From 1f59073afbb9c3300e1dd892725ec6c70d63396f Mon Sep 17 00:00:00 2001 From: Matteo Degiacomi Date: Fri, 6 Sep 2024 14:40:00 +0100 Subject: [PATCH] Update paper.md --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index c9c21ce..b39c75f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -47,7 +47,7 @@ This is a graduate-level course, aimed at beginners in biomolecular simulation. ## Content -The objective of this course is not to make students proficient in one or few selected software for MD simulation preparation, execution, or analysisis. Instead, it is aimed at providing students with a general overview of the key decision-making required to carry out MD simulations of biomolecules and extracting quantitative data from them. In this context, the course is subdivided in two Units featuring lectures and practical sessions. Lectures are software-agnostic, whereas practical sessions demonstrate how those concepts could be put into practice by exposing student to authentic tasks leveraging on commonly used Python packages, such as MDAnalysis [@michaud-agrawal2011mdanalysis, @alibay2023building] and scikit-learn [@pedregosa2011scikitlearn]. While each practical session can be run by students on their own computer, these are also available in Google colab. This solution, requiring no local installation, is especially suitable for those unfamiliar with how to set-up a Python environment, or having limited access to computational resources. +The objective of this course is not to make students proficient in one or few selected software for MD simulation preparation, execution, or analysisis. Instead, it is aimed at providing students with a general overview of the key decision-making required to carry out MD simulations of biomolecules and extracting quantitative data from them. In this context, the course is subdivided in two Units featuring lectures and practical sessions. Lectures are software-agnostic, whereas practical sessions demonstrate how those concepts could be put into practice by exposing student to authentic tasks leveraging on commonly used Python packages, such as MDAnalysis [@michaud-agrawal2011mdanalysis, oliver_beckstein-proc-scipy-2016@alibay2023building] and scikit-learn [@pedregosa2011scikitlearn]. While each practical session can be run by students on their own computer, these are also available in Google colab. This solution, requiring no local installation, is especially suitable for those unfamiliar with how to set-up a Python environment, or having limited access to computational resources. ### Unit 1: Simulation Preparation @@ -92,7 +92,7 @@ In our teaching practice, we provide students with post-its of two different col Thanks to the increasing availability of computational power and software automating many of the processes associated with biomolecular simulation and analysis, the palette of questions addressable with MD is broadening. While this is positive, it remains crucial for computational scientists to have a clear understanding of what is being simulated and how. Indeed, to date many decisions associated with system building and analysis cannot be delegated to a machine without human verification. In this context, we see our course as a first stepping-stone, detailing the key decisions that need to be made, providing examples of how this can be done in practice, and directing learners to relevant software and specialized analysis techniques for further education. -Despite its long history, MD remains an evolving field. New techniques that push the boundaries of what is possible keep emerging, as exemplified by the current revolution associated with the integration of modern machine learning techniques in molecular modelling pipelines. While we expect that majority of the concepts presented in this course will be valid for many years to come, we are endeavouring to keeping the course material up-to-date by highlighting current methodological trends. For instance in the latest iteration of this course we have introduced a discussion on how how to interpret and use models produced by AlphaFold [ref]. +Despite its long history, MD remains an evolving field. New techniques that push the boundaries of what is possible keep emerging, as exemplified by the current revolution associated with the integration of modern machine learning techniques in molecular modelling pipelines. While we expect that majority of the concepts presented in this course will be valid for many years to come, we are endeavouring to keeping the course material up-to-date by highlighting current methodological trends. For instance in the latest iteration of this course we have introduced a discussion on how how to interpret and use models produced by AlphaFold [@jumper2021highly]. # Contributions to the course