Improved MCMC Methods for Molecular Simulation

改进的分子模拟 MCMC 方法

• 批准号: 2734035
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2734035
• 关键词: Improved; MCMC; Methods; Molecular; Simulation

We aim at answering questions such as what is the probability that a drug molecule binds a target protein? The molecule equilibrium probability is given by the well-known Boltzmann distribution. Existing methods based on molecular dynamics struggle to generate accurate and independent samples from Boltzmann distributions, especially in high dimensions. We will address these limitations by using advances in deep learning, probabilistic modelling and approximate inference. We will use an initial flexible proposal distribution given by a normalising flow. Samples from the flow will then be used to initialise a fixed-length Markov chain whose output will be improved samples with less bias. This will result in independent samples. To eliminate any bias in the sampling procedure, we will use importance sampling to weight the samples using the flow density. To reduce variance in the weights we will use 1) Hamiltonian Annealed Importance Sampling (HAIS) and 2) alpha divergence methods, which minimise weight variance when alpha = 2. We will produce new deep learning based methods for molecule simulation. Such methods will contribute to pushing the state of the outcomes art in the modelling and simulation of molecular processes, with applications to drug design.

我们的目标是回答这样的问题，如药物分子结合靶蛋白的概率是多少？分子平衡几率由著名的玻尔兹曼分布给出。基于分子动力学的现有方法很难从玻尔兹曼分布中生成精确和独立的样本，特别是在高维中。我们将利用深度学习、概率建模和近似推理的进步来解决这些限制。我们将使用由标准化流程给出的初始灵活提案分布。然后，来自流的样本将用于初始化固定长度的马尔可夫链，其输出将是具有更少偏差的改进样本。这将产生独立样本。为了消除采样过程中的任何偏差，我们将使用重要性采样来使用流量密度对样本进行加权。为了减少权重的方差，我们将使用1）哈密尔顿退火重要性采样（HAIS）和2）α发散方法，当α = 2时，最小化权重方差。我们将为分子模拟提供新的基于深度学习的方法。这些方法将有助于推动分子过程建模和模拟的最新成果，并将其应用于药物设计。