A macromolecular structure building toolkit for machine learning and cloud applications

科学家们对生物分子的原子结构感兴趣：换句话说，分子是什么样子。详细了解分子的样子为它可能的工作方式提供了重要的线索。如果我们能更进一步，在与其他生物分子或人工化合物(如药物)相互作用的过程中捕获分子，我们就能更清楚地了解它们是如何工作的。我们对生物分子结构的大部分知识来自实验技术，包括X射线结晶学和电子显微镜(EM)。这些实验技术为我们提供了真实分子的图片，我们可以在其中看到分子结构的轮廓，但我们通常无法看到单个原子或将它们区分开来。因此，我们需要根据我们从用于构建分子的遗传密码中了解到的分子来解释这张图。我们通过两种方式解决这个问题：通过允许用户使用3D图形放置原子的软件来查看形状，或者通过尝试自动完成相同过程的软件。自动过程涉及许多步骤，从识别原子组到将它们连接起来并将它们与遗传密码匹配。计算机视觉的最新进展为改进自动口译创造了巨大的机会，在这些领域工作的科学家在一些步骤上做出了革命性的改进。然而，这些突破只有在与其余步骤结合使用时才有用。因此，我们希望将我们的自动口译软件分解为单独的步骤，使这些步骤非常易于其他组使用。然后他们可以尝试用他们的新代码替换他们感兴趣的步骤，并将得到的方法作为一个完整的包分发。这项工作的另一个有趣的元素是，它的结构使得科学的主要好处是让其他人受益。科学是由科学家在他人工作的基础上进行的。我们观察到，一些从事科学工作的方式阻碍了这一点--科学是由资深科学家领导的小组完成的，他们相互竞争资金和认可，这阻碍了方法和结果的分享。我们想要测试是否有更好的方法来做科学，并通过主要为他人造福来用更少的资金取得更大的进步。如果我们是正确的，那么在5-10年的过程中，我们应该能够确定我们的工作使之成为可能的项目，即使我们没有发起或参与这些项目。我们的目标是通过将基于我们工作的项目与基于其他组件或从头开始构建的项目进行比较，来定性地了解我们的方法是如何影响现场实践的。这个项目的最后一步是使我们编写的工具在Web浏览器中工作，以便用户不需要安装特殊软件。这将把我们的工作与云计算的发展联系起来，我们还将调整我们的方法，以帮助预测来自谷歌DeepMind项目的分子形状的进展。这将使该领域的新参与者更容易掌握确定分子结构的步骤，包括学生、学校、拥有更有限计算资源的参与者，如Chromebook和移动设备。参与的障碍往往将科学实践局限于现有的特权群体，因此，使这些方法更广泛地可用将减少机会不平等，并鼓励科学界的多样性。

Scientists are interested in the atomic structure of biological molecules: in other words, what the molecules look like. Knowing in detail what a molecule looks like provides important clues to how it might work. If we can go further and capture molecules in the process of interacting with other biological molecules, or artificial compounds such as drugs, we get a clearer picture of how they work.Most of our knowledge of the structure of biological molecules comes from experimental techniques including X-ray crystallography and electron microscopy (EM). These experimental techniques give us pictures of the real molecules in which we can see an outline of the molecular structure, but we can't usually see the individual atoms or tell them apart. So we need to interpret the map in terms of what we know about the molecule from the genetic code which was used to build it. We address this in two ways: through software which allows the user to place atoms using 3D graphics to see the shapes, or by software which tries to do the same process automatically.The automatic process involves lots of steps, from recognizing groups of atoms to linking them up and matching them to the genetic code. Recent advances in computer vision have created huge opportunities to improve automatic interpretation, and scientists working in these areas have produced revolutionary improvements in some of the steps. However these breakthroughs are only useful in combination with the rest of the steps. So we want to break up our automated interpretation software into the individual steps and make those steps very easy for other groups to use. They can then try replacing the step they are interested in with their new code and distribute the resulting method as a complete package.Another interesting element of this work is that it is structured so that the primary benefit of science is to others. Science works by scientists building on the work of others. We have observed that some of the ways in which science is done discourages this - science is done by groups led by senior scientists who are in competition with one another for funds and recognition, which disincentives the sharing of methods and results. We want to test if there is a better way to do science and achieve more progress with less funding by working primarily to benefit others. If we are right, then over the course of 5-10 years we should be able to identify projects which have been enabled by our work, even if we did not initiate or participate in those projects. We will aim to build a qualitative picture of how our approach has impacted practice in the field by comparing project building on our work to projects building on other components or built from scratch.A final strand of this project is to make the tools that we write work in web browsers, so that users do not need to install special software. This will link our work with developments in cloud computing, and we will also adapt our methods to help with advances in predicting the shape of molecules which have come from Google's DeepMind project. This will make the steps of determining molecular structures more accessible to new participants in the field, including students, schools, participants with more limited computing resources such as Chromebooks and mobile devices. Barriers to participation often serve to confine the practice of science to existing privileged groups, so making these methods more widely available will reduce inequalities of opportunity and encourage diversity in the scientific community.