Design, synthesis and evaluation of a pyrazolo[3,4-d]pyrimidine derivative as a novel and potent TGFβ1R1 inhibitor

The transforming growth factor β1 (TGFβ1)/SMAD signaling pathway regulates many vital physiological processes. The development of potent inhibitors targeting activin receptor-like kinase 5 (ALK5) would provide potential treatment reagents for various diseases. A significant number of ALK5 inhibitors have been discovered, and they are currently undergoing clinical evaluation at various stages. However, the clinical demands were far from being met. In this study, we utilized an alternative conformation-similarity-based virtual screening (CSVS) combined with a fragment-based drug designing (FBDD) strategy to efficiently discover a potent and active hit with a novel chemical scaffold. After structural optimization in the principle of group replacement, compound 57 was identified as the most promising ALK5 inhibitor. Compound 57 demonstrated significant inhibitory effects against the TGF-β1/SMAD signaling pathway. It could markedly attenuate the production of extracellular matrix (ECM) and deposition of collagen. Also, the lead compound showed adequate pharmacokinetic (PK) properties and good in vivo tolerance. Moreover, treatment with compound 57 in two different xerograph models showed significant inhibitory effects on the growth of pancreatic cancer cells. These results suggested that lead compound 57 refers as a promising ALK5 inhibitor both in vitro and in vivo, which merits further validation.

Fragment growth-based discovery of novel TNIK inhibitors for the treatment of colorectal cancer

Traf2-and Nck-interacting protein kinase (TNIK) plays an important role in regulating signal transduction of the Wnt/β-catenin pathway and is considered an important target for the treatment of colorectal cancer. Inhibiting TNIK has potential to block abnormal Wnt/β-catenin signal transduction caused by colorectal cancer mutations. We discovered a series of 6-(1-methyl-1H-imidazole-5-yl) quinoline derivatives as TNIK inhibitors through Deep Fragment Growth and virtual screening. Among them, 35b exhibited excellent TNIK kinase and HCT116 cell inhibitory activity with IC50 values of 6 nM and 2.11 μM, respectively. 35b also shown excellent kinase selectivity, PK profiles, and oral bioavailability (84.64%). At a p. o. dosage of 50 mg/kg twice daily 35b suppressed tumor growth on the HCT116 xenograft model. Taken together, 35b is a promising lead compound of TNIK inhibitors, which merits further investigation.

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.

QM assisted ML for 19F NMR chemical shift prediction

BACKGROUND

Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens.

METHODS

In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set.

RESULTS

Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm .

Integrated fragment-based drug design and virtual screening techniques for exploring the antidiabetic potential of thiazolidine-2,4-diones: Design, synthesis and in vivo studies

Diabetes mellitus is a metabolic disorder characterized by elevated blood sugar levels and related complications. This study focuses on harnessing and integrating fragment-based drug design and virtual screening techniques to explore the antidiabetic potential of newly synthesized thiazolidine-2,4-dione derivatives. The research involves the design of novel variations of thiazolidine-2,4-dione compounds by Fragment-Based Drug Design. The screening process involves pharmacophore based virtual screening through docking algorithms, and the identification of newly twelve top-scoring compounds. The molecular docking analysis revealed that compounds SP4e, SP4f showed highest docking scores of -9.082 and -10.345. The binding free energies of the compounds SP4e, SP4f and pioglitazone was found to be -19.9, -16.1 and -13 respectively, calculated using the Prime MM/GBSA approach. The molecular dynamic study validates the docking results. Furthermore, In the Swiss albino mice model, both SP4e and SP4f exhibited significant hypoglycaemic effects, comparable to the reference drug pioglitazone. Furthermore, these compounds demonstrated favorable effects on the lipid profile, reducing total cholesterol, triglycerides, and LDL levels while increasing HDL levels. In mice tissue, the disease control group showed PPAR-γ expression of 4.200 ± 0.24, while compound SP4f displayed higher activation at 7.84 ± 0.431 compared to compound SP4e with an activation of 7.68 ± 0.65. In zebrafish model, SP4e and SP4f showed significant reductions in blood glucose levels and lipid peroxidation, along with increased glutathione levels and catalase activity. These findings highlighted the potential of SP4e and SP4f as antidiabetic agents, warranting further exploration for therapeutic applications. The in vitro study was performed in HEK-2 cell line, the pioglitazone group demonstrated PPAR-γ expression of EC50 = 575.2, while compound SP4f exhibited enhanced activation at EC50 = 739.0 in contrast to compound SP4e activation of EC50 = 826.7.

Development of selective class I protein arginine methyltransferase inhibitors through fragment-based drug design approach

Protein arginine methyltransferases (PRMTs) catalyze the methylation of the terminal nitrogen atoms of the guanidino group of arginine of protein substrates. The aberrant expression of these methyltransferases is linked to various diseases, making them promising therapeutic targets. Currently, PRMT inhibitors are at different stages of clinical development, which validated their significance as drug targets. Structural Genomics Consortium (SGC) has reported several small fragment inhibitors as Class I PRMT inhibitors, which can be the starting point for rational drug development. Herein, we report the successful application of a fragment-based approach toward the discovery of selective Class I PRMT inhibitors. Structure-based ligand optimization was performed by strategic incorporation of fragment hits on the drug-like quinazoline core and subsequent fragment growth in the desired orientation towards identified hydrophobic shelf. A clear SAR was established, and the lead compounds 55 and 56 displayed potent inhibition of Class I PRMTs with IC50 values of 92 nM and 37 nM against PRMT4. We report the systematic development of potent Class I PRMT inhibitors with good potency and about 100-fold selectivity when tested against a panel of 31 human DNA, RNA, and protein lysine and arginine methyltransferases. These improved small molecules will provide new options for the development of novel potent and selective PRMT4 inhibitors.

Examining multi-objective deep reinforcement learning frameworks for molecular design

Drug design and optimization are challenging tasks that call for strategic and efficient exploration of the extremely vast search space. Multiple fragmentation strategies have been proposed in the literature to mitigate the complexity of the molecular search space. From an optimization standpoint, drug design can be considered as a multi-objective optimization problem. Deep reinforcement learning (DRL) frameworks have demonstrated encouraging results in the field of drug design. However, the scalability of these frameworks is impeded by substantial training intervals and inefficient use of sample data. In this paper, we (1) examine the core principles of deep or multi-objective RL methods and their applications in molecular design, (2) analyze the performance of a recent multi-objective DRL-based and fragment-based drug design framework, named DeepFMPO, in a real-world application by incorporating optimization of protein-ligand docking affinity with varying numbers of other objectives, and (3) compare this method with a single-objective variant. Through trials, our results indicate that the DeepFMPO framework (with docking score) can achieve success, however, it suffers from training instability. Our findings encourage additional exploration and improvement of the framework. Potential sources of the framework's instability and suggestions of further modifications to stabilize the framework are discussed.

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.

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.

Synthesis-accessibility-oriented design of c-Jun N-terminal kinase 1 inhibitor

Idiopathic pulmonary fibrosis (IPF) is a severe and progressive lung disease with poor prognosis and limited treatment options. The c-Jun N-Terminal Kinase 1 (JNK1), a key component of the MAPK pathway, has been implicated in the pathogenesis of IPF and represents a potential therapeutic target. However, the development of JNK1 inhibitors has been slowed, partly due to synthetic complexity in medicinal chemistry modification. Here, we report a synthesis-accessibility-oriented strategy for designing JNK1 inhibitors based on computational prediction of synthetic feasibility and fragment-based molecule generation. This strategy led to the discovery of several potent JNK1 inhibitors, such as compound C6 (IC₅₀ = 33.5 nM), which exhibited comparable activity to the clinical candidate CC-90001 (IC₅₀ = 24.4 nM). The anti-fibrotic effect of C6 was further confirmed in animal model of pulmonary fibrosis. Moreover, compound C6 could be synthesized in only two steps, compared to nine steps for CC-90001. Our findings suggest that compound C6 is a promising lead for further optimization and development as a novel anti-fibrotic agent targeting JNK1. In addition, the discovery of C6 also demonstrates the feasibility of synthesis-accessibility-oriented strategy in lead discovery.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Discovery and optimization of natural-based nanomolar c-Kit inhibitors via in silico and in vitro studies

c-Kit is a receptor tyrosine kinase which is involved in intracellular signaling and mutations of c-Kit have been associated with various types of cancers. Investigations have shown that inhibition of c-Kit, using tyrosine kinase inhibitors, yielded promising results in cancer treatment marking it as a promising target for cancer therapy. However, the emerging resistance for the current therapy necessitates the development of more potent inhibitors which are not affected by these mutations. Herein, virtual screening of a library of natural-based compounds yielded three hits (2, 5 and 6) which possessed nanomolar inhibitory (2.02, 4.33 and 2.80 nM, respectively) activity when tested in vitro against c-Kit. Single point mutation docking studies showed the hits to be unaffected by the most common resistance mutation in imatinib-resistant cells, mutation of Val654. Although, the top hits exhibited around 3000 higher inhibitory potency toward c-Kit when compared to imatinib (5.4 µM), previous studies have shown that they are metabolically unstable. Fragment-based drug design approaches were then employed to enhance binding affinity of the top hit and make it more metabolically stable. Screening of the generated fragments yielded a new derivative, F1, which demonstrated stronger binding affinity, stability and binding free energy when compared to the hit compound 2.Communicated by Ramaswamy H. Sarma.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

FastGrow: on-the-fly growing and its application to DYRK1A

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.

Development of urease inhibitors by fragment-based dynamic combinatorial chemistry

In this study, fragment-based dynamic combinatorial chemistry (DCC) was explored for the development of novel urease inhibitors. Based on a rationally designed fragment, two iteratively evolved dynamic combinatorial libraries (DCLs) were generated and screened in the presence of urease template. The best ligand identified revealed not only strong urease inhibition but also low cytotoxicity. Additionally, possible inhibitory mechanism was elucidated in the binding kinetic study and docking simulation.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.

Discovery of novel TMPRSS2 inhibitors for COVID-19 using in silico fragment-based drug design, molecular docking, molecular dynamics, and quantum mechanics studies

The global expansion of COVID-19 and the mutations of severe acute respiratory syndrome coronavirus necessitate quick development of treatment and vaccination. Because the androgen-responsive serine protease TMPRSS2 is involved in cleaving the SARS-CoV-2 spike protein allowing the virus to enter the cell, therefore, direct TMPRSS2 inhibition will inhibit virus activation and disease progression which make it an important target for drug discovery. In this study, a homology model of TMPRSS2 protein was initially developed. Then, we used the fragment-based drug design (FBDD) technique to develop effective TMPRSS2 inhibitors. Over a half-million fragments from the enamine database were screened for their binding ability to target protein, and then best-scoring fragments were linked to building new molecules with a good binding affinity. XP docking and MM-GBSA studies revealed 10 new formed molecules with docking score ≤ -14.982 kcal/mol compared to ambroxol (control) with a docking score of -6.464 kcal/mol. Finally, molecular dynamics (MD) and density functional theory (DFT) were calculated for the top 3 molecules.

Identification of a small compound that specifically inhibits Zika virus in vitro and in vivo by targeting the NS2B-NS3 protease

Zika virus (ZIKV) has rapid become a global threat, but no ZIKV-specific vaccines or drugs are currently available. In this study, inhibitors of ZIKV NS2B-NS3 protease were screened from a library containing 4,452 compound fragments. One of the compounds, 6-bromo-1,2-naphthalenedione, exhibited high specific inhibition against ZIKV NS2B-NS3 protease, but had no inhibitory effects against other viral proteases. A microscale thermophoresis (MST) assay confirmed that the compound bound to ZIKV NS2B-NS3 protein with a binding constant (Kd) of 12.26 μM. Indirect immunofluorescence assays, Western blots, and plaque assays indicated that the compound inhibited virus replication in cells. Virus titer was reduced by more than 75% when the compound was present at 1 μM. A time-of-addition assay showed that inhibition occurred at the virus replication stage, but not at the adsorption or invasion stages. The half cytotoxicity concentration (CC50) of the compound on HeLa, Vero, and BHK-21 cells were 445.44 μM, 123.87 μM, and 123.64 μM, respectively. In vivo tests using infected AG129 mice demonstrated that treatment with the compound reduced mortality by up to 60%. Mice treated with the compound showed a reduction in histopathological lesions in brain, testis, and ovary. Viral RNA, IL-1β, and IL-6 mRNA levels decreased significantly in these tissues. In summary, this study has identified a small compound with high and specific inhibitory effects on ZIKV. The compound can be used as a therapeutic agent and is also an ideal starting point for drug optimization.

Progress in Free Energy Perturbation: Options for Evolving Fragments

One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.

Synthesis and identification of a novel skeleton of N-(pyridin-3-yl) proline as a selective CDK4/6 inhibitor with anti-breast cancer activities

CDK4/6 were attractive chemotherapeutic targets for the treatment of malignant tumors, CDK4/6 selective inhibitors have made outstanding contributions in the treatment of breast cancer. However, these inhibitors share a single skeleton of N-(pyridin-2-yl) pyrimidin-2-amine which cannot overcome the side effects in clinical application. In our previous study, an N'- acetylpyrrolidine-1-carbohydrazide was hit as the initial fragment by analyzing the active site characteristics of CDK6. Two series of N-(pyridin-3-yl) proline were obtained by fragment growth method. The QSAR study was carried out according to the in vitro activities data against CDK4/6, and two compounds 7c and 7p with potent inhibitory activities were found to interact with CDK4 in different binding conformation. They showed potential inhibition of cell proliferation against the breast cancer cell, and 7c exhibited promised anti-breast cancer effect in vivo.

Newly designed compounds from scaffolds of known actives as inhibitors of survivin: computational analysis from the perspective of fragment-based drug design

Survivin is an apoptosis suppressing protein linked to different forms of cancer. As it stands, there are no approved drugs for the inhibition of survivin in cancer cells despite a number of promising compounds in clinical trials. This study designed a new set of compounds from fragments of active survivin inhibitors to potentiate their binding with survivin at BIR domain. Three hundred and five (305) fragments made from eight potent inhibitors of survivin were reconstructed to form a new set of compounds. The compounds were optimized using R group enumeration and bioisostere replacement after extensive docking analysis. The optimised compounds were filtered by a validated pharmacophore model to reveal how well they are aligned to the pharmacophore sites. Molecular docking of the well aligned compounds revealed the top-scoring compounds; and these compounds were compared with the eight inhibitors used as template for fragment-based design on the basis of binding affinity (rigid and flexible docking), predicted pIC50 and intermolecular interactions. The electronic behaviours (global descriptors, HOMO/LUMO, molecular electrostatic potential and Fukui functions) of newly designed compounds were calculated to investigate their reactivity and atomic sites prone to neutrophilic/electrophilic attack. The nine newly designed compounds had better rigid and flexible docking scores, free energy of binding and intermolecular interactions with survivin at BIR domain than the eight active inhibitors. Based on frontier molecular orbitals, OPE-3 was found to be the most reactive and less stable compound (0.13194 eV), followed by OPE-4 and OPE-9. The global descriptive parameters showed that OPE-3 had highest softness value (7.5245 eV) while OPE-8 recorded the maximum hardness value (0.08486 eV). The well-validated QSAR model also showed that OPE-3, OPE-7 and OPE-8 had the most significant bioactivity of all the inhibitors. This study thus provides new insight into the design of compounds capable of modulating the activity of survivin.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00108-8.

Fragment-based drug design targeting syntenin PDZ2 domain involved in exosomal release and tumour spread

Syntenin stimulates exosome production and its expression is upregulated in many cancers and implicated in the spread of metastatic tumor. These effects are supported by syntenin PDZ domains interacting with syndecans. We therefore aimed to develop, through a fragment-based drug design approach, novel inhibitors targeting syntenin-syndecan interactions. We describe here the optimization of a fragment, 'hit' C58, identified by in vitro screening of a PDZ-focused fragment library, which binds specifically to the syntenin-PDZ2 domain at the same binding site as the syndecan-2 peptide. X-ray crystallographic structures and computational docking were used to guide our optimization process and lead to compounds 45 and 57 (IC₅₀ = 33 μM and 47 μM; respectively), two representatives of syntenin-syndecan interactions inhibitors, that selectively affect the syntenin-exosome release. These findings demonstrate that it is possible to identify small molecules inhibiting syntenin-syndecan interaction and exosome release that may be useful for cancer therapy.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

Novel 1-(prop-2-yn-1-ylamino)-2,3-dihydro-1H-indene-4-thiol derivatives as potent selective human monoamine oxidase B inhibitors: Design, SAR development, and biological evaluation

Successes have been achieved in developing human monoamine oxidase B (hMAO-B) inhibitors as anti-Parkinson's disease (PD) drugs. However, low efficiency and unwanted side effects of the marketed hMAO-B inhibitors hamper their medical applications, therefore, novel potent selective hMAO-B inhibitors are still of great interest. Herein we report 1-(prop-2-yn-1-ylamino)-2,3-dihydro-1H-indene-4-thiol derivatives as hMAO-B inhibitors, which were designed by employing a fragment-based drug design strategy to link rasagiline to hydrophobic fragments. Among the synthesized 31 compounds, K8 and K24 demonstrated very encouraging hMAO-B inhibitory activities and selectivity over hMAO-A, better than rasagiline and safinamide. In vitro studies indicated that K8 and K24 are nontoxic to nervous tissue cells and they have considerable effects against ROS formation and potential neuroprotective activity. Further mice behavioral tests demonstrated these two compounds have good therapeutic effects on MPTP-induced PD model mice. All these experiment results suggest that compounds K8 and K24 can be promising candidates for further research for treatment of PD.

Protease targeted COVID-19 drug discovery: What we have learned from the past SARS-CoV inhibitors?

The fascinating similarity between the SARS-CoV and SARS-CoV-2, inspires scientific community to investigate deeper into the SARS-CoV proteases such as main protease (Mpro) and papain-like protease (PLpro) and their inhibitors for the discovery of SARS-CoV-2 protease inhibitors. Because of the similarity in the proteases of these two corona viruses, there is a greater chance for the previous SARS-CoV Mpro and PLpro inhibitors to provide effective results against SARS-CoV-2. In this context, the molecular fragments from the SARS-CoV protease inhibitors through the fragment-based drug design and discovery technique can be useful guidance for COVID-19 drug discovery. Here, we have focused on the structure-activity relationship studies of previous SARS-CoV protease inhibitors and discussed about crucial fragments generated from previous SARS-CoV protease inhibitors important for the lead optimization of SARS-CoV-2 protease inhibitors. This study surely offers different strategic options of lead optimization to the medicinal chemists to discover effective anti-viral agent against the devastating disease, COVID-19.

Fragment-Based Drug Design of Selective HDAC6 Inhibitors

Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors - vemurafenib, venetoclax, and erdafitinib, FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as: surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure-activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).

Insights into newly approved drugs from a medicinal chemistry perspective

The development of new drugs is becoming notably harder each decade. To overcome the present pitfalls in the drug development pipeline, such as those related to potency, selectivity, or absorption, distribution, metabolism, excretion and toxicity properties, medicinal chemistry strategies need to be in continuous evolution and need to become even more multidisciplinary. In this review, we present how structure-based, ligand-based, and fragment-based drug design (SBDD, LBDD, and FBDD, respectively) and their respective techniques were used for the design and optimization of successful cases of new molecular entities (NMEs) approved by the Food and Drug Administration (FDA).

Exploration of small-molecule entry disruptors for chikungunya virus by targeting matrix remodelling associated protein

Background and purpose:

A genome-wide clustered regularly interspaced short palindromic repeats- associated protein 9-based screen has revealed that the cell adhesion molecule matrix remodelling associated protein 8 (Mxra8) acts as an entry mediator for many alphaviruses including chikungunya virus. The first X-ray crystal structure reported for Mxra8 a few months ago has a low-resolution of 3.49Å.

Experimental approach:

Homology modelling of Mxra8 protein was done employing the SWISS-MODEL and PRIME module of Maestro. To design novel Mxra8 inhibitors pharmacophore guided fragment-based drug design and structure-based virtual screening of Food and Drug Administration approved drug libraries were undertaken. Molecular docking and molecular dynamics (MD) simulations study were carried out to validate the findings.

Findings / Results:

The molecule H1a (dock score: -6.137, binding energy: -48.95 kcal/mol, and PHASE screen score: 1.528816) was identified as the best hit among the fragment-based designed ligands. Structure- based virtual screening suggested histamine, epinephrine, and capreomycin as potential hits which could be repurposed as Mxra8 inhibitor. MD simulations study suggested that only small molecules like histamine could be a potential inhibitor of Mxra8. H-bond interaction with Arg58 and Glu200 amino acid residues seems to be crucial for effective binding.

Conclusion and implications:

To the best of our knowledge, this is the first report on the design of novel inhibitors against Mxra8 protein to tackle the menace of alphaviruses infections. This design strategy could be used for structure-based drug design against other apo-proteins. This study also advances the application of in silico tools in the field of drug repurposing.

Structural optimization of pyrazolo[1,5-a]pyrimidine derivatives as potent and highly selective DPP-4 inhibitors

Our previous discovery of pyrazolo [1,5-a]pyrimidin-7(4H)-one scaffold-based DPP-4 inhibitors yielded two potent compounds b2 (IC₅₀ = 79 nM) and d1 (IC₅₀ = 49 nM) but characterized by cytotoxicity. Herein, with scaffold hopping and fragment-based drug design strategies, highly potent and selective pyrazolo [1,5-a]pyrimidine DPP-4 inhibitors were found featured by reduced or diminished cytotoxicity. Specifically, c24 (IC₅₀ = 2 nM) exhibits a 25 to 40-fold increase of inhibitory activity respect to those of b2 and d1, respectively, 2-fold from Alogliptin (IC₅₀ = 4 nM), and remarkable selectivity over DPP-8 and DPP-9 (>2000 fold). Further docking studies confirmed that the pyrazolo [1,5-a]pyrimidine core interacts with the S1 pocket whereas its substituted aromatic ring interacts with the sub-S1 pocket. The interactive mode in this case resembles that of Alogliptin and Trelagliptin. Further in vivo IPGTT assays in diabetic mice demonstrated that c24 effectively reduces glucose excursion by 48% at the dose of 10 mg/kg, suggesting that c24 is worthy of further development as a potent anti-diabetes agent.

Studies on fragment-based design of allosteric inhibitors of human factor XIa

Human factor XIa (hFXIa) has emerged as an attractive target for development of new anticoagulants that promise higher level of safety. Different strategies have been adopted so far for the design of anti-hFXIa molecules including competitive and non-competitive inhibition. Of these, allosteric dysfunction of hFXIa's active site is especially promising because of the possibility of controlled reduction in activity that may offer a route to safer anticoagulants. In this work, we assess fragment-based design approach to realize a group of novel allosteric hFXIa inhibitors. Starting with our earlier discovery that sulfated quinazolinone (QAO) bind in the heparin-binding site of hFXIa, we developed a group of two dozen dimeric sulfated QAOs with intervening linkers that displayed a progressive variation in inhibition potency. In direct opposition to the traditional wisdom, increasing linker flexibility led to higher potency, which could be explained by computational studies. Sulfated QAO 19S was identified as the most potent and selective inhibitor of hFXIa. Enzyme inhibition studies revealed that 19S utilizes a non-competitive mechanism of action, which was supported by fluorescence studies showing a classic sigmoidal binding profile. Studies with selected mutants of hFXIa indicated that sulfated QAOs bind in heparin-binding site of the catalytic domain of hFXIa. Overall, the approach of fragment-based design offers considerable promise for designing heparin-binding site-directed allosteric inhibitors of hFXIa.

Development of non-nucleoside reverse transcriptase inhibitors (NNRTIs): our past twenty years

Human immunodeficiency virus (HIV) is the primary infectious agent of acquired immunodeficiency syndrome (AIDS), and non-nucleoside reverse transcriptase inhibitors (NNRTIs) are the cornerstone of HIV treatment. In the last 20 years, our medicinal chemistry group has made great strides in developing several distinct novel NNRTIs, including 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), thio-dihydro-alkoxy-benzyl-oxopyrimidine (S-DABO), diaryltriazine (DATA), diarylpyrimidine (DAPY) analogues, and their hybrid derivatives. Application of integrated modern medicinal strategies, including structure-based drug design, fragment-based optimization, scaffold/fragment hopping, molecular/fragment hybridization, and bioisosterism, led to the development of several highly potent analogues for further evaluations. In this paper, we review the development of NNRTIs in the last two decades using the above optimization strategies, including their structure–activity relationships, molecular modeling, and their binding modes with HIV-1 reverse transcriptase (RT). Future directions and perspectives on the design and associated challenges are also discussed.

Fragments: where are we now?

Fragment-based drug discovery (FBDD) has become a mainstream technology for the identification of chemical hit matter in drug discovery programs. To date, the food and drug administration has approved four drugs, and over forty compounds are in clinical studies that can trace their origins to a fragment-based screen. The challenges associated with implementing an FBDD approach are many and diverse, ranging from the library design to developing methods for identifying weak affinity compounds. In this article, we give an overview of current progress in fragment library design, fragment to lead optimisation and on the advancement in techniques used for screening. Finally, we will comment on the future opportunities and challenges in this field.

Fragment-Based Optimized EthR Inhibitors with in Vivo Ethionamide Boosting Activity

Killing more than one million people each year, tuberculosis remains the leading cause of death from a single infectious agent. The growing threat of multidrug-resistant strains of Mycobacterium tuberculosis stresses the need for alternative therapies. EthR, a mycobacterial transcriptional regulator, is involved in the control of the bioactivation of the second-line drug ethionamide. We have previously reported the discovery of in vitro nanomolar boosters of ethionamide through fragment-based approaches. In this study, we have further explored the structure-activity and structure-property relationships in this chemical family. By combining structure-based drug design and in vitro evaluation of the compounds, we identified a new oxadiazole compound as the first fragment-based ethionamide booster which proved to be active in vivo, in an acute model of tuberculosis infection.

Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots)

BACKGROUND

Fragment-based ligand design is used for the development of novel ligands that target macromolecules, most notably proteins. Central to its success is the identification of fragment binding sites that are spatially adjacent such that fragments occupying those sites may be linked to create drug-like ligands. Current experimental and computational approaches that address this problem typically identify only a limited number of sites as well as use a limited number of fragment types.

METHODS

The site-identification by ligand competitive saturation (SILCS) approach is extended to the identification of fragment bindings sites, with the method termed SILCS-Hotspots. The approach involves precomputation of the SILCS FragMaps following which the identification of Hotspots, performed by identifying of all possible fragment binding sites on the full 3D structure of the protein followed by spatial clustering.

RESULTS

The SILCS-Hotspots approach identifies a large number of sites on the target protein, including many sites not accessible in experimental structures due to low binding affinities and binding sites on the protein interior. The identified sites are shown to recapitulate the location of known drug-like molecules in both allosteric and orthosteric binding sites on seven proteins including the androgen receptor, the CDK2 and Erk5 kinases, PTP1B phosphatase and three GPCRs; the β2-adrenergic, GPR40 fatty-acid binding and M2-muscarinic receptors. Analysis indicates the importance of considering all possible fragment binding sites, and not just those accessible to experimental methods, when identifying novel binding sites and performing ligand design versus just considering the most favorable sites. The approach is shown to identify a larger number of known binding sites of drug-like molecules versus the commonly used FTMap and Fpocket methods.

GENERAL SIGNIFICANCE

The present results indicate the potential utility of the SILCS-Hotspots approach for fragment-based rational design of ligands, including allosteric modulators.

Design and synthesis of diphenylpyrimidine derivatives (DPPYs) as potential dual EGFR T790M and FAK inhibitors against a diverse range of cancer cell lines

A new class of pyrimidine derivatives were designed and synthesized as potential dual FAK and EGFR^T790M inhibitors using a fragment-based drug design strategy. This effort led to the identification of the two most active inhibitors, namely 9a and 9f, against both FAK (IC₅₀ = 1.03 and 3.05 nM, respectively) and EGFR^T790M (IC₅₀ = 3.89 and 7.13 nM, respectively) kinase activity. Moreover, most of these compounds also exhibited strong antiproliferative activity against the three evaluated FAK-overexpressing pancreatic cancer (PC) cells (AsPC-1, BxPC-3, Panc-1) and two drug-resistant cancer cell lines (breast cancer MCF-7/adr cells and lung cancer H1975 cells) at concentrations lower than 6.936 μM. In addition, 9a was also effective in the in vivo assessment conducted in a FAK-driven human AsPC-1 cell xenograft mouse model. Overall, this study offers a new insight into the treatment of hard to treat cancers.

Design, synthesis and biological evaluation of 2-hydrazinyladenosine derivatives as A2A adenosine receptor ligands

To obtain potential A_2A adenosine receptor agonists, a series of 2-hydrazinyladenosine derivatives were synthesized and assayed for adenosine receptors activity using radioligand binding activity assays. The binding activity of the subtypes was examined, and the structure-activity relationship of this class of compounds at the A_2A receptor was investigated. A fragment-based computer-aided design method was used to modify the 2-position side chain structures with different structural fragments, and the newly generated molecules were docked to the A_2A receptor to assess scoring and screening activity. To synthesize compounds with better scoring activity, the newly synthesized compounds were tested for in vitro receptor binding activity. 2-Hydrazinyladenosine derivatives of 32 new structural types were designed and synthesized, with the most potent adenosine derivative 23 exhibiting a K_i value of 1.8 nM for A_2AAR and significant selectivity for the A_2A receptor compared to the A₁ receptor. In addition to, compound 23, 24, 30, 31, and 42 also exhibited potent A_2A receptor selectivity, with Ki values for the A_2A receptor of 6.4, 20, 67 and 6.3 nM, respectively. We also found that compound 35 has a high A₁ receptor selectivity, with a K_i value for the A₁ receptor of 4.5 nM. Further functional assays also demonstrated that these compounds have potent A_2A receptor agonist activity. The study shows the applicability of an in silico fragment-based molecular design for rational lead optimization in A_2AAR.

A fragment-based approach towards the discovery of N-substituted tropinones as inhibitors of Mycobacterium tuberculosis transcriptional regulator EthR2

Tuberculosis (TB) caused by the pathogen Mycobacterium tuberculosis, represents one of the most challenging threat to public health worldwide, and with the increasing resistance to approved TB drugs, it is needed to develop new strategies to address this issue. Ethionamide is one of the most widely used drugs for the treatment of multidrug-resistant TB. It is a prodrug that requires activation by mycobacterial monooxygenases to inhibit the enoyl-ACP reductase InhA, which is involved in mycolic acid biosynthesis. Very recently, we identified that inhibition of a transcriptional repressor, termed EthR2, derepresses a new bioactivation pathway that results in the boosting of ethionamide activation. Herein, we describe the identification of potent EthR2 inhibitors using fragment-based screening and structure-based optimization. A target-based screening of a fragment library using thermal shift assay followed by X-ray crystallography identified 5 hits. Rapid optimization of the tropinone chemical series led to compounds with improved in vitro potency.

In silico fragment-based drug design with SEED

We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 10³-10⁴ fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-10² fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.

Discovery of BAZ2A bromodomain ligands

The bromodomain adjacent to zinc finger domain protein 2A (BAZ2A) is implicated in aggressive prostate cancer. The BAZ2A bromodomain is a challenging target because of the shallow pocket of its natural ligand, the acetylated side chain of lysine. Here, we report the successful screening of a library of nearly 1500 small molecules by high-throughput docking and force field-based binding-energy evaluation. For seven of the 20 molecules selected in silico, evidence of binding to the BAZ2A bromodomain is provided by ligand-observed NMR spectroscopy. Two of these compounds show a favorable ligand efficiency of 0.42 kcal/mol per non-hydrogen atom in a competition-binding assay. The crystal structures of the BAZ2A bromodomain in complex with four fragment hits validate the predicted binding modes. The binding modes of compounds 1 and 3 are compatible with ligand growing for optimization of affinity for BAZ2A and selectivity against the close homologue BAZ2B.

Fragment-Based Design, Synthesis, and Biological Evaluation of 1-Substituted-indole-2-carboxylic Acids as Selective Mcl-1 Inhibitors

Based on a known selective Mcl-1 inhibitor, 6-chloro-3-(3-(4-chloro-3,5-dimethylphenoxy)propyl)-1H-indole-2-carboxylic acid, we applied a fragment-based approach to obtain new molecules that extended into the p1 pocket of the BH3 groove and then exhibited binding selectivity for the Mcl-1 over the Bcl-2 protein. After we deconstructed the 1H-indole-2-carboxylic acid from the parental molecule, a benzenesulfonyl was substituted at the 1-position to adopt a geometry preferred for accessing the p1 pocket according to the binding mode of the parental molecule identified by X-ray crystallography. A linear relationship between the free energy of ligand binding (ΔG) and the count of non-hydrogen heavy atoms (HAC) was maintained during the molecular growing to occupy the p1 pocket. Finally, we not only obtained compound 12 with a 7.5-fold selectivity to Mcl-1 (K_i  = 0.48 µM by fluorescence polarization) over Bcl-2 (K_i  = 3.6 µM), but also provided evidence that additional occupation of the p1 pocket is more favorable for Mcl-1 than for Bcl-2 binding, and contributes more to Mcl-1 inhibition than occupation of the p2 pocket. Compound 12 exhibited a selective killing ability on Mcl-1-dependent cancer cells.

Developments in SPR Fragment Screening

INTRODUCTION

Fragment-based approaches have played an increasing role alongside high-throughput screening in drug discovery for 15 years. The label-free biosensor technology based on surface plasmon resonance (SPR) is now sensitive and informative enough to serve during primary screens and validation steps.

AREAS COVERED

In this review, the authors discuss the role of SPR in fragment screening. After a brief description of the underlying principles of the technique and main device developments, they evaluate the advantages and adaptations of SPR for fragment-based drug discovery. SPR can also be applied to challenging targets such as membrane receptors and enzymes.

EXPERT OPINION

The high-level of immobilization of the protein target and its stability are key points for a relevant screening that can be optimized using oriented immobilized proteins and regenerable sensors. Furthermore, to decrease the rate of false negatives, a selectivity test may be performed in parallel on the main target bearing the binding site mutated or blocked with a low-off-rate ligand. Fragment-based drug design, integrated in a rational workflow led by SPR, will thus have a predominant role for the next wave of drug discovery which could be greatly enhanced by new improvements in SPR devices.

Efficient exploration of chemical space by fragment-based screening

Screening methods seek to sample a vast chemical space in order to identify starting points for further chemical optimisation. Fragment based drug discovery exploits the superior sampling of chemical space that can be achieved when the molecular weight is restricted. Here we show that commercially available fragment space is still relatively poorly sampled and argue for highly sensitive screening methods to allow the detection of smaller fragments. We analyse the properties of our fragment library versus the properties of X-ray hits derived from the library. We particularly consider properties related to the degree of planarity of the fragments.

Structure-based drug design for G protein-coupled receptors

Our understanding of the structural biology of G protein-coupled receptors has undergone a transformation over the past 5 years. New protein-ligand complexes are described almost monthly in high profile journals. Appreciation of how small molecules and natural ligands bind to their receptors has the potential to impact enormously how medicinal chemists approach this major class of receptor targets. An outline of the key topics in this field and some recent examples of structure- and fragment-based drug design are described. A table is presented with example views of each G protein-coupled receptor for which there is a published X-ray structure, including interactions with small molecule antagonists, partial and full agonists. The possible implications of these new data for drug design are discussed.

Fragment-based approaches to the discovery of kinase inhibitors

Protein kinases are one of the most important families of drug targets, and aberrant kinase activity has been linked to a large number of disease areas. Although eminently targetable using small molecules, kinases present a number of challenges as drug targets, not least obtaining selectivity across such a large and relatively closely related target family. Fragment-based drug discovery involves screening simple, low-molecular weight compounds to generate initial hits against a target. These hits are then optimized to more potent compounds via medicinal chemistry, usually facilitated by structural biology. Here, we will present a number of recent examples of fragment-based approaches to the discovery of kinase inhibitors, detailing the construction of fragment-screening libraries, the identification and validation of fragment hits, and their optimization into potent and selective lead compounds. The advantages of fragment-based methodologies will be discussed, along with some of the challenges associated with using this route. Finally, we will present a number of key lessons derived both from our own experience running fragment screens against kinases and from a large number of published studies.

Fragment-based approach to the design of 5-chlorouracil-linked-pyrazolo[1,5-a][1,3,5]triazines as thymidine phosphorylase inhibitors

5-Chlorouracil-linked-pyrazolo[1,5-a][1,3,5]triazines were designed as new thymidine phosphorylase inhibitors based on the fragment based drug design approach. Multiple-step convergent synthetic schemes were devised to generate the target compounds. The intermediate 5-chloro-6-chloromethyluracil was synthesized by a 4-step reaction. A series of the second bicyclic intermediates, namely pyrazolo[1,5-a][1,3,5]triazin-2-thioxo-4-one, was obtained from various substituted 3-aminopyrazoles. These two intermediates were coupled finally in the presence of sodium ethoxide and methanol to yield the desirable target compounds. The methylthio coupling spacer was found to be suitable in enabling the interaction of the two fragments at the active site and allosteric site of the enzyme. The best coupled compound (9q) inhibited the thymidine phosphorylase with an IC₅₀ value as low as 0.36 ± 0.1 μM. In addition, 9q demonstrated a mixed-type of enzyme inhibition kinetics, thus suggesting that it might indeed potentially bind at two different sites on the enzyme.