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所在平台: Udemy |
课程主页: https://www.udemy.com/course/qsar-modeling-principles-and-practice/
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**课程名称：** QSAR建模：原理与实践 **课程概述：** 本课程将深入讲解定量构效关系（QSAR）建模在计算机辅助药物设计（CADD）中的应用。您将掌握自主开发QSAR模型所需的实用技能，为您的职业生涯增添一项宝贵技能，尤其对于药物设计、化学信息学、生物信息学及相关领域的从业者而言。 课程内容涵盖了QSAR建模的关键机器学习概念，并从理论层面阐述了进行QSAR分析所需的化学和统计学知识。在理论基础之后，课程将进入实践环节，引导您利用真实QSAR数据集进行实验。课程将提供所有数据集，方便您跟随实验步骤进行操作。我们将使用一款免费、现代且高效的QSAR建模软件贯穿课程始终。 **实践实验模块：** * **实验一：** 详细分步讲解并实践多元线性回归（MLR）方法构建QSAR模型。 * **实验二：** 探索使用分子指纹和偏最小二乘法（PLS）构建QSAR模型。 * **实验三：** 学习并应用k近邻（kNN）等非线性方法进行QSAR建模。 * **实验四：** 掌握使用自动描述符选择方法进行QSAR模型开发。 * **实验五：** 实践利用QSAR模型进行数据库虚拟筛选。 在每个实践实验中，我们将首先进行理论讲解，然后通过实验进行演示。 **最终环节：** 课程最后部分将讲解QSAR研究的规划、执行和报告撰写，提供QSAR分析的通用指南以及成功发表QSAR研究的技巧，帮助您整合所学理论和实践知识，高效地开展QSAR研究。 **总体目标：** 本课程旨在全面介绍QSAR建模的主题，并赋予您进行QSAR建模研究的实践能力。
In this course you will learn the subject of Quantitative Structure-Activity Relationship (QSAR) modeling in Computer-Aided Drug Design (CADD). You will obtain practical skills to develop QSAR models on your own, which will be a valuable tool and a great addition for your skills set. In particular, for Drug Design, Cheminformatics, Bioinformatics or related fields practitioners. This course also explains key concepts in machine learning, which is involved in developing QSAR models. In the initial sections, the theoretical aspects of QSAR modeling are explained which include the chemical and statistical knowledge required for performing QSAR. Following the initial theoretical sections, the practical sections will involve performing practical QSAR experiments using real QSAR datasets. You will be provided all the datasets so you can follow along the experiments. A free QSAR modeling software will be used throughout the course which is modern and efficient for all the required tasks. In the first experiment, each step in the QSAR modeling process will be explained and performed in details. In the subsequent experiments, new concepts will be introduced including using automatic descriptor selection methods, using non-linear regression algorithms and performing virtual screening with QSAR models. In those practical sections, each concept will be explained theoretically first then it will be demonstrated in a practical experiment. The subjects of the experiments are as follow:Experiment 1: Developing a QSAR model (MLR method) step by step in details.Experiment 2: Developing a QSAR model using fingerprints and the PLS method.Experiment 3: Developing a QSAR model using a non-linear method (kNN).Experiment 4: Developing a QSAR model with automatic descriptor selection methods.Experiment 5: Performing virtual screening on a database using a QSAR model. In the final section, planning, executing and reporting QSAR studies will be explained, and general guidelines for proceeding in QSAR analysis will be given, as well as general tips on how to successfully publish a QSAR study. This will further bring together all the theoretical and practical knowledge obtained previously to give you a clear view and efficiency in performing QSAR studies. Overall, this course is intended to explain the subject of QSAR and grant practical skills for performing QSAR modeling studies.
