Molecularly-based Medicine Exemplar Sample Clauses

Molecularly-based Medicine Exemplar. Machine learning and large-scale computing (UPF) CompBioMed directs its research to classical molecular dynamics (MD) simulations, which will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy [14]. Notwithstanding this, MD will still be in the regime of low-throughput, high-latency predictions with average accuracy. In this paper, the authors envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data. On these grounds, such techniques can be considered as a post-process stage: the predictive model is built upon those expensive individual simulations. Apart from the research on the proper ML algorithms, the post-process stage presents more difficulties, such as Input / Output (I/O), storage and data analysis. The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural biology and drug discovery. Figure 14. Overview of a combined simulation and machine learning approach. a. MD data generation is expected to reach the second aggregated timescale by 2022 and an output files size of several petabytes by 2022 based on a trend of maximum aggregated time per paper per year using the ACEMD software.
Sample 1
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Molecularly-based Medicine Exemplar. Machine learning and large-scale computing (UPF) CompBioMed directs its research to classical molecular dynamics (MD) simulations, which will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy [15]. Notwithstanding this, MD will still be in the regime of low-throughput, high-latency predictions with average accuracy. In this paper, the authors envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data. On these grounds, such techniques can be considered as a post-process stage: the predictive model is built upon those expensive individual simulations. Apart from the research on the proper ML algorithms, the post-process stage presents more difficulties, such as Input / Output (I/O), storage and data analysis. The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural biology and drug discovery. This case also represents a potential for co-design of a different kind than the precedent ones, because it focuses on the efficiency of ML applications and MD. Figure 15. Overview of a combined simulation and machine learning approach. a. MD data generation is expected to reach the second aggregated timescale by 2022 and an output files size of several petabytes by 2022 based on a trend of maximum aggregated time per paper per year using the ACEMD software.
Sample 1

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