Artificial Intelligence in Pharmaceutical Industry Course
Duration: 6th months
| Topic | Detail |
| 1st Week | |
| Introduction to Artificial Intelligence |
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| Quiz | Regarding Lecture |
| Assignment | Assign students to research a specific challenge faced by the pharma industry. Students should analyze the challenge, discuss current efforts to resolve it, and suggest how AI could be used to address the challenge. |
| 2nd, 3rd, 4th & 5th Week | |
| Python for AI | |
| Introduction to the Python language | · Overview of Python as a programming language
· Setting up the development environment · Running Python code (using the REPL and writing scripts) |
| Variables, Data Types, and Operators | · Introduction to variables, numbers, strings, lists, dictionaries, etc.
· Explanation of basic operators (arithmetic, comparison, logical, etc.) · Hands-on assignment: Write a program that takes inputs from the user and performs basic calculations |
| Control Structures | · Overview of control structures (if/else, for loops, while loops)
· Explanation of how to use control structures to control the flow of the program · Hands-on assignment: Write a program that implements a simple decision-making algorithm using an if/else statement |
| Functions and Modules | · Overview of functions and modules in Python
· Explanation of how to define and use functions to modularize code · Hands-on assignment: Write a program that implements a simple mathematical function (e.g., finding the factorial of a number) and uses it in a script |
| Quiz | Regarding Lecture |
| Assignment | Coding exercises in each topic as described above |
| 6th, 7th, 8th & 9th Week | |
| Machine Learning with Python | |
| Overview of machine learning and its types (supervised, unsupervised, reinforcement) | · Definition and explanation of supervised learning (linear regression, logistic regression, decision trees, KNN)
· Definition and explanation of unsupervised learning (clustering, dimensionality reduction) · Definition and explanation of reinforcement learning |
| Quiz | Regarding Lecture |
| Assignment | About the algorithms of supervised and unsupervised ML |
| 10th , 11th Week | |
| Introduction to popular Python libraries for machine learning (scikit-learn, TensorFlow, Keras, PyTorch) | · Overview of scikit-learn and its features
· Overview of TensorFlow and its features · Overview of Keras and its features · Overview of PyTorch and its features
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| Evaluation: Evaluate the performance of the models using performance metrics such as accuracy, precision, recall, F1 score, etc. | |
| Quiz | Regarding Lecture |
| Assignment | 1. Regression: Implement a simple linear regression model to predict the target value of a given dataset
2. Classification: Implement a decision tree classifier to predict the class of a given dataset 3. Clustering: Implement the k-means clustering algorithm to cluster the data into groups |
| 12th Week | |
| Machine Learning in the Pharma industry | · Case studies of machine learning applications in drug discovery, such as structure-based virtual screening and ligand-based pharmacophore modeling
· Use of machine learning for toxicity prediction and ADME (Absorption, Distribution, Metabolism, and Excretion) properties of new compounds · Predictive modeling for patient selection in clinical trials and drug efficacy evaluation · Machine learning in pharmacovigilance, such as adverse event prediction and drug safety monitoring |
| Quiz | Regarding Lecture |
| Assignment | Role of ML in the pharma industry |
| 13th, 14th ,15th & 16th Week | |
| Deep Learning with Python | |
| Overview of deep learning | · Overview of deep learning frameworks (Tensorflow, Keras, PyTorch, etc.)
· Importance of Python for deep learning |
| Fundamentals of Neural Networks | · Artificial Neural Networks (ANNs)
· Perceptron model · Activation functions · Backpropagation algorithm |
| Preprocessing Data for Deep Learning | · Importance of data preprocessing
· Techniques for data preprocessing (normalization, one-hot encoding, etc.) · Handling missing values and outlier detection |
| Convolutional Neural Networks (CNNs) | · Understanding convolutions and filters
· Building blocks of CNNs (convolutional layer, pooling layer, etc.) · Transfer learning and fine-tuning pre-trained models |
| 17th Week | |
| Building neural networks using popular Python libraries (TensorFlow, Keras , PyTorch) | |
| Quiz | Regarding Lecture |
| Assignments | 1. Install TensorFlow, Keras, or PyTorch and familiarize yourself with the basic syntax and structure of the framework
2. Implement a simple feedforward neural network in Python to classify a sample dataset (e.g. MNIST or IRIS dataset) 3. Load a sample dataset, preprocess the data (normalize, one-hot encoding, etc.), and split into training and testing sets 4. Train a simple CNN on a sample image dataset (e.g. CIFAR-10 or MNIST) and evaluate its performance |
| 18th Week | |
| Deep Learning in the pharma industry | Case studies of computer vision applications in the pharmaceutical industry, such as high-throughput screening and image-based personalized medicine. Image classification models for cell analysis, drug discovery, and drug efficacy prediction. Computer vision-based models for patient diagnosis and treatment response prediction |
| 19th ,20th ,21st , 22nd Week | |
| NLP in python | |
| Overview of NLP in python | 1. Overview of NLP and its techniques
2. Text preprocessing and feature extraction 3. Text classification, sentiment analysis 4. Hands-on coding exercises with Python libraries such as NLTK, spaCy |
| Quiz | Regarding Lecture |
| Assignment | Hands-on coding exercises in Python to implement various NLP techniques |
| 23rd Week | |
| Application of NLP in pharma industry | 1. Use of NLP for drug labeling and package insert analysis
2. Text mining of scientific literature for drug discovery and repurposing 3. Sentiment analysis of social media data for market analysis and forecasting 4. NLP for pharmacovigilance, such as adverse event classification and signal detection |
| 24th Week | |
| Ethical, Legal, and Regulatory Aspects of AI in Pharmaceuticals
Emerging Trends and Future of AI in Pharmaceuticals |
• Data privacy and security
• Intellectual property and patent law • Ethical considerations and responsible AI
• Advancements in AI technologies • Integration of AI with other technologies • prospects of AI in the pharmaceutical industry |
| Assignment | Teams of students will work on a simple project related to AI in the pharmaceutical industry |
| End of Course | |
