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<!DOCTYPE html>
<!-- saved from url=(0053)https://getbootstrap.com/docs/4.0/examples/carousel/# -->
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="">
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<title>DeepDriveMD</title>
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<li class="nav-item">
<a class="nav-link" href="about.html">About</a>
</li>
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<a class="nav-link" href="join.html">Join</a>
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<div class="col-lg-12">
<h2>About DeepDriveMD</h2>
<p>Simulations of biological macromolecules play an important role in understanding the physical basis of a
number of complex processes such as protein folding. Even with increasing computational power and
evolution of specialized architectures, the ability to simulate protein folding at atomistic scales
still remains challenging. This stems from the dual aspects of high dimensionality of protein
conformational landscapes, and the inability of atomistic molecular dynamics (MD) simulations to
sufficiently sample these landscapes to observe folding events. Machine learning/deep learning (ML/DL)
techniques, when combined with atomistic MD simulations offer the opportunity to potentially overcome
these limitations by: (1) effectively reducing the dimensionality of MD simulations to automatically
build latent representations that correspond to biophysically relevant reaction coordinates (RCs), and
(2) driving MD simulations to automatically sample potentially novel conformational states based on
these RCs.</p>
</div>
</div>
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<h2>Funding</h2>
<p>DeepDriveMD is supported by the United States Department of Energy.</p>
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<h2 id='references'>References</h2>
<ol class="bibliography"><li><span id="lee2019deepdrivemd">Lee, H., Ma, H., Turilli, M., Bhowmik, D., Jha, S., & Ramanathan, A. (2019). <i>DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding</i>.</span></li></ol>
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