Towards New Anti-inflammatory Agents: Design, Synthesis and Evaluation of Molecules Targeting XIAP-BIR2.

The X-chromosome-linked inhibitor of apoptosis protein (XIAP) plays a crucial role in controlling cell survival across multiple regulated cell death pathways and coordinating a range of inflammatory signalling events. The discovery of selective inhibitors for XIAP-BIR2, able to disrupt the direct physical interaction between XIAP and RIPK2, offer promising therapeutic options for NOD2-mediated diseases like Crohn's disease, sarcoidosis, and Blau syndrome. The objective of this study was to design, synthesize, and evaluate small synthetic molecules with binding selectivity to XIAP-BIR2 domain. To achieve this, we applied an interdisciplinary drug design approach and firstly we have synthesized an initial fragment library to achieve a first XIAP inhibition activity. Then using a growing strategy, larger compounds were synthesized and one of them presents a good selectivity for XIAP-BIR2 versus XIAP-BIR3 domain, compound 20c. The ability of compound 20c to block the NOD1/2 pathway was confirmed in cell models. These data show that we have synthesized molecules capable of blocking NOD1/2 signalling pathways in cellulo, and ultimately leading to new anti-inflammatory compounds.

Fragment-based virtual screening identifies novel leads against Plasmepsin IX (PlmIX) of Plasmodium falciparum: Homology modeling, molecular docking, and simulation approaches.

Despite continuous efforts to develop safer and efficient medications, malaria remains a major threat posing great challenges for new drug discovery. The emerging drug resistance, increased toxicities, and impoverished pharmacokinetic profiles exhibited by conventional drugs have hindered the search for new entities. Plasmepsins, a group of Plasmodium-specific, aspartic acid protease enzymes, are involved in many key aspects of parasite biology, and this makes them interesting targets for antimalarial chemotherapy. Among different isoforms, PlmIX serves as an unexplored antimalarial drug target that plays a crucial role along with PlmV and X in the parasite's survival by digesting hemoglobin in the host's erythrocytes. In this study, fragment-based virtual screening was performed by modeling the three-dimensional structure of PlmIX and predicting its ligand-binding pocket by using the Sitemap tool. Screening identified the fragments with the XP docking score ≤ -3 kcal/mol from the OTAVA General Fragment Library (≈16,397 fragments), and the selected fragments were chosen for ligand breeding. The resulting ligands (≈69,858 ligands) were subsequently subjected to filtering based on the QikProp properties along with carcinogenicity testing performed using CarcinoPred-EL and then docked in the SP (≈14,078 ligands) as well as XP mode (≈3,104 ligands), and compared with that of control ligands 49C and I0L. The top-ranked ligands were taken further for the calculation of the free energy of binding using Prime MM-GBSA. Overall, a total of six complexes were taken further for MD simulation studies performed at 100 ns to attain a better understanding of the binding mechanisms, and compounds 3 and 4 were found to be the most efficient ones in silico. The analysis of compound 3 revealed that the carbonyl group present in position 1 on the isoindoline moiety (Arg554) was responsible for inhibitory activity against PlmIX. However, the analysis of compound 4 revealed that the amide linkage sandwiched between the phenyl ring and isoquinoline moiety (Lys555 and Ser226) as well as carbonyl oxygen of the carbamoyl group present at position 2 of the pyrazole ring (Gln222) were responsible for PlmIX inhibitory activity, owing to their crucial interactions with key amino acid residues.

Design, synthesis and evaluation of a pyrazolo[3,4-d]pyrimidine derivative as a novel and potent TGFß1R1 inhibitor.

The transforming growth factor ß1 (TGFß1)/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-ß1/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.

Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.

Introduction: The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.

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/ß-catenin pathway and is considered an important target for the treatment of colorectal cancer. Inhibiting TNIK has potential to block abnormal Wnt/ß-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.

Tubulin inhibitors. Selected scaffolds and main trends in the design of novel anticancer and antiparasitic agents.

Design of tubulin inhibitors as anticancer drugs dynamically developed over the past 20 years. The modern arsenal of potential tubulin-targeting anticancer agents is represented by small molecules, monoclonal antibodies, and antibody-drug conjugates. Moreover, targeting tubulin has been a successful strategy in the development of antiparasitic drugs. In the present review, an overall picture of the research and development of potential tubulin-targeting agents using small molecules between 2018 and 2023 is provided. The data about some most often used and prospective chemotypes of small molecules (privileged heterocycles, moieties of natural molecules) and synthetic methodologies (analogue-based, fragment-based drug design, molecular hybridization) applied for the design of novel agents with an impact on the tubulin system are summarized. The design and prospects of multi-target agents with an impact on the tubulin system were also highlighted. Reported in the review data contribute to the "structure-activity" profile of tubulin-targeting small molecules as anticancer and antiparasitic agents and will be useful for the application by medicinal chemists in further exploration, design, improvement, and optimization of this class of molecules.

Computational fragment-based drug design of potential Glo-I inhibitors.

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0-18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.

Fragment databases from screened ligands for drug discovery (FDSL-DD).

Fragment-based drug design (FBDD) is one major drug discovery method employed in computer-aided drug discovery. Due to its inherent limitations, this process experiences long processing times and limited success rates. Here we present a new Fragment Databases from Screened Ligands Drug Design method (FDSL-DD) that intelligently incorporates information about fragment characteristics into a fragment-based design approach to the drug development process. The initial step of the FDSL-DD is the creation of a fragment database from a library of docked, drug-like ligands for a specific target, which deviates from the traditional in silico FBDD strategy, incorporating structure-based design screening techniques to combine the advantages of both approaches. Three different protein targets have been tested in this study to demonstrate the potential of the created fragment library and FDSL-DD. Utilizing the FDSL-DD led to an increase in binding affinity for each protein target. The most substantial increase was exhibited by the ligand designed for TIPE2, with a 3.6 kcalmol-1 difference between the top ligand from the FDSL-DD and top ligand from the high throughput virtual screening (HTVS). Using drug-like ligands in the initial HTVS allows for a greater search of chemical space, with higher efficiency in fragments selection, less grid boxes, and potentially identifying more interactions.

Reflections on improving drug success rates with AIDD and CADD / 中国药科大学学报

@#Abstract: The rapid advancements in artificial intelligence (AI) and computational sciences, particularly through the introduction of artificial intelligence drug design (AIDD) and computer-aided drug design (CADD) technologies, have revolutionized pathways in drug development. These include techniques such as natural language processing, image recognition, deep learning, and machine learning. By employing advanced algorithms and data processing techniques, these technologies have significantly enhanced the efficiency and success rate of R&D processes. In drug discovery, AI technologies have accelerated the identification of drug targets, screening of candidate drugs, pharmacological assessments, and quality control, effectively reducing R&D risks and costs. This article delves into the application of AIDD and CADD in drug development, analyzing their roles in enhancing the success rates and efficiencies of drug design, exploring their future trends, and addressing the potential challenges.

Designing of fragment based inhibitors with improved activity against E. coli AmpC ß-lactamase compared to the conventional antibiotics.

One of the most common primary resistance mechanism of multi-drug resistant (MDR) Gram negative pathogenic bacteria to combat ß-lactam antibiotics, such as penicillins, cephalosporins and carbapenems is the generation of ß- lactamases. The uropathogenic E. coli is mostly getting multi-drug resistance due to the synthesis of AmpC ß-lactamases and therefore new antibiotics and inhibitors are needed to treat the evolving infections. The current study was designed for targetting AmpC ß-lactamase of E. coli using molecular docking based virtual screening, linking fragments for designing novel compounds and binding mode analysis using molecular dynamic simulation with target protein. The FCH group all-purpose fragment library consisting of 9388 fragments has been screened against AmpC ß-lactamase protein of E. coli and the antibiotics and anti-infectives used in treatment of Urinary tract Infections (UTIs) were also screened with AmpC ß-lactamase protein. Among the 9388 fragments, 339 fragment candidates were selected and linked with cefepime antibiotic having maximum binding affinity for AmpC target protein. Computational analysis of interactions as well as molecular dynamics (MD) simulations were also conducted for identifying the most promising ligand-pocket complexes from docking investigations to comprehend their thermodynamic properties and verify the docking outcomes as well. Overall, the linked complexes (LCs) showed good binding interactions with AmpC ß-lactamase. Interestingly, our fragment-based LCs remained relatively stable in comparison with cefepime antibiotic. Moreover, S12 fragment linked complex remained the most stable during 50 ns with remarkable number of interactions indicating it as promising candidate in novel lead discovery against MDR E. coli infections.

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.

Strategies in the Design and Development of Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs).

AIDS (acquired immunodeficiency syndrome) is a potentially life-threatening infectious disease caused by human immunodeficiency virus (HIV). To date, thousands of people have lost their lives annually due to HIV infection, and it continues to be a big public health issue globally. Since the discovery of the first drug, Zidovudine (AZT), a nucleoside reverse transcriptase inhibitor (NRTI), to date, 30 drugs have been approved by the FDA, primarily targeting reverse transcriptase, integrase, and/or protease enzymes. The majority of these drugs target the catalytic and allosteric sites of the HIV enzyme reverse transcriptase. Compared to the NRTI family of drugs, the diverse chemical class of non-nucleoside reverse transcriptase inhibitors (NNRTIs) has special anti-HIV activity with high specificity and low toxicity. However, current clinical usage of NRTI and NNRTI drugs has limited therapeutic value due to their adverse drug reactions and the emergence of multidrug-resistant (MDR) strains. To overcome drug resistance and efficacy issues, combination therapy is widely prescribed for HIV patients. Combination antiretroviral therapy (cART) includes more than one antiretroviral agent targeting two or more enzymes in the life cycle of the virus. Medicinal chemistry researchers apply different optimization strategies including structure- and fragment-based drug design, prodrug approach, scaffold hopping, molecular/fragment hybridization, bioisosterism, high-throughput screening, covalent-binding, targeting highly hydrophobic channel, targeting dual site, and multi-target-directed ligand to identify and develop novel NNRTIs with high antiviral activity against wild-type (WT) and mutant strains. The formulation experts design various delivery systems with single or combination therapies and long-acting regimens of NNRTIs to improve pharmacokinetic profiles and provide sustained therapeutic effects.

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.

Fragment-based structural exploration and chemico-biological interaction study of HDAC3 inhibitors through non-linear pattern recognition, chemical space, and binding mode of interaction analysis.

HDAC3 is an emerging target for the identification and discovery of novel drug candidates against several disease conditions including cancer. Here, a fragment-based non-linear machine learning (ML) method along with chemical space exploration followed by a structure-based binding mode of interaction analysis study was carried out on some HDAC3 inhibitors to obtain the key structural features modulating HDAC3 inhibition. Both the ML and chemical space analysis identified several physicochemical and structural properties namely lipophilicity, polar and relative polar surface area, arylcarboxamide moiety, bulky fused aromatic group, n-alkyl, and cinnamoyl moieties, the higher number of oxygen atoms, π-electrons for the substituted tetrahydrofuronaphthodioxolone moiety favorable for higher HDAC3 inhibition. Moreover, hydrogen bond forming capabilities, the length and substitution position of the linker moiety, the importance of phenyl ring in the linker motif, the contribution of heterocyclic cap moieties for effective inhibitor binding at the HDAC3 catalytic site that correspondingly affects the HDAC3 inhibitory potency. Again, macrocyclic ring structure and cyclohexyl cap moiety are responsible for lower HDAC3 inhibition. The MD simulation study of selected compounds explained strong binding patterns at the HDAC3 active site as evidenced by the lower RMSD and RMSF values. Nevertheless, it also explained the importance of the crucial structural fragments derived from the fragment-based analysis during ligand-enzyme interactions. Therefore, the outcomes of this current structural analysis will be a useful tool for fragment-based drug discovery of effective HDAC3 inhibitors for clinical therapeutics in the future.Communicated by Ramaswamy H. Sarma.

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.

Privileged fragment-based design, synthesis and in vitro antitumor activity of imatinib analogues.

Based on the privileged fragment-based drug design strategy, a series of imatinib analogues bearing the moiety of 3-(2-amino-2-oxoacetyl)-1H-indole were designed and synthesized, and the in vitro antitumor activity of these compounds was detected by MTT method using K562 (human myeloid leukemia) and K562R (imatinib-resistant chronic myeloid leukemia) cell lines. Molecular docking was used to preliminarily explain the possible binding modes. The most potent compound I2 exhibited better antitumor activity than those of imatinib against K562 and K562R cancer cells with IC50 values of 0.8 µM and 0.7 µM.

Discovery of novel potent drugs for influenza by inhibiting the vital function of neuraminidase via fragment-based drug design (FBDD) and molecular dynamics simulation strategies.

The current work describes a fragment linking methodology to generate new neuraminidase inhibitors. A total number of 28,977 fragments from Zinc 20 have been obtained and screened for neuraminidase receptor affinity. Using Schrödinger software, the highest-scoring 270 fragment hits (with scores greater than -7.6) were subjected to fragment combining to create 100 new molecules. These 100 novel compounds were studied using XP docking to evaluate the molecular interaction modes and their binding affinity to neuraminidase receptor. The top ten molecules were selected, for ADMET, drug-likeness features. Based on these characteristics, the best four developed molecules and Zanamivir were submitted to a molecular dynamics simulation investigation to estimate their dynamics within the neuraminidase receptor using Gromacs software. All MD simulation findings show that the generated complexes are very stable when compared to the clinical inhibitor (Zanamivir). In addition, the four designed neuraminidase inhibitors formed very stable complexes with neuraminidase receptor (with total binding energies ranging from -83.50 to -107.85 Kj/mol) according to the total binding energy calculated by MM-PBSA. For the objective of developing new influenza medications, these novel molecules have the potential to be further evaluated in vitro and in vivo for influenza drug discovery.Communicated by Ramaswamy H. Sarma.

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.