Bigger data,collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.     

Status of Drug Discovery Research Based on Marine Organisms from Eastern Indonesia

Despite the vastness of Eastern Indonesian Waters (EIW), no review has been done on the status of drug discovery research based on marine organisms from this area. The aim of this paper is to briefly discuss the challenges and perspectives on drug discovery research based on marine organisms in these indigenous waters. The emphasis is on the last 20 year period (1993 to 2013). Research activities completed during this period suggest that marine organisms from EIW could be utilized as an important natural resource for future drug discovery and development. However, lack of facilities, as well as competent human resources, significantly hinder progress on drug discovery research. More in-depth study especially on deep-sea natural products needs to be carried out to solidify the research on the potential for marine organisms from EIW to contribute to the future of drug discovery.     

Computer-aided drug design at Boehringer Ingelheim

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.     

eSHAFTS: Integrated and graphical drug design software based on 3D molecular similarity

With the development of computer technology, computer-aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three-dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug-ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html . © 2019 Wiley Periodicals, Inc.     

Virtual Computational Chemistry Laboratory – Design and Description

Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.     

Computational chemistry in drug lead discovery and design

The main contributions of our group during the last 15 years developing and using biomolecular simulation tools in drug lead discovery and design, in close collaboration with experimental researchers, are presented. Special emphasis has been given to methodological improvements in the following areas: (1) target homology modeling incorporating knowledge about known ligands to accurately characterize the binding site; (2) designing alternative strategies to account for protein flexibility in high-throughput docking; (3) development of stochastic- and normal-mode-based methods to de novo design structurally diverse protein conformers; (4) development and validation of quantum mechanical semi-empirical linear-scaling calculations to correctly estimate ligand binding free energy. Several successful cases of computer-aided drug discovery are also presented, especially our recent work on viral targets.     

Design and Synthesis of 56 Shape-Diverse 3D Fragments

Fragment-based drug discovery is now widely adopted for lead generation in the pharmaceutical industry. However, fragment screening collections are often predominantly populated with flat, 2D molecules. Herein, we describe a workflow for the design and synthesis of 56 3D disubstituted pyrrolidine and piperidine fragments that occupy under-represented areas of fragment space (as demonstrated by a principal moments of inertia (PMI) analysis). A key, and unique, underpinning design feature of this fragment collection is that assessment of fragment shape and conformational diversity (by considering conformations up to 1.5 kcal mol⁻¹ above the energy of the global minimum energy conformer) is carried out prior to synthesis and is also used to select targets for synthesis. The 3D fragments were designed to contain suitable synthetic handles for future fragment elaboration. Finally, by comparing our 3D fragments with six commercial libraries, it is clear that our collection has high three-dimensionality and shape diversity.     

Screening of Big Pharma’s Library against Various in-house Biological Targets

Open innovation initiatives provide opportunities for collaboration and sharing of knowledge and experience between industry, academia, and government institutions. Through open innovation, Merck is offering a Mini Library of 80 carefully selected compounds from previous research and development projects to a broader scientific community for testing in academic drug discovery projects. These compounds are predominantly drug-like and cover a broad range of molecular targets. They could potentially interact with other enzymes, receptors, transporters, and ion channels of interest. The Mini Library was tested on seven in-house enzymes (bacterial MurA, MurC ligase, and DdlB enzyme, human MAO-A/B, human BChE, and murine AChE), and several hits were identified. A follow-up series of structural analogues provided by Merck gave a more detailed insight into the accessibility and the quality of the hit compounds. For example, sartan derivatives were moderate inhibitors of MurC, whereas bisarylureas were potent, selective, nanomolar inhibitors of hMAO-B. Importantly, 3-n-butyl-substituted indoles were identified as low nanomolar selective inhibitors of hBChE. All in all, the hit derivatives provide new starting points for the further exploration of the chemical space of high-quality enzyme inhibitors.     

Developing strategies for isolation of minor impurities with mass spectrometry-directed fractionation

Efficient and automated purification of new chemical entities/potential drug substances and isolation of minor impurities are important aspects of early drug discovery and development strategies, especially when combinatorial synthesis is applied. LC–MS controlled preparative LC and automated fraction collection have been developed for this purpose. The success of such an approach is greatly determined by the quality of the software controlling the application, the coordination between software and hardware, and the reliability of the hardware. The performance of a commercially-available LC–MS controlled autopurification system was evaluated by fractionating four impurities of buspirone as a model compound, eluting closely to the major component under both acidic and basic mobile-phase conditions. A purification strategy for these four components is proposed.     

Identification of novel S-adenosyl-L-homocysteine hydrolase inhibitors through homology-model-based virtual screening, synthesis, and biological evaluation

The present study describes a successful application of computational approaches to identify novel Leishmania donovani (Ld) AdoHcyase inhibitors utilizing the differences for Ld AdoHcyase NAD(+) binding between human and Ld parasite. The development and validation of the three-dimensional (3D) structures of Ld AdoHcyase using the L. major AdoHcyase as template has been carried out. At the same time, cloning of the Ld AdoHcyase gene from clinical strains, its overexpression and purification have been performed. Further, the model was used in combined docking and molecular dynamics studies to validate the binding site of NAD in Ld. The hierarchical structure based virtual screening followed by the synthesis of five active hits and enzyme inhibition assay has resulted in the identification of novel Ld AdoHcyase inhibitors. The most potent inhibitor, compound 5, may serve as a "lead" for developing more potent Ld AdoHcy hydrolase inhibitors as potential antileishmanial agents.     

Repositioning of Quinazolinedione-Based Compounds on Soluble Epoxide Hydrolase (sEH) through 3D Structure-Based Pharmacophore Model-Driven Investigation

The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes.     

The beautiful cell: high-content screening in drug discovery

The term “high-content screening” has become synonymous with imaging screens using automated microscopes and automated image analysis. The term was coined a little over 10 years ago. Since then the technology has evolved considerably and has established itself firmly in the drug discovery and development industry. Both the instruments and the software controlling the instruments and analyzing the data have come to maturity, so the full benefits of high-content screening can now be realized. Those benefits are the capability of carrying out phenotypic multiparametric cellular assays in an unbiased, fully automated, and quantitative fashion. Automated microscopes and automated image analysis are being applied at all stages of the drug discovery and development pipeline. All major pharmaceutical companies have adopted the technology and it is in the process of being embraced broadly by the academic community. This review aims at describing the current capabilities and limits of the technology as well as highlighting necessary developments that are required to exploit fully the potential of high-content screening and analysis.     

WinDock: structure-based drug discovery on Windows-based PCs

In recent years, virtual database screening using high-throughput docking (HTD) has emerged as a very important tool and a well-established method for finding new lead compounds in the drug discovery process. With the advent of powerful personal computers (PCs), it is now plausible to perform HTD investigations on these inexpensive PCs. To make HTD more accessible to a broad community, we present here WinDock, an integrated application designed to help researchers perform structure-based drug discovery tasks under a uniform, user friendly graphical interface for Windows-based PCs. WinDock combines existing small molecule searchable three-dimensional (3D) libraries, homology modeling tools, and ligand-protein docking programs in a semi-automatic, interactive manner, which guides the user through the use of each integrated software component. WinDock is coded in C++.     

MSE with mass defect filtering for in vitro and in vivo metabolite identification

Metabolite identification studies involve the detection and structural characterization of the biotransformation products of drug candidates. These experiments are necessary throughout the drug discovery and development process. The use of high-resolution chromatography and high-resolution mass spectrometry together with data processing using mass defect filtering is described for in vitro and in vivo metabolite identification studies. Data collection was done using UPLC coupled with an orthogonal hybrid quadrupole time-of-flight mass spectrometer. This experimental approach enabled the use of MS(E) data collection (where E represents collision energy) which has previously been shown to be a powerful approach for metabolite identification studies. Post-acquisition processing with a prototype mass defect filtering program was used to eliminate endogenous interferences in the study samples, greatly enhancing the discovery of metabolites. The ease of this approach is illustrated by results showing the detection and structural characterization of metabolites in plasma from a preclinical rat pharmacokinetic study.     

Using silico methods predicting ligands for orphan GPCRs

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.     

Thermo-oxidation behaviour of composite materials at high temperatures: A review of research activities carried out within the COMEDI program

The present paper presents a review of the main activities carried out within the context of the COMEDI research program, a joint collaboration involving three research teams focusing on the thermo-oxidation behaviour of composite materials at high temperatures.The scientific aim of the COMEDI research program was to better identify the link between the physical mechanisms involved in thermo-oxidation phenomena: oxygen reaction-diffusion, chemical shrinkage strain/stress, degradation at different scales and to provide tools for predicting the thermo-oxidation behaviour of composite materials under thermo-oxidative environments including damage onset.This aim was accomplished by investigating experimentally the thermo-oxidation behaviour of pure resin samples - both industrial and “model” materials - and by interpreting the results by a coupled reaction-diffusion-mechanics multiphysics model.A dedicated numerical model tool has been developed and implemented into the ABAQUS^® finite element commercial software. This tool was employed to simulate the thermo-oxidative behaviour of a fibre-matrix microscopic representative composite cell.Finally, the model predictions for the composite have been validated by comparing the experimental and the simulated local matrix shrinkage displacements and the mass loss of composite specimens.     

Subtractive Genomics Approach to Identify Putative Drug Targets and Identification of Drug-like Molecules for Beta Subunit of DNA Polymerase III in Streptococcus Species

The prolonged use of the antibiotics over the years has transformed many organisms resistant to multiple drugs. This has made the field of drug discovery of vital importance in curing various infections and diseases. The drugs act by binding to a specific target protein of prime importance for the cell??s survival. Streptococcus agalactiae, Streptococcus pneumoniae, and Streptococcus pyogenes are the few gram positive organisms that have developed resistance to drugs. It causes pneumonia, meningitis, pharyngitis, otitis media, sinusitis, bacteremia, pericarditis, and arthritis infections. The present study was carried out to identify potential drug targets and inhibitors for beta subunit of DNA polymerase III in these three Streptococcus species that might facilitate the discovery of novel drugs in near future. Various steps were adopted to find out novel drug targets. And finally 3D structure of DNA polymerase III subunit beta was modeled. The ligand library was generated from various databases to find the most suitable ligands. All the ligands were docked using Molegro Virtual Docker and the lead molecules were investigated for ADME and toxicity.