Next generation atomistic modelling for medicinal chemistry and biology

诺贝尔物理学奖获得者理查德·费曼在他的《物理学讲义》中有一句名言：“......生物所做的一切都可以用原子的摆动来理解。”这个看似简单的陈述突出了结构生物学家，药物化学家和计算科学家在试图了解人类健康和疾病时所面临的困难。我们习惯于在原子尺度上思考物体的静态、孤立的画面，但通常是动态的。（“抖动和摆动”）的系统和它的环境相互作用，决定了基础科学，例如内在无序的蛋白质在神经退行性疾病中的作用，或生物光合作用中量子纠缠和分子振动之间的可能联系。他提出了在原子结构和动力学水平上研究生命的挑战，但也通过量子力学定律和著名的薛定谔方程提供了（理论上）解决方案。量子力学在原子尺度上解释了物质的基本行为，甚至更小。它使科学家能够对实验无法获得的材料做出预测，例如星星核心中固体氢的结构。在更日常的层面上，微电子和可再生能源行业的研究人员经常使用量子力学来快速扫描大量假设的材料成分。这样一来，只要预测出所需的特性，就可以开始昂贵的新材料制造过程。然而，量子力学并不能直接帮助科学家理解疾病的生物分子起源，也不能帮助科学家设计新的药物来对抗疾病。对于足以模拟所有需要发生的原子运动的长度和时间尺度，例如，对于药物分子找到其目标，求解（甚至近似）量子力学方程是不可行的。相反，计算化学家使用一个非常简化的计算模型，称为力场，来估计原子的动力学。力场将原子模拟为通过弹簧结合在一起的分子，并通过静电力和货车范德华力与其他原子相互作用，这些力在原子尺度上比重力强得多。这些相互作用的强度由数千个可调参数建模，这些参数经过手动调整以重现数十年来的实验数据。我们正在达到一个停滞点，迫切需要计算机辅助设计新药的准确性，但参数调整只能带来很小的改进。我对UKRI未来领袖奖学金的愿景是建立一个多学科团队，共同努力缩小量子力学与生物学和医学中使用的近似力场之间的准确性差距。通过与国际编码工作的合作，我将建立理论和软件基础设施，以免除这些可调力场参数，而是直接为研究中的系统（如与疾病有关的蛋白质）导出它们。这将使我能够建立更精确的静电和货车德瓦尔斯相互作用的计算模型，这些模型决定了潜在药物与其靶点的结合强度。通过跨越学科界限进行数据科学和机器学习培训，我将部署因其在面部和语音识别中的应用而闻名的专业知识，以创建一系列工具来加快参数分配并提高力场设计的准确性。最后，通过在制药行业进行借调，我将确保开发的方法将用于下一代药物的成本效益设计。

Nobel Laureate Richard Feynman in his Lectures on Physics famously remarked that "...everything that living things do can be understood in terms of the jigglings and wigglings of atoms". This deceptively simple statement highlights the difficulty that structural biologists, medicinal chemists and computational scientists are faced with when attempting to understand human health and disease. We are used to thinking about a static, isolated picture of objects at the atomic scale, but often it is the dynamics (the "jigglings and wigglings") of the system and its environmental interactions that determine the underlying science, such as the role of intrinsically disordered proteins in neurodegenerative diseases or the possible link between quantum entanglement and molecular vibrations in biological photosynthesis.Twentieth century science not only set the challenge of studying life at the level of the structure and dynamics of atoms, but also provided (in theory) the solution, through the laws of quantum mechanics and the famous Schroedinger equation. Quantum mechanics explains the fundamental behaviour of matter at the atomic scale, and smaller. It enables scientists to make predictions about materials that are inaccessible to experiment, such as the structure of solid hydrogen in a star's core. At a more everyday level, quantum mechanics is routinely used by researchers in the microelectronics and renewable energy industries to rapidly scan multitudes of hypothetical materials compositions. In this way, the costly manufacturing process of the new materials need only begin once the desired properties have been predicted.However, quantum mechanics does not directly enable scientists to understand the biomolecular origins of disease, or to design new medicines to combat it. The reason for this comes down to Feynman's statement. It is infeasible to solve (even approximate) equations of quantum mechanics for the length and time scales sufficient to model all of the atomistic movements that need to take place, for example, for a drug molecule to find its target. Instead, computational chemists use a much simplified computational model, known as a force field, to estimate the dynamics of atoms. The force field models the atoms as bonded together in a molecule by springs, and interacting with other atoms through electrostatic and van der Waals forces, which are much stronger than gravity at the atomic scale. The strengths of these interactions are modelled by thousands of adjustable parameters, which have been manually tuned to reproduce experimental data over a period of many decades. We are reaching a stagnation point where accuracy is urgently needed for computer-aided design of new medicines, but parameter tuning delivers only small improvements.My vision for this UKRI Future Leaders Fellowship is to build a multi-disciplinary team that will work together to close the accuracy gap between quantum mechanics, and the approximate force fields used in biology and medicine. By working with international coding efforts, I will build the theory and software infrastructure required to dispense with these adjustable force field parameters, and instead derive them directly for the system under study, such as a protein implicated in disease. This will enable me to build more accurate computational models of the electrostatic and van der Waals interactions that determine the strength of binding of potential drugs to their targets. By crossing disciplinary boundaries to train in data science and machine learning, I will deploy the expertise that has been made famous for its applications in face and speech recognition, to create a spectrum of tools for speeding up the assignment of parameters and improving the accuracy of force field design. Finally, by undertaking secondments in the pharmaceutical industry, I will ensure that the developed methods will be used for the cost efficient design of the next generation of medicines.