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Publisher: IBM   (Total: 1 journals)   [Sort alphabetically]

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IBM J. of Research and Development     Hybrid Journal   (Followers: 18, SJR: 0.275, CiteScore: 1)
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IBM Journal of Research and Development
Journal Prestige (SJR): 0.275
Citation Impact (citeScore): 1
Number of Followers: 18  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-8646
Published by IBM Homepage  [1 journal]
  • Preface: Computational Technologies for Drug Discovery
    • Authors: Wendy Cornell;
      Pages: 1 - 2
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • The challenge of new chemical entities attrition
    • Authors: K. Cooper;
      Pages: 1:1 - 1:9
      Abstract: The pharmaceutical industry is facing many challenges to the successful delivery of new medicines from its research and development (R&D) efforts. Central to these productivity challenges is the declining survival of small molecule drug candidates from the preclinical stage through to Food and Drug Administration (FDA) approval and launch, despite the introduction of many new technologies and significant increases in R&D budgets. Only 50% of small molecules survive the preclinical stage, and only 1 in 16 of those that enter Phase I survive through to approval and launch. This contrasts with a 1 in 9 survival rate for biologics from Phase I through to approval and launch. Three key factors contribute to this diminishing survival rate. First, small molecules with poor physicochemical properties are closely associated with poor survival; second, many of the chosen biological targets for small molecules fail to produce meaningful efficacy in the disease; and third, the constant turmoil of strategy changes within pharmaceutical companies accounts for a surprisingly high rate of small molecule program failure. Each of the three key factors is reviewed, and recommendations for how to address each factor are explored.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • Bayesian optimization for accelerated drug discovery
    • Authors: E. O. Pyzer-Knapp;
      Pages: 2:1 - 2:7
      Abstract: The space of potential drug-like molecules is vast, precluding “random-walk”-like searches from achieving any reasonable effectiveness. Active search techniques have been increasing in popularity in recent years as a method for accelerating the discovery of novel pharmaceutical molecules. By providing an effective method for prioritizing molecules within the discovery process, the efficiency of the discovery process can be dramatically improved. In this paper, we describe the use of Bayesian optimization, a method for iterative optimization of black-box functions for achieving this end, balancing the exploitation of current knowledge acquired from data, with the acquisition of new knowledge about which little is known.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • The Pharmit backend: A computer systems approach to enabling interactive
           online drug discovery
    • Authors: D. R. Koes;
      Pages: 3:1 - 3:6
      Abstract: Pharmit (http://pharmit.csb.pitt.edu) is an open-source online resource that allows users to interactively search libraries of millions of compounds as part of a structure-based drug discovery workflow. In this paper, I describe the systems-level implementation decisions made in designing Pharmit that, when combined with novel sublinear time search algorithms, allow it to screen millions of molecules in seconds. The key concepts are to maximize parallelism while minimizing intrathread communication, optimize data layout for sequential processing, and efficiently manage memory allocation. I describe how these concepts are applied to the cheminformatic data inherent to Pharmit and discuss limitations and possible future directions.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • Scalable molecular dynamics with NAMD on the Summit system
    • Authors: B. Acun;D. J. Hardy;L. V. Kale;K. Li;J. C. Phillips;J. E. Stone;
      Pages: 4:1 - 4:9
      Abstract: NAnoscale Molecular Dynamics (NAMD) is a parallel molecular dynamics application that has been used to make breakthroughs in understanding the structure and dynamics of large biomolecular complexes, such as viruses like HIV and various types of influenza. State-of-the-art biomolecular simulations often require integration of billions of timesteps, computing all interatomic forces for each femtosecond timestep. Molecular dynamics simulation of large biomolecular systems and long-timescale biological phenomena requires tremendous computing power. NAMD harnesses the power of thousands of heterogeneous processors to meet this demand. In this paper, we present algorithmic improvements and performance optimizations that enable NAMD to achieve high performance on the IBM Newell platform (with IBM POWER9 processors and NVIDIA Volta V100 GPUs), which underpins the Oak Ridge National Laboratory's Summit and Lawrence Livermore National Laboratory's Sierra supercomputers. The Top-500 supercomputers November 2018 list shows Summit at the number one spot, with 200 petaflop/s peak performance, and Sierra second with 125 petaflop/s. Optimizations for NAMD on Summit include: data layout changes for GPU acceleration and CPU vectorization, improving GPU offload efficiency, increasing performance with Parallel Active Messaging Interface support in Charm++, improving efficiency of fast Fourier transform calculations, improving load balancing, enabling better CPU vectorization and cache performance, and providing an alternative thermostat through stochastic velocity rescaling. We also present performance scaling results on early Newell systems.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • Semiempirical molecular dynamics (SEMD) simulations: Parameterization and
           validation for biological systems precursors
    • Authors: F. Zipoli;M. Hijazi;R. Petraglia;V. Weber;T. Laino;
      Pages: 5:1 - 5:9
      Abstract: The design and development of novel drugs could benefit considerably from more reliable atomistic modeling schemes able to deal with the appropriate level of theoretical description on size and time scales relevant for complex life-science systems. This has not been possible, mainly due to limitations of the scaling and performance of quantum chemical algorithms on large biomolecules. Recently, we presented a novel approach that allows us to apply quantum simulations to the kinds of biological domains that are typical for classical Hamiltonians. For this we exploited a novel and efficient large-scale parallel sparse matrix–matrix multiplication algorithm. The scheme is sufficiently general to deal with any type of quantum scheme, and we opted our algorithm to an easily parametrizable semiempirical Neglect of Diatomic Differential Orbitals (NDDO) Hamiltonian. In this paper, we present a novel NDDO parameterization specifically tailored for treating intra- and inter-molecular interactions in proteins. We include a validation of static and dynamic properties for individual amino acids as well as for small polypeptides using both experimental and density functional theory benchmarks for reference. We also demonstrate the preeminence of this approach for describing bond-breaking reactions in terms of time-to-solution compared to traditional quantum simulations.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • Potential of quantum computing for drug discovery
    • Authors: Y. Cao;J. Romero;A. Aspuru-Guzik;
      Pages: 6:1 - 6:20
      Abstract: Quantum computing has rapidly advanced in recent years due to substantial development in both hardware and algorithms. These advances are carrying quantum computers closer to their impending commercial utility. Drug discovery is a promising area of application that will find a number of uses for these new machines. As a prominent example, quantum simulation will enable faster and more accurate characterizations of molecular systems than existing quantum chemistry methods. Furthermore, algorithmic developments in quantum machine learning offer interesting alternatives to classical machine learning techniques, which may also be useful for the biochemical efforts involved in early phases of drug discovery. Meanwhile, quantum hardware is scaling up rapidly into a regime where an exact simulation is difficult even using the world’s largest supercomputers. We review how these recent advances can shift the paradigm with which one thinks about drug discovery, focusing on both the promises and caveats associated with each development. In particular, we highlight how hybrid quantum-classical approaches to quantum simulation and quantum machine learning could yield substantial progress using noisy-intermediate scale quantum devices, whereas fault-tolerant, error-corrected quantum computers are still in their development phase.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • Systematic analysis of drug combinations that mitigate adverse drug
           reactions
    • Authors: J. Shim;H. Luo;P. Zhang;Y. Li;
      Pages: 7:1 - 7:9
      Abstract: Seeking beneficial drug–drug combinations (DDCs) from real-world evidence is an emerging topic in phenotypic drug discovery. With sophisticated algorithms, the numbers of DDC hypotheses generated often reach to tens of thousands. However, due to limited resources, only a few, top-ranking hypotheses are selected for experimental validations. Often, researchers start from the topmost DDC pairs and work their way down until they find a pair with successful validation. While this is an established way of performing validations, there still exists room to improve. Here, we present a systematic approach to perform a secondary analysis on the DDCs to streamline the validation procedure. Specifically, we propose a method in which we search for additional patterns in terms of chemical classes and biological target interactions of the drug pair. Using 78,345 DDC hypotheses generated from the FDA Adverse Event Reporting System (FAERS) data in our prior study, we demonstrate how the proposed analysis can reveal additional biochemical and mechanical insights of drug interactions that can streamline experimental validation.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
  • Hybrid natural language processing for high-performance patent and
           literature mining in IBM Watson for Drug Discovery
    • Authors: R. L. Martin;D. Martinez Iraola;E. Louie;D. Pierce;B. A. Tagtow;J. J. Labrie;P. G. Abrahamson;
      Pages: 8:1 - 8:12
      Abstract: IBM Watson for Drug Discovery (WDD) is a cognitive computing software platform for early stage pharmaceutical research. WDD extracts and cross-references life sciences information from very large-scale structured and unstructured data, identifying connections and correlations in an unbiased manner, and enabling more informed decision making through explainable analytics and scientific visualizations. This paper describes in detail the high-throughput natural language processing system implemented in WDD. This system enables a new WDD release every three weeks, comprising the latest publications as part of a continually growing corpus of over 30 million scientific and intellectual property documents, each reprocessed using the latest annotators and structured reference data to extract a set of domain-relevant entity and relationship concepts. The hybrid approach to natural language processing in WDD incorporates model- and rule-based techniques utilized in concert for high-performance named entity recognition, and a similar ensemble approach to named entity resolution tasks, culminating in semantic relationship extraction. Statistics on full-scale annotation results and example use cases are also provided.
      PubDate: Nov.-Dec. 1 2018
      Issue No: Vol. 62, No. 6 (2018)
       
 
 
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