Synthesis of de novo designed small-molecule inhibitors of bacterial RNA polymerase

ReMODE: a deep learning-based web server for target-specific drug design

Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/ .

Comprehensive assessment of deep generative architectures for de novo drug design

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Bacterial Transcription as a Target for Antibacterial Drug Development

Transcription, the first step of gene expression, is carried out by the enzyme RNA polymerase (RNAP) and is regulated through interaction with a series of protein transcription factors. RNAP and its associated transcription factors are highly conserved across the bacterial domain and represent excellent targets for broad-spectrum antibacterial agent discovery. Despite the numerous antibiotics on the market, there are only two series currently approved that target transcription. The determination of the three-dimensional structures of RNAP and transcription complexes at high resolution over the last 15 years has led to renewed interest in targeting this essential process for antibiotic development by utilizing rational structure-based approaches. In this review, we describe the inhibition of the bacterial transcription process with respect to structural studies of RNAP, highlight recent progress toward the discovery of novel transcription inhibitors, and suggest additional potential antibacterial targets for rational drug design.

Applications of structure-based design to antibacterial drug discovery

In recent years bacterial resistance has been observed against many of our current antibiotics, for instance most worryingly against the cephalosporins which are typically the last line of defence against many bacterial infections. Additionally the failure of high throughput screening in the discovery of new antibacterial drug leads has led to a decline in the number of antibacterial agents reaching the market. Alternative methods of drug discovery including structure based drug design are needed to meet the threats caused by the emergence of resistance. In this review we explore the latest advancements in the identification of new antibacterial agents through the use of a number of structure based drug design programs.

Synthesis and biological activity of novel bis-indole inhibitors of bacterial transcription initiation complex formation

The synthesis of novel bis-indole amides and glyoxylamides as bacterial transcription complex formation inhibitors and their structure–activity relationships are discussed.

An in silico structure-based approach to anti-infective drug discovery

In light of the low success rate of target-based genomics and HTS (High Throughput Screening) approaches in anti-infective drug discovery, in silico structure-based drug design (SBDD) is becoming increasingly prominent at the forefront of drug discovery. In silico SBDD can be used to identify novel enzyme inhibitors rapidly, where the strength of this approach lies with its ability to model and predict the outcome of protein-ligand binding. Over the past 10 years, our group have applied this approach to a diverse number of anti-infective drug targets ranging from bacterial D-ala-D-ala ligase to Plasmodium falciparum DHODH. Our search for new inhibitors has produced lead compounds with both enzyme and whole-cell activity with established on-target mode of action. This has been achieved with greater speed and efficiency compared with the more traditional HTS initiatives and at significantly reduced cost and manpower.

Structure-based ligand design of novel bacterial RNA polymerase inhibitors

Bacterial RNA polymerase (RNAP) is essential for transcription and is an antibacterial target for small molecule inhibitors. The binding region of myxopyronin B (MyxB), a bacterial RNAP inhibitor, offers the possibility of new inhibitor design. The molecular design program SPROUT has been used in conjunction with the X-ray cocrystal structure of Thermus thermophilus RNAP with MyxB to design novel inhibitors based on a substituted pyridyl-benzamide scaffold. A series of molecules, with molecular masses <350 Da, have been prepared using a simple synthetic approach. A number of these compounds inhibited Escherichia coli RNAP.

Structure-based discovery of antibacterial drugs

The modern era of antibacterial chemotherapy began in the 1930s, and the next four decades saw the discovery of almost all the major classes of antibacterial agents that are currently in use. However, bacterial resistance to many of these drugs is becoming an increasing problem. As such, the discovery of drugs with novel modes of action will be vital to meet the threats created by the emergence of resistance. Success in discovering inhibitors using high-throughput screening of chemical libraries is rare. In this Review we explore the exciting opportunities for antibacterial-drug discovery arising from structure-based drug design.

De novo drug design

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

Construction of the cyclophane core of the hirsutellones via a RCM strategy

Construction of the highly strained [10]-paracyclophane core of the hirsutellones has been completed via an effective RCM strategy.

Furanyl-rhodanines are unattractive drug candidates for development as inhibitors of bacterial RNA polymerase

Previous studies suggest that furanyl-rhodanines might specifically inhibit bacterial RNA polymerase (RNAP). We further explored three compounds from this class. Although they inhibited RNAP, each compound also inhibited malate dehydrogenase and chymotrypsin. Using biosensors responsive to inhibition of macromolecular synthesis and membrane damaging assays, we concluded that in bacteria, one compound inhibited DNA synthesis and another caused membrane damage. The third rhodanine lacked antibacterial activity. We consider furanyl-rhodanines to be unattractive RNAP inhibitor drug candidates.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Design and synthesis of new hydroxyethylamines as inhibitors of D-alanyl-D-lactate ligase (VanA) and D-alanyl-D-alanine ligase (DdlB)

The Van enzymes are ATP-dependant ligases responsible for resistance to vancomycin in Staphylococcus aureus and Enteroccoccus species. The de novo molecular design programme SPROUT was used in conjunction with the X-ray crystal structure of Enterococcus faeciumd-alanyl-d-lactate ligase (VanA) to design new putative inhibitors based on a hydroxyethylamine template. The two best ranked structures were selected and efficient syntheses developed. The inhibitory activities of these molecules were determined on E. faecium VanA, and due to structural similarity and a common reaction mechanism, also on d-Ala-d-Ala ligase (DdlB) from Escherichia coli. The phosphate group attached to the hydroxyl moiety of the hydroxyethylamine isostere within these systems is essential for their inhibitory activity against both VanA and DdlB.