Basic introduction to molecular modeling, chemoinformatics and data mining to predict chemical and biological properties of molecules of pharmaceutical interest. 1. Knowledge and understanding. At the end of the course the student will know the basic principles of molecular modelling and chemoinformatics as well as computational tools and software used in the chemical and pharmaceutical research. 2. Ability to apply Knowledge and understanding. At the end of the course the student will be able to choose the proper methods and software to support different projects in the field of chemistry or medicinal chemistry. 3. Autonomy of judgement. The student will be able to interpret in silico data from his/her own calculations, and to address their validity. 4. Communication skills. The student will use a technical language to describe his/her in silico experiments. 5. Learning skills. The student will be able to plan and realize an in silico experiment, and to present and discuss its results.

The course proposes a set of basic contents ranging different fields: 1. Types of molecular data. Methods and software for molecules and macromolecules representation, to extract data from open access databases, and to visualize molecular 3D structure. 2. Molecular description. Different types of molecular descriptors: molecular fingerprints, topological descriptors, 3D-descriptors; QSAR and 3D-QSAR techniques (Quantitative Structure-Activity Relationships). 3. Use of molecular descriptors to predict chemical properties or biological activity. Predictive methods: development and use of different statistical models (unsupervised methods for pattern recognition as well as supervised methods for properties prediction), validation methods, applicability domain of the models. 4. Ligand-target interaction simulation. Basic principles of molecular mechanics, to understand the internal energy of molecular conformations and the inter-molecular interactions. Simulations of the ligand-target binding by means of Molecular Interaction Fields, docking and brief introduction to molecular dynamics. 5. Artificial intelligence. Artificial Intelligence case studies on drug design topics.

Slides for the lessons, material provided by the teacher and manuals and tutorials for the software used. The Moodle platform will be used for sharing the materials.

The course offers a comprehensive introduction to various fundamental topics, in the chemoinformatics field.

It begins with the exploration of different types of molecular data, including methods and software for representing molecules and macromolecules, extracting data from open access databases, and visualizing molecular 3D structures.

Then, the course delves into molecular description, examining various types of molecular descriptors such as molecular fingerprints, topological descriptors, and 3D-descriptors, along with QSAR and 3D-QSAR techniques (Quantitative Structure-Activity Relationships).

The course continues by addressing the use of molecular descriptors to predict chemical properties or biological activities. It presents predictive methods that involve developing and utilizing different statistical models, including unsupervised methods for pattern recognition and supervised methods for properties prediction, as well as validation techniques and the applicability domain of these models.

Another significant topic covered is the simulation of ligand-target interactions, introducing basic principles of molecular mechanics to understand the internal energy of molecular conformations and inter-molecular interactions. This includes simulations of ligand-target binding through Molecular Interaction Fields, docking, and molecular dynamics.

Finally, the course explores the applications of artificial intelligence in drug design, providing case studies that demonstrate the role and impact of AI methodologies in this field.

Theoretical lessons integrated with “hands-on” sessions, in which every student will work on a dedicated computer to run calculations and simulations. In at least one lesson students will use virtual reality 3D headsets. Any changes to the methods described here, necessary to ensure the application of safety protocols related to potential emergency situations, will be communicated on the Department's website, the Study Program's website, and the MS Teams and Moodle pages of the course.

Realize a project work with real data, by applying the key concepts learnt during the course. Illustration of the obtained results will be part of the oral exam. The student will choose his/her project among a list of proposals, or will propose his/her own, in line with the course contents. Examples of case studies are given below: i) Given a list of molecules and one or more protein targets of pharmaceutical interest, extract from public databases biological data and perform a preliminary study to distinguish active from inactive molecules. A wide range of projects will arise from different targets and sets of molecules. ii) Given a biological target with a known binding site, use docking tools to propose the binding mode of a set of analogues with known activity data, and discuss a proposal for structure-activity relationships (SAR) data. iii) Given a biological target with a known binding site and a library of candidates, develop a virtual screening procedure to select a bunch of candidates with the desired profile. The project work will include the following sections: Data, Methods, Results, Discussion and conclusions. To realize the project work, the Computer-Aided Drug Design research group will provide for the appropriate hardware and software resources. During the oral exam, the student will illustrate the results of the case study, even with the help of dedicated materials (slides, images, video). After the exposition, the exam will continue with oral questions about the key concepts of the course. Alternatively, upon students' request, the exam may be taken as either a written test (multiple-choice questions) or an oral exam, covering the topics addressed during the course. Any changes to the methods described here, necessary to ensure the application of safety protocols related to potential emergency situations, will be communicated on the Department's website, the Study Program's website, and the MS Teams and Moodle pages of the course.

The basic knowledge on chemo-informatics, given by course, allows to work in the field "Good health and well-being". Chemo-informatics systems are often used on preliminary research to reduce tests on animals.