Learn QSAR modeling in Computer-Aided Drug Design (CADD) and obtain practical skills for performing QSAR Studies.
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.
What you’ll learn
- Understanding the principles of QSAR modeling in Drug Design..
- Practical skills in developing and validating QSAR models using various methods and techniques..
- Planning, executing and reporting QSAR studies for publication or projects..
- Performing virtual screening using QSAR models..
- Improved Computer-Aided Drug Design (CADD) skills and knowledge such as planning and executing workflows..
Course Content
- Course Overview –> 1 lecture • 6min.
- Introduction –> 2 lectures • 27min.
- Regression Analysis (Theoretical) –> 3 lectures • 41min.
- QSAR Model Development (Practical) –> 6 lectures • 30min.
- QSAR Modeling with PLS (Practical) –> 2 lectures • 17min.
- Non-Linear QSAR Modeling (Practical) –> 2 lectures • 18min.
- Automatic Descriptor Selection (Practical) –> 2 lectures • 35min.
- QSAR-Based Virtual Screening –> 2 lectures • 18min.
- Planning & Reporting QSAR Studies –> 2 lectures • 25min.
Requirements
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.