32657 - INTELLECTUAL PROPERTY

Learning outcomes

Students will understand how artificial intelingence tools can be used in chemical applications and understand the concerns regarding intelectual property in chemical innovation, as well as the need to defend against industrial forgery. The students are expected to be able to: 1. Understand and apply machine learning, deep learnign and articificial intelligence in a chemistry context; 2. Understand and apply an array of chemical and physical methods to detect industry forgery; 3. Understand the basic principles of IP right protection dealing with molecular materials and to decide the best IP strategy, from publication to patenting.

Course contents

The CU is composed of three modules with the following contents.

Patenting New Products

This module aims to: describe the most relevant aspects of intellectual property fundamentals; discuss publication, secrecy and patenting: three alternative routes to address IP protection; describe the patenting process; illustrate several concrete case studies of active pharmaceutical ingredients patenting and litigations; address the issue of crystal forms, polymorphism, hydrates, and co-crystals, the frontier of IP strategies in the pharma industry.
Summary of its contents:
1. Fundamentals the most important aspects of intellectual property rights when dealing with organic molecular crystals (pharmaceuticals, pigments, agrochemicals, imaging materials).
2. The claim structure and examples of patents based on organic materials, active pharmaceutical ingredients or materials for imaging.
3. Hydrates interconversion; most accessible analytical tools, diffraction and thermodynamic techniques
4. Spectroscopic techniques (with applications to solid state principles).

Industrial Forgery Detection

Introduction to the problems related with industrial forgery, namely those related with health security, industrial and intellectual property.
Some of the more relevant analytical technics used on detection of industrial forgery will be presented, with special emphasis on the use of the analytical results in a forensic context.

Machine Learning, Deep Learning, and Artificial Intelligence in Chemistry: Basics and applications

1. Introduction to ML, DL, and AI in chemistry: overview of ML, DL, and AI, their role in modern chemistry, and key differences between them.
2. Data in chemistry: types of chemical data, data preprocessing, and data quality issues.
3. Machine learning basics: supervised, unsupervised, and reinforcement learning approaches; regression, classification, clustering, and dimensionality reduction techniques.
4. Fundamentals of deep learning: neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) and their applications in chemistry.
5. Applications of ML/DL in chemistry: prediction of molecular properties (e.g., toxicity, solubility), materials discovery, and drug design.
6. AI tools and platforms: introduction to common tools used in AI.
7. Case studies in chemistry: practical examples of ML, DL, and AI applied to material/drug discovery, catalysis, quantum chemistry, etc.
8. Ethical considerations: ethical concerns in ML, DL, and AI, data privacy and reproducibility in scientific research.

Readings/Bibliography

Lecturer notes and slides

• "Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More"; Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande; O'Reilly; 2019.
• "Machine Learning in Chemistry: The Impact of Artificial Intelligence"; Ed.: Hugh M. Cartwright; Royal Society of Chemistry; 2020.
• Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. Machine Learning for Molecular and Materials Science. Nature (2018), 559(7715), 547-555.

Teaching methods

The course unit is divided into three modules taught independently at different times in the academic year, each module is organized in theoretical classes where main concepts are introduced, as well as tutorial classes with discussion of case-study examples.

Assessment methods

Each module learning is evaluated independently, exploiting: i) written tests; ii) oral presentations or interviews; iii) written assignments or combinations of them. The Course Unit grade will be the arithmetic mean of grades from the three modules. ChIRS grades scale goes from 1 to 100, pass grade is
40, and will be translated into ECTS and different University scales. Criteria: knowledge on a very limited number of topics covered in the course and analytical ability that emerges only with the help of the instructor, using generally correct language → 40-45;
Knowledge on a limited number of topics covered in the course and independent analytical ability only on purely executive
issues, using correct language → 45-60;
Knowledge on a large number of topics covered in the course, ability to make independent critical analysis choices, mastery
of specific terminology → 60-80;
Essentially comprehensive knowledge on the topics covered in the course, ability to make independent critical analysis and
connection choices, full mastery of specific terminology, and ability for argumentation and self-reflection → 80-100.

Module III: scientific report that solves a real-world challenge requiring the use of selected ML, DL, and/or AI. A literature review on the application of ML/DL/AI techniques and/or tools to real-world chemistry problems may also be included.

Teaching tools

Lectures slides and notes will be available on the course moodle https://emmcchir-learning.ualg.pt

Office hours

See the website of Jose Antonio De Sousa Moreira

See the website of Maria De Lurdes Dos Santos Cristiano

See the website of