Declaration

This note is based on the article, “Structure-Based Virtual Screening Using Phase”¹, and created with the Schrödinger Software Release 2023-4.

This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.

Introduction

This tutorial demonstrates the creation, validation, and application of pharmacophore hypotheses to recognize common protein-ligand interactions and use them in virtual screening. You will learn how to create a pharmacophore hypothesis using a protein-ligand complex, how to modify a pharmacophore hypothesis to bias by experimental observables and to screen against the hypothesis to identify Leukotriene-A4 hydrolase inhibitors.

Declaration

This note is based on the article, “Ligand-Based Virtual Screening Using Phase”¹, and created with the Schrödinger Software Release 2023-4.

This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.

Introduction

This tutorial demonstrates the creation of pharmacophore hypotheses from both congeneric and diverse ligands sets. You will learn how to create a pharmacophore hypothesis from a congeneric set of ligands with known experimental binding affinity. Additionally, you will learn how to create a Phase Database from a set of ligands, and use it to both prepare and filter a ligand library for future ligand screens or docking. Lastly, you will screen a Phase Database against a set of hypotheses, generate a pharmacophore hypothesis from a diverse ligand set, and visualize the binding modes.

Declaration

This tutorial is based on the Schrödinger Product Documentation, “Field-based QSAR”¹, and created with the Schrödinger Software Release 2023-4.

This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.

Copying the Field-Based QSAR Exercise Files

1. Use the following link to download the zip archive that contains the tutorial files: https://content.schrodinger.com/quick_start_guide/current/field_qsar.zip

2. Unzip the files into your working directory.

3. Choose File → Change Working Directory in Maestro to set the working directory to where you unzipped the files, if needed.

Declaration

This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.

Introduction

Glide is a Schrödinger module that performs ligand-receptor docking reliably. To run a Glide virtual screen, you need a grid file and a ligand file. The grid file is typically generated from a prepared protein (using Protein Preparation Workflow and Receptor Grid Generation), and the ligand file is processed by LigPrep.

Prepare the Protein Using the Protein Preparation Workflow

RESOURCES

Docker Desktop is available for Mac, Linux and Windows https://docs.docker.com/desktop/

Install Docker Engine on Linux https://docs.docker.com/engine/install/

View example projects that use Docker https://github.com/docker/awesome-compose/

Check out our docs for information on using Docker https://docs.docker.com/

What is discussed in this question?

During the process of research writing, we often need to cite certain references. With the aid of reference management tools, we can efficiently modify the style of citations to meet the requirements of various publishers. However, I'm puzzled about whether the in-text citation should be positioned before or after the final punctuation mark.

Before we delve into this question, there are some definitions that need to be clarified. The process of citing references generally involves two parts—in-text citations and the bibliography.

The bibliography, also called reference lists, a list of all sources used in your research, is typically placed at the end of the text, as below^[1]:

References

1. Berezin, M. Y. & Achilefu, S. Fluorescence lifetime measurements and biological imaging. Chem. Rev. 110, 2641–2684 (2010).
2. Kandori, H., Katsuta, Y., Ito, M. & Sasabe, H. Femtosecond fluorescence study of the rhodopsin chromophore in solution. J. Am. Chem. Soc. 117, 2669–2670 (1995).
3. Baba, M., Li, Y. & Matsuoka, M. Intensity interference of ultrashort pulsed fluorescence. Phys. Rev. Lett. 76, 4697–4700 (1996).
4. Muskens,O. L., Giannini, V., Sánchez-Gil, J. A. & Rivas, J. G. ómez Strong enhancement of the radiative decay rate of emitters by single plasmonic nanoantennas. Nano Lett. 7, 2871–2875 (2007). ...
5. McGlynn, J. A., Wu, N. & Schultz, K. M. Multiple particle tracking microrheological characterization: fundamentals, emerging techniques and applications. J. Appl. Phys. 127, 201101 (2020).
6. Ghosh, A., Karedla, N., Thiele, JanChristoph, Gregor, I. & Enderlein, J. örg. Fluorescence lifetime correlation spectroscopy: Basics and applications. Methods 140–141, 32–39 (2018).
7. Newville, M., Stensitzki, T., Allen, D. B. & Ingargiola, A. LMFIT: nonlinear least-square minimization and curve-fitting for Python. https://doi.org/10.5281/zenodo.11813 (2023).

Introduction

What is Anaconda Distribution?

Anaconda® Distribution is a free Python/R data science distribution that contains:

• conda - a package and environment manager for your command line interface
• Anaconda Navigator - a desktop application built on conda, with options to launch other development applications from your managed environments
• 250 automatically-installed packages that work well together out of the box
• access to the Anaconda Public Repository, with 8000 open-source data science and machine learning packages

Anaconda Distribution is free, easy to install, and offers free community support.

1 关于 AutoDock

AutoDock 是一个预测小分子（如底物或候选药物）如何与已知 3D 结构的受体结合的分子对接工具。目前主要有 AutoDock 4 和 AutoDock Vina 两种发行版本，其中 AutoDock 4 还有一个 GPU 加速版本——AutoDock-GPU。¹

AutoDock 4 进行分子对接时由两部分组成：

1. autogrid 计算受体的 Grid；

2. autodock 执行配体与靶蛋白 Grid 的对接。

注：AutoDock Vina 不需要选择原子类型及预先计算 Grid，Grid 在执行对接时计算，因此能够用于虚拟筛选（批量对接）；且 AutoDock Vina 相对 AutoDock 4 拥有更高的精度，更加推荐使用 AutoDock Vina。

本文是南开大学孙宏伟老师《量子化学与分子力学/分子模拟》课程 Gaussian 实操部分的笔记，孙老师这部分的授课内容主要基于《Exploring Chemistry with Electronic Structure Methods》第二版和第三版中的实例，这本书也被称为“高斯圣经”，非常具有代表性。我在孙老师的授课基础上，同时也参考了此书和其它的一些资料，尽可能从解决实际问题的角度出发，整理整篇笔记。

1 Gaussian 文件

1.1 Gaussian 输入文件结构

Gaussian 的输入文件后缀名为“.gjf”，下面以示例的“e2_01.gjf”为例：

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%chk=e2_01
# RHF/6-31G(d) sp

Formaldehyde Single Point Energy

0 1
C   0.   0.   0.
O   0.  1.22  0.
H  .94  -.54  0.
H -.94  -.54  0.

1 前言

  根据量子力学理论，波函数能够给出一个系统系全部物理量的信息，因此只要求解薛定谔方程（Schrödinger equation）就能够准确预测整个体系的各种性质。但是对于多体系来说，计算过于复杂，准确求解薛定谔方程几乎不可能。密度泛函理论（Density-functional theory , DFT ）正是为了求解薛定谔方程而发展的一种近似方法，通过近似和变换，能够较为准确地求解薛定谔方程，使理论化学计算成为可能。 [1]

  开始之前，首先引入泛函（Functional）的概念。泛函是函数的函数（A functional is a function of a function），即 F[f]，其中 f(x) 也是一个函数。密度泛函从字面意思来讲就是，将薛定谔方程表达为电子密度的泛函。

2 Born–Oppenheimer 近似

  对于一个多体系，既包含了原子核又包括了电子。考虑到原子核的质量要比电子大很多，Born–Oppenheimer 近似（Born–Oppenheimer approximation）认为原子核相对于电子接近静止，可以将原子核变量从薛定谔方程中分离。薛定谔方程简化成关于电子变量的函数： \[ {\displaystyle {\hat {H}}\Psi =\left[{\hat {T}}+{\hat {V}}+{\hat {U}}\right]\Psi =\left[\sum _{i=1}^{N}\left(-{\frac {\hbar ^{2}}{2m_{i}}}\nabla _{i}^{2}\right)+\sum _{i=1}^{N}V(\mathbf {r} _{i})+\sum _{i<j}^{N}U\left(\mathbf {r} _{i},\mathbf {r} _{j}\right)\right]\Psi =E\Psi ,} \]