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Data-Driven Molecular Dynamics: A Multifaceted Challenge

1
Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, I-34136 Trieste, Italy
2
Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, I-16163 Genova, Italy
3
Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
Current affiliation: Global Research Informatics/Computational Chemistry, Evotec (France) SAS, 31100 Toulouse, France.
Pharmaceuticals 2020, 13(9), 253; https://doi.org/10.3390/ph13090253
Received: 25 August 2020 / Revised: 14 September 2020 / Accepted: 16 September 2020 / Published: 18 September 2020
(This article belongs to the Special Issue Computational Drug Discovery and Development in the Era of Big Data)
The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data. View Full-Text
Keywords: machine learning; dimensionality reduction; reaction coordinates; collective variables; Markov state models; maximum entropy principle; experimental data machine learning; dimensionality reduction; reaction coordinates; collective variables; Markov state models; maximum entropy principle; experimental data
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Bernetti, M.; Bertazzo, M.; Masetti, M. Data-Driven Molecular Dynamics: A Multifaceted Challenge. Pharmaceuticals 2020, 13, 253.

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