Pharma AI & Computational Chemistry5 Structured Units

Machine Learning & AI in Drug Discovery

Machine learning algorithms, chemical descriptor analysis, QSAR modeling, and predictive toxicity for drug discovery.

Unit IUpcoming Curriculum

Foundations of AI & Machine Learning in Healthcare

Introduction to AI terminology, Supervised vs Unsupervised learning in biomedicine, data preprocessing pipelines for pharmaceutical databases, and training-validation splits.

Planned Topics:

AI in Drug Discovery Overview

Types of ML Algorithms (Supervised, Unsupervised, Reinforcement)

Handling Missing Clinical & Chemical Data

Model Evaluation Metrics (Accuracy, Precision, Recall, AUC-ROC)

Unit NotesContent in Roadmap

Study materials and code examples will be released sequentially.

Unit IIUpcoming Curriculum

Molecular Descriptors & Chemical Feature Engineering

Translating chemical structures into machine-readable numerical formats: SMILES strings, Morgan fingerprints, MACCS keys, and physicochemical descriptor extraction using RDKit.

Planned Topics:

SMILES & InChI Structure Representations

2D/3D Molecular Fingerprints (Morgan/ECFP)

RDKit Library for Chemical Informatics

Feature Scaling & Dimensionality Reduction (PCA)

Unit NotesContent in Roadmap

Study materials and code examples will be released sequentially.

Unit IIIUpcoming Curriculum

QSAR & Bioactivity Prediction Models

Building Quantitative Structure-Activity Relationship (QSAR) models using Random Forests, Support Vector Machines (SVM), and Gradient Boosting to predict compound potency (IC50 / EC50).

Planned Topics:

Principles of QSAR Modeling

Regression Models for IC50/pIC50 Prediction

Classification of Active vs Inactive Compounds

Applicability Domain & Cross-Validation

Unit NotesContent in Roadmap

Study materials and code examples will be released sequentially.

Unit IVUpcoming Curriculum

ADMET & Toxicity Prediction with Neural Networks

Predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiles, hERG cardiac toxicity, and blood-brain barrier permeability using deep neural networks.

Planned Topics:

ADMET Property Profiling in Early Discovery

Deep Learning Architectures (MLP, Graph Neural Networks)

Mutagenicity & Carcinogenicity Classifiers

hERG Channel Inhibition Prediction

Unit NotesContent in Roadmap

Study materials and code examples will be released sequentially.

Unit VUpcoming Curriculum

Generative AI & De Novo Molecular Design

Exploration of generative chemistry using Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) for designing novel drug-like molecules against specific disease targets.

Planned Topics:

De Novo Molecular Generation Concepts

SMILES-based RNNs & Transformers

Docking Score Optimization with Reinforcement Learning

Drug-likeness Filters (Lipinski's Rule of 5)

Unit NotesContent in Roadmap

Study materials and code examples will be released sequentially.