Data Analytics and AI Drug Development

Introduction to the Course

Program Objectives

• Understand the drug development pipeline and where data analytics and AI create value
• Learn about biomedical data types used in AI-driven drug discovery and development
• Gain hands-on experience in preparing, analyzing, and modeling drug development data
• Build proficiency in key applications such as target prioritization, virtual screening, QSAR modeling, and ADMET prediction
• Explore the role of AI in clinical trials, including patient stratification, endpoint prediction, and trial optimization
• Develop a portfolio-ready mini project relevant to real-world drug development

What Will You Learn (Modules)

Module 1: Drug Development Data Landscape

• Pipeline overview: discovery → preclinical → clinical → approval → lifecycle.
• Data sources: omics, assays, chemistry, EHR/RWD, imaging, PV, manufacturing.
• Common challenges: missing data, bias, batch effects, noise, governance.
• KPIs: time, cost, success probability, decision quality.

Module 2: Analytics & AI Fundamentals (Practical)

• Supervised/unsupervised learning; features/labels; overfitting.
• Model types: regression, trees, clustering, neural networks (high-level).
• Metrics: AUC, precision/recall, calibration, RMSE; error analysis.
• Data prep: cleaning, imbalance, leakage prevention, train/test split.

Module 3: AI in Discovery (Targets to Leads)

• Target ID: multi-omics and network/knowledge graph concepts.
• Virtual screening: docking support + ML scoring (overview).
• QSAR/ADMET: property prediction and early risk flags.
• Generative design: molecule generation concepts + validation needs.

Module 4: Preclinical Analytics

• Assay analytics: normalization, dose-response curves, hit calling concepts.
• Toxicity prediction: in silico screening logic and limitations.
• Biomarkers: feature selection concepts, signal vs noise.
• Study reports: reproducible analysis and QC checklists.

Module 5: Clinical Trials Analytics

• Trial feasibility: site selection, cohort sizing concepts.
• Patient stratification: risk models, subgroup analysis (intro).
• Endpoints and missingness: practical data handling concepts.
• Real-world evidence: confounding awareness and validation mindset.

Module 6: Pharmacovigilance & Medical Monitoring

• Safety data basics: case intake, coding, duplicate detection concepts.
• Signal detection: disproportionality concepts and triage workflows.
• NLP support: literature triage and case narratives (with human review).
• Reporting: documentation and audit readiness basics.

Module 7: Manufacturing, Quality & Supply Chain Analytics

• PAT/SCADA data concepts; batch monitoring and drift detection.
• Anomaly detection for deviations and OOS/OOT signals (intro).
• Quality-by-Design (QbD): CQAs/CPPs and data links (overview).
• Forecasting: demand/inventory optimization use cases.

Module 8: Validation, Compliance & Responsible AI

• Model validation: performance, robustness, explainability needs by use case.
• Bias and generalization: population shift and model drift.
• Privacy/security: handling sensitive health data (high-level).
• Monitoring: dashboards, alerts, change control, documentation.

Final Project

• Pick one stage (discovery/preclinical/clinical/PV/manufacturing).
• Define problem, data, model approach, KPIs, validation plan, risks.
• Deliverables: solution canvas + workflow diagram + KPI dashboard + governance checklist.

Who Should Take This Course?

This course is ideal for:

• Pharma & biotech professionals in R&D, clinical research, data teams, regulatory, or medical affairs
• Students (UG/PG) in biotechnology, pharmacy, bioinformatics, chemistry, and life sciences
• Researchers working on omics, drug discovery, ADMET, biomarkers, or clinical datasets
• Data/AI professionals entering life sciences and health-focused analytics
• Career switchers aiming to move into AI-driven pharma, biotech, and clinical analytics
• Enthusiasts interested in AI for drug discovery and translational research

Job Opportunities

Graduates of this course will be well-equipped for roles such as:

• AI Drug Discovery Scientist: Applying AI and predictive models to identify novel compounds.

• Pharmaceutical Data Scientist: Managing and analyzing clinical and biological datasets.

• Clinical Informatics Specialist: Using AI to optimize trials and predict patient outcomes.

• AI Bioinformatics Analyst: Integrating multi-omics data for precision medicine.

• Drug Safety Analyst: Monitoring adverse effects and ensuring pharmacovigilance.

Why Learn With Nanoschool?

At Nanoschool, you will receive expert-led training that combines AI theory with hands-on pharmaceutical applications. Key benefits include:

• Expert-Led Training: Learn from instructors with deep expertise in AI and pharmaceutical sciences.

• Hands-On Learning: Work with real-world datasets, AI platforms, and bioinformatics tools.

• Industry-Relevant Curriculum: Stay updated on the latest AI breakthroughs in drug development.

• Career Support: Receive guidance, mentoring, and placement assistance in pharmaceutical and biotech industries.

Key outcomes of the course

After completing the course, you will be able to:

• Use AI to accelerate drug discovery and optimize clinical trial outcomes.

• Develop predictive models for drug efficacy, toxicity, and patient response.

• Implement AI solutions for personalized medicine and precision therapeutics.

• Analyze pharmaceutical data for actionable insights and regulatory compliance.

• Contribute to AI-driven innovation in pharmaceutical companies, biotech startups, and healthcare research institutions.

Enroll now and discover how AI and data analytics are transforming the future of drug development. Learn to harness cutting-edge AI tools to make drug discovery faster, safer, and more effective.