9,849 research outputs found

    Druggable chemical space and enumerative combinatorics

    Get PDF

    11th German Conference on Chemoinformatics (GCC 2015) : Fulda, Germany. 8-10 November 2015.

    Get PDF

    Pathways Across the Valley of Death: Novel Intellectual Property Strategies for Accelerated Drug Discovery

    Get PDF
    Drug discovery is stagnating. Government agencies, industry analysts, and industry scientists have all noted that, despite significant increases in pharmaceutical R&D funding, the production of fundamentally new drugs - particularly drugs that work on new biological pathways and proteins - remains disappointingly low. To some extent, pharmaceutical firms are already embracing the prescription of new, more collaborative R&D organizational models suggested by industry analysts. In this Article, we build on collaborative strategies that firms are already employing by proposing a novel public-private collaboration that would help move upstream academic research across the valley of death that separates upstream research from downstream drug candidates. By exchanging trade secrecy for contract-based collaboration, our proposal would both protect intellectual property rights and enable many more researchers to search for potential drug candidates

    Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme

    Get PDF
    Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtual screening of new ligands on the basis of biological structures for identification of hits and generation of leads or optimization (potency/ property) of leads in drug discovery program. Hence, docking is approach of SBDD which plays an important role in rational designing of new drug molecules. Quantitative structure-activity relationship (QSAR) is an important chemometric tool in computational drug design. It is a common practice of LBDD. The study of QSAR gives information related to structural features and/or physicochemical properties of structurally similar molecules to their biological activity. In this paper, a comprehensive review on several computational tools of SBDD and LBDD such as virtual screening, molecular docking and QSAR methods of and their applications in the drug discovery and development programme have been summarized. Keywords: Virtual screening, Molecular docking, QSAR, Drug discovery, Lead molecul

    Contract-Based General-Purpose GPU Programming

    Get PDF
    Using GPUs as general-purpose processors has revolutionized parallel computing by offering, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper suggests a programming library, SafeGPU, that aims at striking a balance between programmer productivity and performance, by making GPU data-parallel operations accessible from within a classical object-oriented programming language. The solution is integrated with the design-by-contract approach, which increases confidence in functional program correctness by embedding executable program specifications into the program text. We show that our library leads to modular and maintainable code that is accessible to GPGPU non-experts, while providing performance that is comparable with hand-written CUDA code. Furthermore, runtime contract checking turns out to be feasible, as the contracts can be executed on the GPU

    Biophysics in drug discovery : impact, challenges and opportunities

    Get PDF
    Over the past 25 years, biophysical technologies such as X-ray crystallography, nuclear magnetic resonance spectroscopy, surface plasmon resonance spectroscopy and isothermal titration calorimetry have become key components of drug discovery platforms in many pharmaceutical companies and academic laboratories. There have been great improvements in the speed, sensitivity and range of possible measurements, providing high-resolution mechanistic, kinetic, thermodynamic and structural information on compound-target interactions. This Review provides a framework to understand this evolution by describing the key biophysical methods, the information they can provide and the ways in which they can be applied at different stages of the drug discovery process. We also discuss the challenges for current technologies and future opportunities to use biophysical methods to solve drug discovery problems

    ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

    Get PDF
    Drug discovery is said to be a multi-dimensional issue in which different properties of drug candidates including efficacy, pharmacokinetics, and safety need to be improved with respect to giving the final drug product. Current advances in fields such as artificial intelligence (AI) systems that refine the design thesis through report investigation, microfluidics-assisted chemical synthesis, and biological testing are now giving a cornerstone for the establishment of greater automation into detail of this process. AI has stimulated computer-aided drug discovery. This could likely speed up time duration for compound discovery and enhancement and authorize more productive hunts of related chemicals. However, such optimization also increases substantial theories, technical, and organizational queries, as well as suspicion about the ongoing boost around them. Machine learning, in particular deep learning, in multiple scientific disciplines, and the development in computing hardware and software, among other factors, continue to power this development worldwide

    How can natural products serve as a viable source of lead compounds for the development of new/novel anti-malarials?

    Get PDF
    Malaria continues to be an enormous global health challenge, with millions of new infections and deaths reported annually. This is partly due to the development of resistance by the malaria parasite to the majority of established anti-malarial drugs, a situation that continues to hamper attempts at controlling the disease. This has spurred intensive drug discovery endeavours geared towards identifying novel, highly active anti-malarial drugs, and the identification of quality leads from natural sources would greatly augment these efforts. The current reality is that other than compounds that have their foundation in historic natural products, there are no other compounds in drug discovery as part of lead optimization projects and preclinical development or further that have originated from a natural product start-point in recent years. This paper briefly presents both classical as well as some more modern, but underutilized, approaches that have been applied outside the field of malaria, and which could be considered in enhancing the potential of natural products to provide or inspire the development of anti-malarial lead compounds

    Theory and applications of differential scanning fluorimetry in early-stage drug discovery

    Get PDF
    Differential scanning fluorimetry (DSF) is an accessible, rapid, and economical biophysical technique that has seen many applications over the years, ranging from protein folding state detection to the identification of ligands that bind to the target protein. In this review, we discuss the theory, applications, and limitations of DSF, including the latest applications of DSF by ourselves and other researchers. We show that DSF is a powerful high-throughput tool in early drug discovery efforts. We place DSF in the context of other biophysical methods frequently used in drug discovery and highlight their benefits and downsides. We illustrate the uses of DSF in protein buffer optimization for stability, refolding, and crystallization purposes and provide several examples of each. We also show the use of DSF in a more downstream application, where it is used as an in vivo validation tool of ligand-target interaction in cell assays. Although DSF is a potent tool in buffer optimization and large chemical library screens when it comes to ligand-binding validation and optimization, orthogonal techniques are recommended as DSF is prone to false positives and negatives
    corecore