MASTER AI for Drug Discovery

AI for Drug Discovery Master Program — Curriculum Overview

The curriculum is structured to ensure clear conceptual separation between modules, avoiding overlap by assigning each topic a unique functional role within the drug discovery pipeline. Each unit includes its Scope and a short Description, intended to be refined by the assigned instructors.

Total hours 264
Modules 6
Units 27
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Next lesson 18 September 2026 Professor Di Biasi Module 5 - 5.1

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MASTER AI for Drug Discovery

Program Overview & Module Details

The curriculum is structured to ensure clear conceptual separation between modules, avoiding overlap by assigning each topic a unique functional role within the drug discovery pipeline. Each unit includes its Scope and a short Description, intended to be refined by the assigned instructors.

Total hours 264
Modules 6
Units 26
Updated 2026-01-21

Module 1 — Fundamentals of Drug Discovery

Core conceptual foundations for the pipeline: physicochemical intuition, thermodynamic interpretation, medicinal chemistry constraints, and pathway-level pharmacology.

1.1 Introduction to AI for Drug Discovery & Physical Chemistry for Drug Discovery

High-level orientation and physicochemical intuition.

9 h

Scope

High-level orientation and physicochemical intuition.

Description

This unit introduces the end-to-end drug discovery pipeline (target identification, hit discovery, lead optimization, preclinical development), highlighting where and why AI-based methods are impactful. The AI component is intentionally conceptual and non-algorithmic, focusing on use cases (virtual screening, QSAR, generative design, repurposing) rather than methods. The physical chemistry component provides an operational understanding of key molecular properties (pKa, logP/logD, solubility, polarity, lipophilicity) and their qualitative impact on permeability, exposure, and developability. Emphasis is placed on interpreting property trends rather than deriving equations.

Stefano Piotto (UNISA)
26 January 2026 Done
23 February 2026 Done
27 March 2026 Done
2 April 2026 Done

1.2 Physical Chemistry: Thermodynamics and Molecular Interactions

Quantitative interpretation of molecular recognition.

6 h

Scope

Quantitative interpretation of molecular recognition.

Description

This unit formalizes molecular interactions using thermodynamic principles. Free energy of binding is decomposed into enthalpic and entropic contributions, with explicit discussion of solvent effects, conformational penalties, and enthalpy–entropy compensation. The goal is to enable students to interpret affinity changes observed in modeling or experiments, not to perform simulations.

Stefano Leoni (Cardiff University)
5 March 2026 Done

1.3 Medicinal Chemistry: Drug-likeness and ADMET; Lipinski Rules and Molecular Descriptors

Multi-parameter optimization and chemical design rules.

18 h

Scope

Multi-parameter optimization and chemical design rules.

Description

This extended unit covers drug-likeness criteria across chemical space, including Rule-of-Five and beyond-Ro5 compounds. ADMET properties (absorption, metabolic stability, clearance, toxicity) are discussed in relation to structural features. Molecular descriptors and fingerprints are introduced strictly as chemical abstractions supporting SAR reasoning, not as ML features (covered later). Case studies illustrate trade-offs between potency and developability.

Marco Ostacolo (UNISA)
3 April 2026 Done
9 April 2026 Done
17 April 2026 Done
23 April 2026 Done
8 May 2026 Done
15 May 2026 Done

1.4 Pharmacology: Signaling Pathways

Biological context for target modulation.

12 h

Scope

Biological context for target modulation.

Description

This unit introduces the pharmacokinetic and pharmacodynamic principles required to interpret drug response, including exposure, bioavailability, clearance, target engagement, and dose–response relationships. It also presents the main receptor classes and their associated signaling outputs, providing the biological and pharmacological basis for understanding how target modulation translates into efficacy and adverse effects.

Raffaella Belvedere (UNISA)
20 March 2026 Done
1 April 2026 Done
22 April 2026 Done
22 May 2026 Done
28 May 2026 Done
11 June 2026 Done

Module 2 — AI for Drug Repurposing

From target hypotheses to mechanistic interpretation and translational strategy for repurposing.

2.1 Target Identification and Validation: Omics, CRISPR, Chemogenomics

Generation of target hypotheses.

9 h

Scope

Generation of target hypotheses.

Description

This unit focuses on identifying and prioritizing targets using omics data, functional genomics (CRISPR screens), and chemogenomic relationships. The emphasis is on interpreting experimental and computational evidence to justify target selection, rather than on downstream drug design.

Alessandra Tosco (UNISA)
10 June 2026 Done
24 June 2026 Done

2.2 Pharmacology: Receptor–Ligand Interactions and Drug Mechanisms

Mechanistic interpretation of drug action, including receptor and enzyme interactions..

9 h

Scope

Mechanistic interpretation of drug action.

Description

This unit introduces the fundamental principles of pharmacodynamics, including drug–receptor interactions, dose–response relationships, and agonism/antagonism. It explores receptor types, enzyme–ligand interactions, and the molecular forces governing binding. The course also covers basic signal transduction pathways, enzyme mechanisms and inhibition, and provides an introduction to structure–activity relationships (SAR) to understand how molecular structure influences biological activity.

Gianluigi Lauro (UNISA)
16 April 2026 Done
14 May 2026 Done
4 June 2026 Done
16 June 2026Done
29 June 2026Done

2.3 Network Pharmacology: Gene–Gene Interactions

Systems-level integration of targets and pathways.

9 h

Scope

Systems-level integration of targets and pathways.

Description

Here, biological systems are modeled as networks to contextualize drug effects beyond single targets. Concepts such as disease modules, network proximity, and pathway crosstalk are introduced. This unit explicitly builds on (but does not repeat) pathway biology from Module 1 and target identification from Module 2.1.

Stefano Piotto (UNISA)

2.4 Computational and Translational Approaches to Drug Repurposing and Clinical Development

Translation from computational hypotheses to clinical strategy.

9 h

Scope

Translation from computational hypotheses to clinical strategy.

Description

This unit integrates molecular, biological, and clinical evidence to evaluate repurposing opportunities. Topics include real-world data, clinical trial design considerations, patient stratification, and regulatory constraints. The emphasis is on decision-making and risk assessment rather than algorithm development.

Harald HHW Schmidt (Maastricht University)

2.5 AI Applications in Structural Biology

From structural hypotheses to systems-level biomedical insight

6 h

Scope

Translation from AI-based structural hypotheses to biological interpretation and research planning.

Description

This unit introduces AI-driven methods used to generate and interpret structural hypotheses in biomedical research. Topics include protein structure and complex prediction, binding-site characterization, docking and scoring with learned potentials, molecular representation learning (sequence/structure/graph), and AI-assisted analysis of cryo-EM, X-ray crystallography, and SAXS data. Students will also explore how structural biology interfaces with systems medicine through network-informed target prioritization and mechanism-of-action inference. Practical activities may include reproducing reference pipelines, critical evaluation of model uncertainty and bias, and defining research mini-projects suitable for internships or thesis work.

Sona Vasudevan (Georgetown University)

Module 3 — Statistics and Computational Methods

Statistical validity, classical ML evaluation, and foundational computational concepts for drug discovery data.

3.1 Biomedical Statistics and Data Preprocessing

Statistical validity and data integrity.

12 h

Scope

Statistical validity and data integrity.

Description

This unit covers essential statistical concepts that are useful to process drug discovery data sets, including: fundamentals of descriptive statistics and data distributions, data exploration and visualization, hypothesis testing, error probabilities, confidence intervals, data preparation, and sampling.

Marco Carpentiero (UNISA)
26 June 2026Done
3 July 2026Done
20 July 2026Done
27 July 2026Done

3.2 Supervised and Unsupervised Machine Learning Methods

Classical ML modeling and evaluation.

18 h

Scope

Classical ML modeling and evaluation.

Description

This unit introduces supervised (regression, classification) and unsupervised (clustering, dimensionality reduction) ML methods commonly used in cheminformatics. Emphasis is placed on model selection, cross-validation, performance metrics, and error analysis. All evaluation methodology is confined to this unit to avoid repetition elsewhere.

Luigi Di Biasi (UNISA)
6 March 2026Done
19 March 2026Done
28 April 2026Done
22 July 2026Done

3.3 Structural Bioinformatics

Structural data interpretation.

6 h

Scope

Structural data interpretation.

Description

This unit focuses on protein and RNA structure quality, binding site identification, and structural variability. The objective is to enable critical assessment of structural inputs used later in modeling and simulation modules.

Lucia Sessa (SoftMining srl)
13 March 2026Done

3.4 Data Integration and Ontologies

Semantic integration of heterogeneous data.

9 h

Scope

Semantic integration of heterogeneous data.

Description

This unit addresses how diverse biomedical datasets are harmonized using ontologies and standardized identifiers. Emphasis is on data consistency, provenance, and interpretability rather than numerical processing.

Stefano Piotto (UNISA)

Module 4 — Molecular Modeling and Simulation

Structure-based workflows, dynamics, quantum/classical methods, and affinity/QSAR modeling.

4.1 Computational Modeling: Docking, Scoring, and Molecular Dynamics

End-to-end structure-based modeling workflows.

15 h

Scope

End-to-end structure-based modeling workflows.

Description

This unit introduces core computational modeling approaches used in drug discovery, including molecular docking, scoring functions, and basic molecular dynamics simulations. The focus is on ligand–target interaction analysis, pose evaluation, and the interpretation of simulation results to support structure-based reasoning.

Gianluigi Lauro (UNISA)
2 July 2026Done
13 July 2026Done
14 July 2026Done

4.2 Advanced Molecular Dynamics

Sampling quality and uncertainty.

9 h

Scope

Sampling quality and uncertainty.

Description

This unit extends MD concepts to enhanced sampling methods, convergence analysis, and advanced trajectory interpretation. It is explicitly separated from basic MD to avoid repetition.

Stefano Leoni (Cardiff University)
29 April 2026Done

4.3 QM/MM Theory and Applications in RNA Systems

Quantum-level modeling of biomolecular processes.

12 h

Scope

Quantum-level modeling of biomolecular processes.

Description

This unit introduces QM/MM theory with applications to RNA recognition and catalysis, highlighting when quantum effects are essential and how to interpret results critically.

Giulia Palermo (UCLA)
1 September 2026Done
4 September 2026Done
7 September 2026next

4.4 Binding Affinity Prediction

Prediction and uncertainty of binding strength.

6 h

Scope

Prediction and uncertainty of binding strength.

Description

This unit focuses on comparing physics-based and ML-based affinity prediction approaches, with emphasis on ranking reliability, error sources, and applicability domains.

Giulia Palermo (UCLA)
1 September 2026Done

4.5 QSAR Modeling

Statistical modeling of structure–activity relationships.

9 h

Scope

Statistical modeling of structure–activity relationships.

Description

This unit presents QSAR workflows, including dataset curation, descriptor selection, model validation, and interpretability. QSAR is treated as a statistically grounded methodology distinct from modern deep learning.

Sk. Abdul Amin (UNISA)
5 June 2026Done
19 June 2026Done
9 July 2026Done

Module 5 — Advanced AI Techniques

Neural representation learning, generative design, robust data engineering, and interpretability for regulated settings.

5.1 Deep Learning and Graph Neural Networks for Molecular Modeling

Representation learning for molecules.

12 h

Scope

Representation learning for molecules.

Description

This unit introduces neural representations of molecules, focusing on graph-based models and their chemical interpretation. Evaluation concepts are assumed from Module 3 and not repeated.

Luigi Di Biasi (UNISA)

5.2 Generative Models and Transformer-based Optimization for Molecular Design

De novo molecular generation and optimization.

12 h

Scope

De novo molecular generation and optimization.

Description

This unit covers generative AI methods for molecule design, emphasizing constraint handling, multi-objective optimization, and chemical validity.

Arkadeep Sarkar (SoftMining srl)
10 July 2026Done
17 July 2026Done
21 July 2026Done

5.3 Data Engineering for AI Pipelines in Drug Discovery

Reproducible and scalable AI workflows.

9 h

Scope

Reproducible and scalable AI workflows.

Description

This unit addresses pipeline orchestration, data versioning, experiment tracking, and reproducibility, clearly separated from data preprocessing (Module 3.1).

Sk. Abdul Amin (UNISA)

5.4 Explainable AI for Clinical Processes and Drug Discovery

Interpretability and trust.

12 h

Scope

Interpretability and trust.

Description

This unit focuses on explainability techniques, their limitations, and their role in regulatory and clinical contexts.

Christopher Banerji (King’s college of London/Alan Turing Insitute)

Module 6 — Industrial Applications and Case Studies

Industrial practice, governance, and commercialization topics complementing the technical curriculum.

6.1 Industrial Case Study: AI in Pharma

Real-world end-to-end industrial case studies.

9 h

Scope

Real-world end-to-end industrial case studies.

Description

Real-world end-to-end case studies illustrating integration of AI into pharmaceutical R&D.

Andrea Perez Villa (Bayer)

6.2 IP Management and Innovation

Intellectual property strategy for AI-driven discovery.

6 h

Scope

Intellectual property strategy for AI-driven discovery.

Description

Foundations of intellectual property strategy in AI-driven drug discovery.

Barend Bouma (European Patent Attorney Pharma & Life Sciences) & Bart de Leeuw (European Patent Attorney Software & AI)
23 June 2026Done
30 June 2026Done

6.3 Data Security in AI-based Discovery

Governance and compliance for sensitive biomedical data.

6 h

Scope

Governance and compliance for sensitive biomedical data.

Description

Governance, compliance, and secure handling of sensitive biomedical data.

Domenico De Luca (Tolemaica)

6.4 Federated Learning and Privacy in Drug Discovery

Collaborative learning under privacy constraints.

9 h

Scope

Collaborative learning under privacy constraints.

Description

Collaborative learning under data privacy constraints, with pharmaceutical use cases.

Luigi Di Biasi (UNISA)

6.5 AI Startups and Commercialization

Translation of AI technologies into ventures.

6 h

Scope

Translation of AI technologies into ventures.

Description

Translation of AI technologies into sustainable biotech and pharma ventures.

Dr. Matteo Marino (Marino engineering srl)

Master's Program Professors

Foto Stefano Piotto
Stefano Piotto
University of Salerno / SoftMining srl
Foto Stefano Leoni
Stefano Leoni
Cardiff University
Foto Raffaella Belvedere
Raffaella Belvedere
University of Salerno
Foto SK Abdul Amin
SK Abdul Amin
Department of Pharmacy - University of Salerno
Foto Luigi Di Biasi
Luigi Di Biasi
University of Salerno
Foto Lucia Sessa
Lucia Sessa
University of Salerno / SoftMining srl
Foto Gianluigi Lauro
Gianluigi Lauro
University of Salerno
Foto Carmine Ostacolo
Carmine Ostacolo
University of Salerno
Foto Alessandra Tosco
Alessandra Tosco
University of Salerno
Foto Harald HHW Schmidt
Harald HHW Schmidt
Maastricht University
Foto Sona Vasudevan
Sona Vasudevan
Georgetown University
Foto Giulia Palermo
Giulia Palermo
UCLA
Foto Arkadeep Sarkar
Arkadeep Sarkar
SoftMining srl
Foto Christopher Banerji
Christopher Banerji
King’s College London / Alan Turing Institute
Foto Barend Bouma e Bart de Leeuw
Barend Bouma
European Patent Attorneys
Foto Barend Bouma e Bart de Leeuw
Bart de Leeuw
European Patent Attorneys
Foto Domenico De Luca
Domenico De Luca
Tolemaica
Foto Matteo Marino
Matteo Marino
Marino engineering srl
Foto Matteo Marino
Marco Carpentiero
University of Salerno