Reliability and reproducibility in computational science: implementing validation, verification and uncertainty quantification
            <i>in silico</i>

Coveney, Peter V.; Groen, Derek; Hoekstra, Alfons G.

doi:10.1098/rsta.2020.0409

Cited by 20 publications

(18 citation statements)

References 15 publications

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“…UQ is an established domain in applied mathematics and engineering but has been notably absent inter alia from the analysis of computer simulations performed using electronic structure and molecular simulation methods. At this time, we are witnessing unified developments in quantifying uncertainty in computer simulation across a wide range of domains including weather, climate, material, fusion, molecular and biomedical sciences [7–13].…”

Section: Introductionmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

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Section: Introductionmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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“…The term "scientific reproducibility" of experimental results and observations and theories means that the reliability of these data does not depend on who produces them, but rather, the same findings should be obtained by anyone performing similar procedures. 576 Coveney and co-authors observed that for computer modelling, three factors assess the reliability of calculations to be compared with existing experimental measurements and to make predictions for which no experimental data are available: (a) validation, namely, the confirmation that the results are in agreement with experiment; (b) verif ication that the software does what it is supposed to do and does not contain any errors arising from an incorrect implementation or incorrect numerical methods; and (c) uncertainty quantification to identify the source of errors within the model, for instance systematic errors due to parameter estimation. 576 In classical MD simulations, the lack of reproducibility depends primarily on the intrinsically chaotic nature of the 577 There are two sources of errors in this type of simulations emerging from random and systematic sources.…”

Section: Molecular Modelling and Experiments: A Synergistic And Neces...mentioning

confidence: 99%

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

2022

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It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as “multistep” processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

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“…Practical efforts to realize a quantum computer introduce many additional physical processes, identified here as noise, that complicate the operational description above. [5][6][7][8] In all cases, an important question is to understand whether the resulting computational output is accurate, reproducible and stable. We next consider these notions in the presence of quantum channels that model noisy operations.…”

Section: Theorymentioning

confidence: 99%

Assessing the Stability of Noisy Quantum Computation

Dasgupta¹,

Humble²

2022

Preprint

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Quantum computation has made considerable progress in the last decade with multiple emerging technologies providing proof-of-principle experimental demonstrations of such calculations. However, these experimental demonstrations of quantum computation face technical challenges due to the noise and errors that arise from imperfect implementation of the technology. Here, we frame the concepts of computational accuracy, result reproducibility, device reliability and program stability in the context of quantum computation. We provide intuitive definitions for these concepts in the context of quantum computation that lead to operationally meaningful bounds on program output. Our assessment highlights the continuing need for statistical analyses of quantum computing program to increase our confidence in the burgeoning field of quantum information science.

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Reliability and reproducibility in computational science: implementing validation, verification and uncertainty quantification in silico

Abstract: One contribution of 15 to a theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Cited by 20 publications

References 15 publications

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

Assessing the Stability of Noisy Quantum Computation

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