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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)., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

CaMKK2 (calcium/calmodulin dependent protein kinase kinase 2) is a serine/threonine protein kinase that regulates phosphorylation of CaM kinases (CaMKs) such as CaMKI, CaMKIV, and AMP-activated protein kinase (AMPK). From a pathological perspective, CaMKK2 plays a role in obesity, diabetes, and prostate cancer. Therefore, CaMKK2 is an attractive target protein for drug design. Here, we tried to find new CaMKK2 inhibitors by using ligand-based and structure-based drug design approaches. From the in silico hit compounds, we identified new inhibitors by using a CaMKK2 kinase assay. We solved X-ray crystallography structures of the CaMKK2-inhibitor complexes and performed Fragment Molecular Orbital (FMO) calculations to analyze the protein-ligand interactions, identify the key residues in inhibitor binding, and quantitatively measure their contribution. We experimentally determined five CaMKK2-inhibitor structures and calculated the binding energies of the inhibitors by the FMO method plus MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) approach. The results showed a high correlation (R = -0.89) between experimentally measured inhibitory activity (pIC 50 ) and the predicted ligand binding energy. We then quantitatively evaluated the contribution of each binding site residue in CaMKK2 by the IFIE (Inter-fragment Interaction Energy)/PIEDA (Pair Interaction Energy Decomposition Analysis) method. The IFIE values indicated that Lys194 and Glu236, which formed hydrogen bonds with the carboxylate groups of the inhibitors, were key residues for ligand binding. PIEDA revealed that the dispersion interaction of inhibitors with hydrophobic residues, such as Ile171, Phe267, and Leu319, contributed highly to ligand binding; we considered that this was due to CH-π interactions with methoxy groups and/or aromatic rings contained in our CaMKK2 inhibitor. These results from the quantitative interaction analysis by the FMO method are useful not only for future CaMMK2 inhibitor development but for application of the FMO method to in silico drug design., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2020. Published by Elsevier Inc.)

Introduction: Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors., Objective: The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field., Methods: As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis., Results: In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome., Conclusion: In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Ung-type uracil-DNA glycosylases are frontline defenders of DNA sequence fidelity in bacteria, plants and animals; Ungs also directly assist both innate and humoral immunity. Critically important in viral pathogenesis, whether acting for or against viral DNA persistence, Ungs also have therapeutic relevance to cancer, microbial and parasitic diseases. Ung catalytic specificity is uniquely conserved, yet selective antiviral drugging of the Ung catalytic pocket is tractable. However, more promising precision therapy approaches present themselves via insights from viral strategies, including sequestration or adaptation of Ung for noncanonical roles. A universal Ung inhibition mechanism, converged upon by unrelated viruses, could also inform design of compounds to inhibit specific distinct Ungs. Extrapolating current developments, the character of such novel chemical entities is proposed.

Over the past ten years, the number of three-dimensional protein structures identified by advanced science and technology increases, and the gene information becomes more available than ever before as well. The development of computing science becomes another driving force which makes it possible to use computational methods effectively in various phases of the drug design and research. Now Structure-Based Drug Design (SBDD) tools are widely used to help researchers to predict the position of small molecules within a three-dimensional representation of the protein structure and estimate the affinity of ligands to target protein with considerable accuracy and efficiency. They also accelerate discovery speed of potent drug and reduce the cost and times for drug research. Here we present an overview of SBDD used in drug discovery and highlight its recent successes and major challenges to current SBDD methodologies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity. It is essential for an efficient structure-based drug design (SBDD) process. FMO enables ab initio approaches to be applied to systems that conventional quantum-mechanical (QM) methods would find challenging. The key advantage of the Fragment Molecular Orbital Method (FMO) is that it can reveal atomistic details about the individual contributions and chemical nature of each residue and water molecule toward ligand binding which would otherwise be difficult to detect without using QM methods. In this chapter, we demonstrate the typical use of FMO to analyze 19 crystal structures of β1 and β2 adrenergic receptors with their corresponding agonists and antagonists.

Background: Peroxisome proliferator-activated receptors (PPAR) are nuclear receptors activated by endogenous fatty acids and prostaglandins that are classified into three types: α, γ and δ, which have different functions and tissue distribution. PPAR modulators have been exploited to the treatment of important metabolic diseases, such as type 2 diabetes mellitus and metabolic syndrome, which are considered relevant epidemic diseases currently. Along the last decades, several studies have reported structural differences between the three PPAR subtypes associated with the discovery of selective ligands, dual and pan-agonists. Nowadays, there are several approved drugs that activate PPARα (fibrates) and PPARγ (glitazones), but up to now there is none clinically used drug targeting PPARδ. Additionally, several side-effects associated with the use of PPARα and γ agonists are reported by regulatory agencies, which do not indicate anymore their use as first-line drugs., Objective: A significant new market has grown in the last years, focusing on the development of new PPARδ agonists as drug candidates to treat metabolic diseases and, in this sense, this study proposes to review the structural requirements to achieve selective PPARδ activation, as well to discuss the most relevant agonists in clinical trials, providing information on the current phase in the drug discovery and design targeting PPARδ., Conclusion: Several PPARδ ligands with high potency were reported in the literature and were designed or discovered by a combination of experimental and computational approaches. Furthermore, the reported importance of pockets and individual residues at PPARδ binding site as well as the importance of substituent and some physicochemical properties that could help to design of new classes of agonists., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background: Neurodegenerative disorders (NDs) are diverse group of disorders characterized by escalating loss of neurons (structural and functional). The development of potential therapeutics for NDs presents an important challenge, as traditional treatments are inefficient and usually are unable to stop or retard the process of neurodegeneration. Computer-Aided Drug Design (CADD) has emerged as an efficient means of developing candidate drugs for the treatment of many disease types. Applications of CADD approach to drug discovery are progressing day by day. The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity., Methods: A wide literature search was performed for writing this review. An updated view on different types of NDs, their effect on human population and a brief introduction to CADD, various approaches involved in this technique, ranging from structural-based to ligand-based drug design has been discussed. The successful application of CADD approaches for the treatment of neurodegenerative disorders is also included in this review., Results: In this review, we have briefly described about CADD and its use in the development of the therapeutic drug candidates against NDs. The successful applications, limitations and future prospects of this approach have also been discussed., Conclusion: CADD can assist researchers studying interactions between drugs and receptors. We believe this review will be helpful for better understanding of CADD and its applications towards the discovery of new drug candidates against various fatal NDs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

The ongoing explosion in genomics data has long since outpaced the capacity of conventional biochemical methodology to verify the large number of hypotheses that emerge from the analysis of such data. In contrast, it is still a gold-standard for early phenotypic validation towards small-molecule drug discovery to use probe molecules (or tool compounds), notwithstanding the difficulty and cost of generating them. Rational structure-based approaches to ligand discovery have long promised the efficiencies needed to close this divergence; in practice, however, this promise remains largely unfulfilled, for a host of well-rehearsed reasons and despite the huge technical advances spearheaded by the structural genomics initiatives of the noughties. Therefore the current, fourth funding phase of the Structural Genomics Consortium (SGC), building on its extensive experience in structural biology of novel targets and design of protein inhibitors, seeks to redefine what it means to do structural biology for drug discovery. We developed the concept of a Target Enabling Package (TEP) that provides, through reagents, assays and data, the missing link between genetic disease linkage and the development of usefully potent compounds. There are multiple prongs to the ambition: rigorously assessing targets' genetic disease linkages through crowdsourcing to a network of collaborating experts; establishing a systematic approach to generate the protocols and data that comprise each target's TEP; developing new, X-ray-based fragment technologies for generating high quality chemical matter quickly and cheaply; and exploiting a stringently open access model to build multidisciplinary partnerships throughout academia and industry. By learning how to scale these approaches, the SGC aims to make structures finally serve genomics, as originally intended, and demonstrate how 3D structures systematically allow new modes of druggability to be discovered for whole classes of targets., (© 2017 The Author(s).)

In this work, iminothiazolidin-4-one derivatives were explored as selective GSK-3β inhibitors. Molecular docking analysis was carried to design a series of compounds, which were synthesized using substituted thiourea, 2-bromoacetophenones and benzaldehydes. Out of the twenty five compounds synthesized during this work, the in vitro evaluation against GSK-3 led to the identification of nine compounds with activity in lower nano-molar range (2-85 nM). Further, in vitro evaluation against CDK-2 showed five compounds to be selective towards GSK-3., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

Using SBDD, a series of 4-amino-7-azaindoles were discovered as a novel class of Alk5 inhibitors that are potent in both Alk5 enzymatic and cellular assays. Subsequently a ring cyclization strategy was utilized to improve ADME properties leading to the discovery of a series of 1H-imidazo[4,5-c]pyridin-2(3H)-one drug like Alk5 inhibitors., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Introduction: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds., Areas Covered: This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening., Expert Opinion: Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

The MAPK pathway is identified as one of the most important pathways involved in cell proliferation and differentiation. A key kinase in the pathway, the Mitogen-activated protein kinase kinase (MEK) is recognized as a promising target for antitumor drugs. Structure-based design and optimization of known MEK inhibitors resulted in identification of compound 10a as a potent non-ATP competitive MEK inhibitor in both in vitro and in vivo tests., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands' binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors. [ABSTRACT FROM AUTHOR]

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure–activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs. [ABSTRACT FROM AUTHOR]

A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 105 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 and 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~ 1.5-fold and sample-efficiency is improved ~ 45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. We find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Lastly, we show improved performance not only on recurrent neural networks but also on a reinforcement learning stabilized transformer architecture. Overall, we show that Augmented Hill-Climb improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale. [ABSTRACT FROM AUTHOR]

Background: In 2003, the first case of severe acute respiratory syndrome coronavirus (SARS-CoV) was recorded. Coronaviruses (CoVs) have caused a major outbreak of human fatal pneumonia. Currently, there is no specific drug or treatment for diseases caused by SARS CoV 2. Computational approach that adopts dynamic models is widely accepted as indispensable tool in drug design but yet to be exploited in covid-19 in Zaria, Nigeria. In this study, steps were taken to advance on the successful achievements in the field of covid-19 drug, with the aid of in silico drug design technique, to create novel inhibitor drug candidates with better activity. In this study, one thousand human immunodeficiency virus (HIV1) antiviral chemical compounds from www.bindingBD.org were docked on the SARS CoV 2 main protease protein data bank identification number 6XBH (PDB ID: 6XBH) and the molecular docking score were ranked in order to identify the compounds with the highest inhibitory effects, and easy selection for future studies. Results: The docking studies showed some interesting results. Inhibitors with Index numbers 331, 741, and 819 had the highest binding affinity. Similarly, inhibitors with Index number 441, 847, and 46 had the lowest hydrogen bond energy. Inhibitor with index number 331 was reported with the lowest value (− 48.38kCal/mol). Five new compounds were designed from the selected six (6) compounds with the best binding score giving a total of thirty (30) novel compounds. The low binding energy of inhibitor with index no. 847b is unique, as most of the interaction energies are of H-bond type with amino acids (Thr26, Gly143, Ser144, Cys145, Glu166, Gln189, Hie164, Met49, Thr26, Thr25, Thr190, Asn142, Met165) resulting in an overall negative value (−16.31 kCal/mol) making it the best of all the newly designed inhibitors. Conclusions: The novel inhibitor is 2-(2-(5-amino-2-((((3-aminobenzyl)oxy)carbonyl)amino)-5-oxopentanamido)-4-(2-(tert-butyl)-4-oxo-4-(pentan-3-ylamino) butanamido)-3-hydroxybutyl) benzoic acid. The improvement it has over the parent inhibitor is from the primary amine group attached to meta position of first benzene ring and the carboxyl group attached to the ortho position of the second benzene ring. The molecular dynamics studies also show that the novel inhibitor remains stable after the study. This result makes it a better drug candidate against SARS CoV 2 main protease when compared with the co-crystallized inhibitor or any of the 1000 docked inhibitors. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing interest in new modalities such as therapeutic antibodies and gene therapy at a number of pharmaceutical companies. Moreover, in small-molecule drug discovery at such companies, efforts have focused on hard-to-drug targets such as inhibiting protein–protein interactions. Biomolecular NMR spectroscopy has been used in drug discovery in a variety of ways, such as for the reliable detection of binding and providing three-dimensional structural information for structure-based drug design. The advantages of using NMR spectroscopy have been known for decades (Jahnke in J Biomol NMR 39:87–90, (2007); Gossert and Jahnke in Prog Nucl Magn Reson Spectrosc 97:82–125, (2016)). For tackling hard-to-drug targets and increasing the success in discovering drug molecules, in-depth analysis of drug–target protein interactions performed by biophysical methods will be more and more essential. Here, we review the advantages of NMR spectroscopy as a key technology of biophysical methods and also discuss issues such as using cutting-edge NMR spectrometers and increasing the demand of utilizing conformational dynamics information for promoting small-molecule drug discovery. [ABSTRACT FROM AUTHOR]

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

The SBDD was performed to modified the benzbromarone, and the compound 4 was obtained which exhibited superior uric acid-lowering activity. [Display omitted] With increased unhealthy dietary patterns and a sedentary lifestyle, the prevalence of hyperuricemia is growing rapidly, placing a tremendous burden on the public health system. Persistent hyperuricemia in extreme cases induces gout, gouty arthritis, and other metabolic diseases. Benzbromarone is a potent human urate transporter 1 (URAT1) inhibitor that is widely used as a uric acid-lowering drug. Recent studies indicated that benzbromarone can also activate farnesoid X receptor (FXR), whereas its agonistic activity on FXR is rather poor. Mounting evidence suggested that the etiology of gout is directly related to NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasomes, and FXR suppresses the expression of NLRP3 in various ways. Therefore, the dual URAT1 inhibitor and FXR agonist may exert synergistic effects on decreasing uric acid (UA) levels and inhibiting inflammation. To obtain a better dual URAT1 inhibitor and FXR agonist, we performed the structure-based drug design (SBDD) strategy to improve the FXR activation of benzbromarone by forming strong interactions with ARG331 in FXR binding pocket. All of these efforts lead to the identification of compound 4 , which exerts better activity on FXR and uric acid-lowering effect than benzbromarone. [ABSTRACT FROM AUTHOR]

A novel thienopyrimidinone analog was discovered as a potent and highly selective TAK1 inhibitor using the SBDD approach. TAK1 plays a key role in inflammatory and immune signaling, so TAK1 is considered to be an attractive molecular target for the treatment of human diseases (inflammatory disease, cancer, etc.). After the hit compound had been obtained, our modifications successfully increased TAK1 inhibitory activity and solubility, but metabolic stability was still unsatisfactory. To improve metabolic stability, we conducted metabolic identification. Although the obtained metabolite was fortunately a potent TAK1 inhibitor, its kinase selectivity was low. Subsequently, to achieve high kinase selectivity, we used SBDD to follow two strategies: one targeting unique amino acid residues in TAK1, especially the combination of Ser111 and Asn114; the other decreasing the interaction with Tyr106 at the hinge position in TAK1. As expected, our designed compound showed an excellent kinase selectivity profile in both an in-house and a commercially available panel assay of over 420 kinases and also retained its potent TAK1 inhibitory activity (TAK1 IC 50 = 11 nM). [ABSTRACT FROM AUTHOR]

Utilizing structure-based drug design techniques, we designed and synthesized phosphodiesterase 10A (PDE10A) inhibitors based on pyridazin-4(1 H )-one. These compounds can interact with Tyr683 in the PDE10A selectivity pocket. Pyridazin-4(1 H )-one derivative 1 was linked with a benzimidazole group through an alkyl spacer to interact with the OH of Tyr683 and fill the PDE10A selectivity pocket. After optimizing the linker length, we identified 1-(cyclopropylmethyl)-5-[3-(1-methyl-1 H -benzimidazol-2-yl)propoxy]-3-(1-phenyl-1 H -pyrazol-5-yl)pyridazin-4(1 H )-one ( 16f ) as having highly potent PDE10A inhibitory activity (IC 50 = 0.76 nM) and perfect selectivity against other PDEs (>13,000-fold, IC 50 = >10,000 nM). The crystal structure of 16f bound to PDE10A revealed that the benzimidazole moiety was located deep within the PDE10A selectivity pocket and interacted with Tyr683. Additionally, a bidentate interaction existed between the 5-alkoxypyridazin-4(1 H )-one moiety and the conserved Gln716 present in all PDEs. [ABSTRACT FROM AUTHOR]

Methionine aminopeptidase-2 (MetAP2) is an enzyme that cleaves an N-terminal methionine residue from a number of newly synthesized proteins. This step is required before they will fold or function correctly. Pre-clinical and clinical studies with a MetAP2 inhibitor suggest that they could be used as a novel treatment for obesity. Herein we describe the discovery of a series of pyrazolo[4,3-b]indoles as reversible MetAP2 inhibitors. A fragment-based drug discovery (FBDD) approach was used, beginning with the screening of fragment libraries to generate hits with high ligand-efficiency (LE). An indazole core was selected for further elaboration, guided by structural information. SAR from the indazole series led to the design of a pyrazolo[4,3-b]indole core and accelerated knowledge-based fragment growth resulted in potent and efficient MetAP2 inhibitors, which have shown robust and sustainable body weight loss in DIO mice when dosed orally. [ABSTRACT FROM AUTHOR]

[Display omitted] • Matrix metalloproteinase-2 has been a target of choice for the drug development. • Different NMR and X-ray crystallographic structures reported for MMP-2 are analysed in details. • An exquisite picture of the structure-based drug design (SBDD) approaches of MMP-2 inhibitors is provided. • This study delineates plausible strategies for designing specific MMP-2 inhibitor. The protease enzyme, matrix metalloproteinase-2 (MMP-2) has been a target of choice for the drug development due to its multi-façade involvement in numerous diseased conditions including cancer. To find a selective MMP-2 inhibitor several computational strategies are employed in its design and discovery. In these strategies, protein structure of MMP-2 is an inevitable part to formulate effective structure-based drug design (SBDD) of selective MMP-2 inhibitors. In the present communication, several crystal structures of MMP-2 have been analyzed with different statistical parameters and their implementations in SBDD of inhibitors are scrutinized. In addition, binding mode analyses of various classes of inhibitors are discussed to pinpoint the effective design of selective inhibitors by maximizing its interaction with the MMP-2 enzyme binding site. This may provide a crucial insight for exploring the numerous possibilities for SBDD of MMP-2 inhibitors to accelerate anticancer drug discovery efforts. [ABSTRACT FROM AUTHOR]

Abstract: In order to develop potent and selective focal adhesion kinase (FAK) inhibitors, synthetic studies on pyrazolo[4,3-c][2,1]benzothiazines targeted for the FAK allosteric site were carried out. Based on the X-ray structural analysis of the co-crystal of the lead compound, 8-(4-ethylphenyl)-5-methyl-1,5-dihydropyrazolo[4,3-c][2,1]benzothiazine 4,4-dioxide 1 with FAK, we designed and prepared 1,5-dimethyl-1,5-dihydropyrazolo[4,3-c][2,1]benzothiazin derivatives which selectively inhibited kinase activity of FAK without affecting seven other kinases. The optimized compound, N-(4-tert-butylbenzyl)-1,5-dimethyl-1,5-dihydropyrazolo[4,3-c][2,1]benzothiazin-8-amine 4,4-dioxide 30 possessed significant FAK kinase inhibitory activities both in cell-free (IC50 =0.64μM) and in cellular assays (IC50 =7.1μM). These results clearly demonstrated a potential of FAK allosteric inhibitors as antitumor agents. [Copyright &y& Elsevier]

Abstract: A novel 5-phenylamino-8-methylpyrido[2,3-d]pyrimidine-4,7(3H,8H)-dione series of MEK inhibitors has been developed using structure-based drug design. Lead optimization of this series led to the discovery of TAK-733. This was advanced to Phase I clinical studies for cancer treatment. [Copyright &y& Elsevier]

Abstract: Structure based drug design (SBDD) was used to discover heat shock protein 90 (HSP90) inhibitors useful in the treatment of cancer. By using the crystal structure of HSP90–ligand complex (1uyi), a docking model was prepared and was validated by external dataset containing known HSP90 inhibitors. This validated model was then used to virtually screen commercial databases, selected hits of which were bought and sent for real biological evaluation. Further as an alternative method, pharmacophores were generated using crystal structure conformations of ligands in HSP90 complexes (1uyi and 2bz5) and where used for virtual screening. Both cases yielded several hits containing novel scaffolds, particularly compound KHSP8 showed an IC50 value of 0.902μM in case of colon cancer (HT29), which is comparable to doxorubicin (0.828μM). These compounds were being now used as leads for constructing small molecular libraries to get compounds with favourable pharmacokinetics and drug like properties. [Copyright &y& Elsevier]

Abstract: Starting from lead compound 1, we demonstrate how X-ray structural data can be used to understand SAR and expediently optimize bioavailability in a novel series of AMPA receptor modulators, furnishing 5 with improved bioavailability and robust in vivo activity. [Copyright &y& Elsevier]

Abstract: Starting from an HTS derived hit 1, application of biostructural data facilitated rapid optimization to lead 22, a novel AMPA receptor modulator. This is the first demonstration of how structure based drug design can be exploited in an optimization program for a glutamate receptor. [Copyright &y& Elsevier]

Abstract: Following the discovery of a novel series of phosphate-containing small molecular Pin1 inhibitors, the drug design strategy shifted to replacement of the phosphate group with an isostere with potential better pharmaceutical properties. The initial loss in potency of carboxylate analogs was likely due to weaker charge–charge interactions in the putative phosphate binding pocket and was subsequently recovered by structure-based optimization of ligand–protein interactions in the proline binding site, leading to the discovery of a sub-micromolar non-phosphate small molecular Pin1 inhibitor. [Copyright &y& Elsevier]

Abstract: Described herein is design, synthesis, and biological evaluation of novel series of 2-aryl-7-(3′,4′-dialkoxyphenyl)-pyrazolo[1,5-a]pyrimidines acting as inhibitors of type 4 phosphodiesterase (PDE4) which is known as a good target for the treatment of asthma and COPD. For this purpose, structure optimization was conducted with the aid of structure-based drug design using the known X-ray crystallography. Also, biological effects of these compounds on the target enzyme were evaluated by using in vitro assays, leading to the potent and selective PDE-4 inhibitor (IC50 <10nM). [Copyright &y& Elsevier]

Abstract: Pin1 is a member of the cis–trans peptidyl-prolyl isomerase family with potential anti-cancer therapeutic value. Here we report structure-based de novo design and optimization of novel Pin1 inhibitors. Without a viable lead from internal screenings, we designed a series of novel Pin1 inhibitors by interrogating and exploring a protein crystal structure of Pin1. The ligand efficiency of the initial concept molecule was optimized with integrated SBDD and parallel chemistry approaches, resulting in a more attractive lead series. [Copyright &y& Elsevier]

Abstract: We disclose herein our efforts aimed at discovery of selective PARP-1 and PARP-2 inhibitors. We have recently discovered several novel classes of quinazolinones, quinazolidinones, and quinoxalines as potent PARP-1 inhibitors, which may represent attractive therapeutic candidates. In PARP enzyme assays using recombinant PARP-1 and PARP-2, the quinazolinone derivatives displayed relatively high selectivity for PARP-1 and quinoxaline derivatives showed superior selectivity for PARP-2, and the quinazolidinone derivatives did not have selectivity for PARP-1/2. Structure-based drug design analysis via a combination of X-ray structural study utilizing the complexes of inhibitors and human PARP-1 catalytic domain, and homology modeling using murine PARP-2 suggested distinct interactions of inhibitors with PARP-1 and PARP-2. These findings provide a new structural framework for the design of selective inhibitors for PARP-1 and PARP-2. [Copyright &y& Elsevier]

Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative structure–activity relationships (QSARs) were generated along with the investigation of enzyme–inhibitor molecular interactions. Predictive hologram QSAR (HQSAR, r2pred = 0.80) and AutoQSAR (q2 = 0.90) models were built, and key structural properties that underpin cruzain inhibition were identified. Moreover, comparative molecular field analysis (CoMFA, r2pred = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2pred = 0.73) revealed 3D molecular features that strongly affect the activity of the inhibitors. These findings were examined along with molecular docking studies and were highly compatible with the intermolecular contacts that take place between cruzain and the inhibitors. The results gathered herein revealed the main factors that determine the activity of the imidazoles studied and provide novel knowledge for the design of improved cruzain inhibitors. [ABSTRACT FROM AUTHOR]