QSAR Model to Predict Biological Activity Using ML
From Molecules to Meaning: QSAR Modeling with ML and Orange3
Skills you will gain:
About Program:
A Quantitative Structure-Activity Relationship (QSAR) model can be created using Machine Learning (ML) methods to forecast biological responses by forming a mathematical connection between a molecule’s structure and its activity. This process includes applying ML algorithms to a dataset comprising chemical compounds along with their known biological activities, and then utilizing the established relationship to estimate the activity of new, untested compounds. Orange is an open-source toolkit for data visualization, machine learning, and data mining. It provides a visual programming interface for interactive qualitative data analysis and exploration which is developed by the University of Ljubljana. Orange serves as a visual programming platform for data science and machine learning endeavours. Prediction algorithms based on machine learning, such as random forest and SVM, may be employed and validated using methods like leave-one-out (LOO), random sampling, and cross-validation to assess the biological response. In this workshop, a brief overview of machine learning methods and algorithms is provided, which includes supervised, unsupervised, and reinforcement learning, accompanied by examples. Furthermore, the application of machine learning in QSAR modelling is discussed to provide insights into predicting the biological responses based on a diverse range of physiochemical descriptors.
Aim: The aim is to establish a reliable model that can effectively forecast the biological activity of novel, untested compounds, which could expedite drug discovery and various other applications.
Program Objectives:
- To create a Quantitative Structure-Activity Relationship (QSAR) model utilizing machine learning (ML) techniques to forecast a biological response based on the structure of a chemical compound.
- To identify pertinent molecular descriptors, choosing suitable machine learning algorithms, and thoroughly validating the model’s predictive performance.
What you will learn?
Day 1: Orange3 introduction and Data preparation
- Overview of Orange3 data mining tool
- Overview of machine learning algorithm, Data Loading, Descriptors generation, Preprocess data
Day 2: Machine learning methods and Model building
- Prediction model based on Random Forest, SVM
- Performance evaluation and Cross validation
Day 3: Model application and interpretation
- Results interpretation and Data visualization
- Applications of QSAR in pharmaceutical development
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- B.Tech / B.Sc / M.Sc / M.Tech graduates or final-year students from Pharmaceutical Sciences, Bioinformatics, Cheminformatics, Computational Biology, Chemistry, or allied disciplines
- Professionals and researchers working in Drug Discovery, Toxicology, Medicinal Chemistry, Pharmaceutical R&D, or AI/ML in life sciences
- Enthusiasts and learners interested in QSAR modeling, Predictive Toxicology, Machine Learning, or exploring careers in computational drug design
Career Supporting Skills
Program Outcomes
- Proficiency in creating predictive QSAR models
- Familiarity with Orange3 interface and ML workflow
- Ability to select and apply appropriate algorithms and validation strategies
- Skills to preprocess, analyze, and visualize chemical data
- Capability to translate model predictions into research insights
- Readiness to contribute to AI-driven drug discovery and cheminformatics projects