthesize the compounds designed in silico, this study may perhaps serve as a trigger for additional anti-obesity research.Communicated by Ramaswamy H. Sarma.

Identification of novel potential inhibitor of thymidylate kinase from Variola virus

A hit compound was designed using Fragment Based Drug Designing (FBDD) approach, density functional theory (DFT) calculations were performed to find the structural and electronic properties. Additionally, pharmacokinetic properties were studied to understand the biological response of the compound. Docking studies were carried out with the protein structure of VrTMPK and HssTMPK with the reported hit compound. The favored docked complex was further carried to perform MD simulations; the RMSD plot and H-bond analysis was done for 200 ns. Also, MM-PBSA was done to understand the binding energy constituents and stability of the complex. A comparative study of the designed hit compound was done with FDA approved Tecovirimat. As a result, it was found that the reported compound (POX-A)is a potential selective inhibitor for Variola virus. Hence, it can be used to study further in vivo and in vitro behavior of the compound.Communicated by Ramaswamy H. Sarma.

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.

Fragment-Based Lead Discovery Strategies in Antimicrobial Drug Discovery

Fragment-based lead discovery (FBLD) is a powerful application for developing ligands as modulators of disease targets. This approach strategy involves identification of interactions between low-molecular weight compounds (100-300 Da) and their putative targets, often with low affinity (K_D ~0.1-1 mM) interactions. The focus of this screening methodology is to optimize and streamline identification of fragments with higher ligand efficiency (LE) than typical high-throughput screening. The focus of this review is on the last half decade of fragment-based drug discovery strategies that have been used for antimicrobial drug discovery.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Current Fragment-to-lead Approaches Starting from the 7-azaindole: The Pharmacological Versatility of a Privileged Molecular Fragment

Fragment-based drug discovery is one of the most powerful paradigms in the recent context of medicinal chemistry and is being widely practiced by academic and industrial researchers. Currently, azaindoles are among the most exploited molecular fragments in pharmaceutical innovation projects inspired by fragment-to-lead strategies. The 7-azaindole is the most prominent representative within this remarkable family of pyrrolopyridine fragments, as it is present in the chemical structure of several approved antitumor drugs and also of numerous therapeutic candidates. In this paper, a brief overview on existing proofs of concept in the literature will be presented, as well as some recent works that corroborate 7-azaindole as a privileged and pharmacologically versatile molecular fragment.

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.

Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery

Fragment based drug discovery has long been used for the identification of new ligands and interest in targeted covalent inhibitors has continued to grow in recent years, with high profile drugs such as osimertinib and sotorasib gaining FDA approval. It is therefore unsurprising that covalent fragment-based approaches have become popular and have recently led to the identification of novel targets and binding sites, as well as ligands for targets previously thought to be 'undruggable'. Understanding the properties of such covalent fragments is important, and characterizing and/or predicting reactivity can be highly useful. This review aims to discuss the requirements for an electrophilic fragment library and the importance of differing warhead reactivity. Successful case studies from the world of drug discovery are then be examined.

Native Mass Spectrometry-Guided Screening Identifies Hit Fragments for HOP-HSP90 PPI Inhibition

Contemporary medicinal chemistry considers fragment-based drug discovery (FBDD) and inhibition of protein-protein interactions (PPI) as important means of expanding the volume of druggable chemical space. However, the ability to robustly identify valid fragments and PPI inhibitors is an enormous challenge, requiring the application of sensitive biophysical methodology. Accordingly, in this study, we exploited the speed and sensitivity of nanoelectrospray (nano-ESI) native mass spectrometry to identify a small collection of fragments which bind to the TPR2AB domain of HOP. Follow-up biophysical assessment of a small selection of binding fragments confirmed binding to the single TPR2A domain, and that this binding translated into PPI inhibitory activity between TPR2A and the HSP90 C-terminal domain. An in-silico assessment of binding fragments at the PPI interfacial region, provided valuable structural insight for future fragment elaboration strategies, including the identification of losartan as a weak, albeit dose-dependent inhibitor of the target PPI.

Fragment-based drug discovery-the importance of high-quality molecule libraries

Fragment-based drug discovery (FBDD) is now established as a complementary approach to high-throughput screening (HTS). Contrary to HTS, where large libraries of drug-like molecules are screened, FBDD screens involve smaller and less complex molecules which, despite a low affinity to protein targets, display more 'atom-efficient' binding interactions than larger molecules. Fragment hits can, therefore, serve as a more efficient start point for subsequent optimisation, particularly for hard-to-drug targets. Since the number of possible molecules increases exponentially with molecular size, small fragment libraries allow for a proportionately greater coverage of their respective 'chemical space' compared with larger HTS libraries comprising larger molecules. However, good library design is essential to ensure optimal chemical and pharmacophore diversity, molecular complexity, and physicochemical characteristics. In this review, we describe our views on fragment library design, and on what constitutes a good fragment from a medicinal and computational chemistry perspective. We highlight emerging chemical and computational technologies in FBDD and discuss strategies for optimising fragment hits. The impact of novel FBDD approaches is already being felt, with the recent approval of the covalent KRAS^G12C inhibitor sotorasib highlighting the utility of FBDD against targets that were long considered undruggable.

Development of Noncovalent Small-Molecule Keap1-Nrf2 Inhibitors by Fragment-Based Drug Discovery

Targeting the protein-protein interaction (PPI) between the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) and its repressor, Kelch-like ECH-associated protein 1 (Keap1), constitutes a promising strategy for treating diseases involving oxidative stress and inflammation. Here, a fragment-based drug discovery (FBDD) campaign resulted in novel, high-affinity (K_i = 280 nM), and cell-active noncovalent small-molecule Keap1-Nrf2 PPI inhibitors. We screened 2500 fragments using orthogonal assays─fluorescence polarization (FP), thermal shift assay (TSA), and surface plasmon resonance (SPR)─and validated the hits by saturation transfer difference (STD) NMR, leading to 28 high-priority hits. Thirteen co-structures showed fragments binding mainly in the P4 and P5 subpockets of Keap1's Kelch domain, and three fluorenone-based fragments featuring a novel binding mode were optimized by structure-based drug discovery. We thereby disclose several fragment hits, including their binding modes, and show how FBDD can be performed to find new small-molecule Keap1-Nrf2 PPI inhibitors.

Advancements in antimicrobial nanoscale materials and self-assembling systems

Antimicrobial resistance is directly responsible for more deaths per year than either HIV/AIDS or malaria and is predicted to incur a cumulative societal financial burden of at least $100 trillion between 2014 and 2050. Already heralded as one of the greatest threats to human health, the onset of the coronavirus pandemic has accelerated the prevalence of antimicrobial resistant bacterial infections due to factors including increased global antibiotic/antimicrobial use. Thus an urgent need for novel therapeutics to combat what some have termed the 'silent pandemic' is evident. This review acts as a repository of research and an overview of the novel therapeutic strategies being developed to overcome antimicrobial resistance, with a focus on self-assembling systems and nanoscale materials. The fundamental mechanisms of action, as well as the key advantages and disadvantages of each system are discussed, and attention is drawn to key examples within each field. As a result, this review provides a guide to the further design and development of antimicrobial systems, and outlines the interdisciplinary techniques required to translate this fundamental research towards the clinic.

Targeting the ubiquitin system by fragment-based drug discovery

The ubiquitin system contains a wealth of potential drug targets for many diseases and conditions, including neurodegenerative, immune, metabolic and developmental diseases, as well as multiple cancers. Despite years of research, relatively few clinical inhibitors or specific chemical probes for proteins within the ubiquitin system exist, with many interesting target proteins yet to be explored. Fragment-based drug discovery (FBDD) offers efficient and broad coverage of chemical space with small libraries, using covalent and non-covalent approaches. Coupled with advances in structural biology and proteomics, FBDD now provides a thorough screening platform for inhibitor discovery within the ubiquitin system. In this mini review, we summarise the current scope of FBDD and how it has been applied to ubiquitin-activating (E1), ubiquitin-conjugating (E2), ubiquitin ligase (E3) and deubiquitinating (DUB) enzymes. We also discuss the newest frontiers of FBDD and how they could be applied to enable inhibitor and novel chemical probe discovery and provide functional insight into the ubiquitin system.

Exploring the Surface of the Ectodomain of the PD-L1 Immune Checkpoint with Small-Molecule Fragments

Development of small molecules targeting the PD-L1/PD-1 interface is advancing both in industry and academia, but only a few have reached early-stage clinical trials. Here, we take a closer look at the general druggability of PD-L1 using in silico hot spot mapping and nuclear magnetic resonance (NMR)-based characterization. We found that the conformational elasticity of the PD-L1 surface strongly influences the formation of hot spots. We deconstructed several generations of known inhibitors into fragments and examined their binding properties using differential scanning fluorimetry (DSF) and protein-based nuclear magnetic resonance (NMR). These biophysical analyses showed that not all fragments bind to the PD-L1 ectodomain despite having the biphenyl scaffold. Although most of the binding fragments induced PD-L1 oligomerization, two compounds, TAH35 and TAH36, retain the monomeric state of proteins upon binding. Additionally, the presence of the entire ectodomain did not affect the binding of the hit compounds and dimerization of PD-L1. The data demonstrated here provide important information on the PD-L1 druggability and the structure-activity relationship of the biphenyl core moiety and therefore may aid in the design of novel inhibitors and focused fragment libraries for PD-L1.

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying “magic bullets” amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand–target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural–energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

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.