C9590 - TARGETED SUSTAINABLE PRODUCTS

Learning outcomes

Students will know the problems of discovering and designing new pharmaceutically active substances. They will know how to apply the existing tools for supporting SSbD. They will know how to use computational approaches for designing new alternative chemicals and forecasting their properties. The student is expected to be able to: 1. Design and discover new drugs; 2. Have a comprehensive knowledge of the existing digital tools for supporting SSbD; 3. Use the principles of structure-properties relationship for designing new products and forecasting their properties.

Course contents

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

Sustainable Drug DesignThis module is focused on: principles of rational drug design; qualitative and quantitative structure-activity relationships (QSAR); structure based drug design: docking; and approaches to predict binding free energy.

Digital methods for developing Safe and Sustainable-by-Design (SSbD) chemicals and materials.

This module is focused on the introduction to SSbD framework and initiatives, by presenting the idea of SSbD; SSbD methodological guidance by the EU Joint Research Centre; SSbD guidance by The European Chemical Industry Council (CEFIC) and ongoing projects focused on further development of SSbD methodology. Moreover, physics-based digital methods: Freely available and commercial tools will be presented including the examples of using Quantum Chemistry and Molecular Dynamics for SSbD. Furthermore, data-driven methods will be intiducted to the Students including freely available and commercial tools and examples of using Machine Learning for SSbD with perspectives for Artificial Intelligence. At the end Student will be familiarized with PARC SSbD Toolbox. Council (CEFIC). Ongoing projects focused on further development of SSbD methodology.
2. Physics-based digital methods: Freely available and commercial tools. Examples of using Quantum Chemistry and Molecular Dynamics for SSbD.
3. Data-driven methods: Freely available and commercial tools. Examples of using Machine Learning for SSbD. Perspectives for Artificial Intelligence.
4. PARC SSbD Toolbox.

Structure Toxicity Relationship.

This module explores the core concepts of Structure-Activity and Structure-Toxicity Relationships through an integrated theoretical and practical approach using open-source software.The curriculum covers foundational chemoinformatics, including 2D and 3D molecular representations,hashed fingerprints,molecular descriptors,and similarity coefficients.Through applied problem-solving,students will master QSAR and QSPR methodologies,enabling them to computationally predict toxicity and design new molecules with tailored physicochemical properties.

Readings/Bibliography

Lecturer notes and slides

Engel, T. (2018) Applied chemo-informatics: achievements and future opportunities. Wiley
Leach, A. (2001) Molecular modelling: principles and applications. Prentice Hall
Schlick, T. (2010) Molecular Modeling and Simulation. Springer

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 (3) will be assessed through individual report and presentation on a topic related to the module content or a written test.

Teaching tools

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

Office hours

See the website of Carolina Estarellas Martin

See the website of Tomasz Puzyn

See the website of Assimo Maris