Quantitative Chemical Structure and Activity Relationship
A.Y. 2020/2021
Learning objectives
The purpose of this course is that participants gain knowledge on and understand:
- the computational strategies for modelling targets, responsible for biological activity and toxicity, simulating both their interaction with xenobiotics or biotechnological products and their molecular recognition mechanisms at an atomistic level;
- methods to predict and validate the mechanism of action (MoA) of xenobiotics and biotechnological products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the principal physicochemical properties of xenobiotics of relevance to health risk assessment;
- the accuracy of in silico approaches used in scientific studies and risk assessment reports.
- the computational strategies for modelling targets, responsible for biological activity and toxicity, simulating both their interaction with xenobiotics or biotechnological products and their molecular recognition mechanisms at an atomistic level;
- methods to predict and validate the mechanism of action (MoA) of xenobiotics and biotechnological products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the principal physicochemical properties of xenobiotics of relevance to health risk assessment;
- the accuracy of in silico approaches used in scientific studies and risk assessment reports.
Expected learning outcomes
At the end of the course, the student is expected to know:
- the application of the computational methods used in toxicological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used in risk assessment reports;
to gain:
- the bases for deeply understanding methods and results of a toxicological paper and/or data reports;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
- the application of the computational methods used in toxicological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used in risk assessment reports;
to gain:
- the bases for deeply understanding methods and results of a toxicological paper and/or data reports;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Lectures and tutorials will take place remotely using Microsoft Teams and leaving recording on the Stream channel. The activation of synchronous open classes is foreseen every time the students need to discuss each other and to have clarifications and insights from teachers. Students are expected to attend the live class sessions whenever possible, with a microphone and video camera so they can participate in class discussions. Class time will also be used for hands-on exercises based on topics and concepts covered during that session. The structural bioinformatics labs will take place remotely, in synchronous mode, with Microsoft Teams, making the modeling software available on the students' computers.
This organization will be discussed with students during the first lesson and modified if necessary.
This organization will be discussed with students during the first lesson and modified if necessary.
Prerequisites for admission
The course requires the knowledge of base notions of mathematics, chemistry, biochemistry and molecular biology, which are needed for a proficient comprehension of the lessons.
Assessment methods and Criteria
The examination will include a short report based on a relevant paper from the technical literature to discuss how topics covered in class are addressed and applied in the paper for the In silico methods in toxicology module, and of an oral discussion about two different topics of the Structural bioinformatics module.
In Silico Methods in Toxicology
Course syllabus
The module in silico methods in toxicology is aimed at providing students with the fundamental concepts of molecular modelling and a collection of representative in silico approaches playing an increasingly important role in toxicology. Advantages deriving from a complementary use of experimental and in silico techniques will be deeply discussed during the course.
1. Introductions and overview of chemoinformatics and computational toxicology
2. Chemical structure representation (SMILES, MOL, INChI, etc)
3. Structure fingerprints, pairwise similarity
4. Molecular and physicochemical properties
5. Chemical database searching and retrieval; exact, substructure, and similarity searches
6. Humble beginning of QSAR and computational tox endpoints
7. QSAR and Rules for Skin Sensitization and AOP; Skin Permeability
8. QSAR and Rules for Bacterial Reverse Mutagenicity
9. Fundamentals of Read-Across
10. Chemical Safety Assessment - Future perspectives, comp toxin the world of artificial intelligence
11. Discussion and course wrap up
1. Introductions and overview of chemoinformatics and computational toxicology
2. Chemical structure representation (SMILES, MOL, INChI, etc)
3. Structure fingerprints, pairwise similarity
4. Molecular and physicochemical properties
5. Chemical database searching and retrieval; exact, substructure, and similarity searches
6. Humble beginning of QSAR and computational tox endpoints
7. QSAR and Rules for Skin Sensitization and AOP; Skin Permeability
8. QSAR and Rules for Bacterial Reverse Mutagenicity
9. Fundamentals of Read-Across
10. Chemical Safety Assessment - Future perspectives, comp toxin the world of artificial intelligence
11. Discussion and course wrap up
Teaching methods
Frontal lessons. Class time will also be used for hands-on exercises based on topics and concepts covered during that session.
Teaching Resources
No textbook is required for this module. The instructors will, however, provide a list of recommended books, papers, and other materials that students may wish to explore for more in-depth coverage of particular topics.
Structural bioinformatics
Course syllabus
The module Structural bioinformatics is aimed at depicting a landscape of the most up-to-date bioinformatics techniques for the study of the biopolymers (nucleic acids and proteins) that are the main targets of xenobiotics. Students will be introduced to the theoretical bases of the most used computational approaches (4 CFU). Bioinformatics laboratories, totalling 16 hours (1 CFU), will be provided together with lectures, and will be carried out by the Molecular Operating Environment (MOE, http://www.chemcomp.com/MOE-Molecular_Operating_Environment.htm) fully integrated drug discovery software package, kindly offered by the Chemical Computing Group (http://www.chemcomp.com/index.htm).
Frontal lessons (4 CFU)
1. Introduction to bioinformatics
2. Genome organization and evolution
3. Databases, archives and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database search and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading and ab initio approaches
7. Case studies: chimeric comparative modelling of a receptor/target; distant homology modelling of an enzyme
8. Xenobiotic and novel drug risk assessment and prioritization
9. Case study: evaluation of endocrine active substances through an efficient in silico pipeline
10. Introduction to systems biology
Bioinformatics laboratories (1 CFU)
1. Building small molecules, biopolymers and introducing their PTMs
2. Protein comparative modelling
3. Molecular docking
4. Quantitative structure-activity relationship in practice
Frontal lessons (4 CFU)
1. Introduction to bioinformatics
2. Genome organization and evolution
3. Databases, archives and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database search and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading and ab initio approaches
7. Case studies: chimeric comparative modelling of a receptor/target; distant homology modelling of an enzyme
8. Xenobiotic and novel drug risk assessment and prioritization
9. Case study: evaluation of endocrine active substances through an efficient in silico pipeline
10. Introduction to systems biology
Bioinformatics laboratories (1 CFU)
1. Building small molecules, biopolymers and introducing their PTMs
2. Protein comparative modelling
3. Molecular docking
4. Quantitative structure-activity relationship in practice
Teaching methods
Frontal lessons and practical laboratories to be carried out through computational software provided to students by teacher and tutor.
Teaching Resources
Arthur M. Lesk, Introduction to Bioinformatics, Fifth Edition. Oxford University Press 2019
In Silico Methods in Toxicology
CHIM/08 - PHARMACEUTICAL CHEMISTRY - University credits: 5
Lectures: 40 hours
Professor:
Rathman James Flinn
Structural bioinformatics
BIO/10 - BIOCHEMISTRY - University credits: 5
Individual laboratory activities: 16 hours
Lectures: 32 hours
Lectures: 32 hours
Professor:
Eberini Ivano
Professor(s)
Reception:
On Mondays, Wednesdays and Fridays from 9 to 10 am and on appointment previously taken via Microsoft Teams or email
Microsoft Teams