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Python training artificial intelligence 7

2022-02-02 12:01:07 Maomao 648python Teaching

New drug research and development has a long cycle 、 High cost and low success rate , Artificial intelligence is a hot direction in the field of drug research and development , It has been applied to all stages of drug development , The field of medicine pays more and more attention to artificial intelligence technology , At present, the application of artificial intelligence technology in drug research and development is mainly manifested in seven scenarios , Namely : Target drug development 、 Candidate drug mining 、 Compound screening 、 forecast ADMET nature 、 Drug crystal form prediction 、 Assist in pathobiology research and explore new drug indications , Artificial intelligence can directly contribute to the development of new drugs ,AI+ Compared with the traditional model , Time and cost advantages are obvious .AI+ The combination of drug research and development must be the development trend of the pharmaceutical industry in the future , A great revolution in the field of Medicine , Let the pharmaceutical industry usher in a new era , With the outbreak of COVID-19 , Medical practitioners at home and abroad have set foot in AI Artificial intelligence , Many domestic scientific research institutes, universities and enterprises have established many artificial intelligence drug research institutes , Invest a lot of money

Because at home AI The development of artificial intelligence drug research is slow , There is less literature on the learning platform , Training and learning is imminent , At the request of scientific researchers , After months of research , It is decided to hold a meeting with artificial intelligence medical research experts “AI Drug Discovery & Design Artificial intelligence drug discovery and design ” Special training course , The unit has held four sessions of special training , Participants up to 200 More than one , Highly consistent evaluation of training arrangement and training quality

Trainees

Major universities across the country 、 Enterprises 、 Scientific research institutes are engaged in artificial intelligence 、 Life science 、 protein 、 medicine 、 Microbial Pharmacy 、 Bioinformatics 、 botany , zoology 、 Agriculture and pharmacy 、 Chemical engineering , Medical researchers and AI enthusiasts

Training objectives

Let students master the application background and process of artificial intelligence in drug research , And machine learning , Deep learning 、 Computer aided drug design and other operational skills , Complete your own research project alone

Training features

The lecturer is from domestic universities , Chinese Academy of Sciences and other experts give lectures , Teachers are mainly good at deep learning 、 machine learning 、 Medical information statistics 、 Computer aided drug design 、 Artificial intelligence drug discovery 、 molecular docking 、 Molecular dynamics

Curriculum content

AI Drug Discovery & Design Course schedule of special training on artificial intelligence drug discovery and Design

The first day

Relationship between drug discovery and design and clinical trials

1.1 Drug discovery and new drug R & D process ;

1.2 The difference between preclinical research and development and clinical research and development ;

1.3 FDA History and Prospect of new drug approval ;

History of discovery and design

2.1 History of computer development ;

2.2 The history of artificial intelligence ;

2.3 History of computer aided drug design ;

Introduction and installation of drug discovery and design related software

3.1 Introduction to commercial drug design software .

3.2 Introduction and installation of open source drug design software .

3.3 Python Application of programming in drug discovery and design ;

Basic principles of drug discovery and Design

4.1 Structure based drug discovery and design ;

4.2 Ligand based drug discovery and design ;

4.3 Fragment based drug discovery and design ;

Python Fundamentals of crawler programming and Practice :

5.1 python Introduction to common crawler modules and frameworks urllib, requests, lxml, selenium,scrapy;

5.2 Python Crawler application practice — Crawling ZINC Database compound structure ;

5.3 Python Crawler application practice — Activity data collection of small molecule compounds ;

the second day

Construction of target protein and compound database

6.1 Introduction and construction of target protein database ;

6.2 Construction of polypeptide compound database ;

6.3 Construction of small molecule compound database ;

Target protein and compound data processing

7.1 Target protein active site analysis ;

7.2 Format conversion of small molecule compounds (smiles, sdf, mol2, pdbqt);

7.3 Physicochemical properties of small molecule combination and molecular descriptor derivation calculation ;

Statistical analysis of small molecular compound data

8.1 Small molecule compounds molecular descriptors cluster analysis ;

8.2 Principal component analysis of molecular descriptors of small molecular compounds ;

8.3 Feature selection of small molecule compounds ;

On the third day

Pymol Protein structure software —— From installation to mastery

1.Pymol Installation (windows edition )

2. Protein structure display

3. protein - Small molecule interactions show

4. protein - Small molecule docking animation

5. Amino acid modification

6. Calculate small molecules 5A Less than amino acids

7.MOLMOL Draw a disulfide bond

8.SPDBV

Homologous modeling

1 Functions and usage scenarios of homology modeling

2 Homology modeling method

3. Search for homologous proteins

4. Example explanation and practice SWISS-MODEL Do homology modeling

5. There are homologous sequences ——modeller Homologous modeling :

6.modeller Practice

7. No homologous sequence ——I-TASSER

8. Model evaluation ( Protein Raman diagram )

The fourth day

Ligand based drug discovery and design methods

9.1 Logical regression algorithm theory introduction and example demonstration ;

9.2 Introduction and example demonstration of naive Bayesian algorithm ;

9.3 KNN Algorithm introduction and example demonstration ;

9.4 Introduction and example demonstration of support vector machine algorithm ;

9.5 Introduction and example demonstration of decision tree algorithm ;

9.6 Introduction and example demonstration of random forest algorithm ;

9.7 Gradient lifting tree algorithm and XGBOOST Introduction and example demonstration ;

Fifth day

AutoDock Vina Molecular docking case practice

10.1 Target protein crystal protein PDB obtain ;

10.2 Format transformation of target proteins and small molecules ;

10.3 Search for target protein docking active sites ;

10.4 Preparation and demonstration of molecular docking script ;

10.5 Molecular docking results in PyMOL Display and interpretation of results in ;

10.6 Application Autodock vina Batch screening of small molecular compound drugs ;

Basic principles of drug discovery and Design

11.1 Collection of target proteins ;

11.2 Pretreatment of target proteins ;

11.3 Target protein docking active site analysis ;

11.4 protein - Protein biomacromolecule docking ;

Basic principles of drug discovery and Design

12.1 Pretreatment of peptides ;

12.2 Pretreatment of target proteins ;

12.3 Target protein docking active site analysis ;

12.4 protein - Polypeptide molecular docking ;

Sixth days

Molecular dynamics case demonstration

13.1 Linux Ubuntu16.04 Introduction and practice of system installation ;

13.2 Amber Software installation practice ;

13.3 utilize Autodock Docking small molecules ;

13.4 Explanation and practical implementation of the whole process of molecular dynamics ;

Preparation of protein crystals Preparation of small molecular compounds Molecular docking practice utilize ACPYPE Processing small molecules, productivity fields or topology files Preparation of protein and small molecule complexes Minimize energy The composite system was heated and pressed Molecular dynamics process Display and interpretation of molecular dynamics results

Seventh days

Python Foundation and advancement

14.Python Foundation and advancement

14.1 Python List of basic data structures , aggregate , Tuples , Dictionaries , pandas, numpy, matplotlib Application ;

14.2 Python object-oriented programming ( Classes and objects );

15. Using deep learning method to predict preclinical potentially active or toxic drugs ( For the effectiveness and safety of drugs )

15.1 Common deep learning frameworks tensorflow, pytorch And so on use ;

15.2 Application Python Reptile technology collects data on small molecular compounds ;

15.3 Use drug design software or online open source tools to calculate the physical and chemical properties of small molecules attribute ;

15.4 Application Python Process the data set of small molecular compounds ;

15.5 Application python Establish machine learning model and XGBoost Model, etc ;

15.6 Application python Generate a paper report on the results ;

15.7 Application python Visual analysis of the results ;

15.8 Application case introduction and practice of graph convolution network in pharmaceutical chemistry molecules ;

15.9 Generative countermeasure network and AlphaFold Application in drug discovery and Design ;

Extracurricular value-added benefits ( Give AlphaFpld2 Training video , This content is not explained , There are recorded boutique videos )

AlphaFold2 Protein structure prediction

Overview of protein structure and function .

Protein composition

The structure of proteins

The function of proteins

Websites and methods of common protein structure prediction .

Websites and software commonly used for protein structure prediction

Usage and description of common websites and software

Application of machine learning in protein structure prediction .

Protein structure and acquisition of small molecule drug library

Machine learning accelerates the prediction of small molecule drugs

AlphaFold2 Prediction of protein structure by machine learning model

At present, the best artificial intelligence model for practical protein structure prediction AlphaFold2.

AlphaFold2 Model acquisition and installation

AlphaFold2 Related data acquisition

AlphaFold2 Actual operation of the model

Some case pictures

Teaching time and place

2021.12.11-2021.12.12

(9:00-11:30)---(13:30-17:00)

Online practice

2021.12.14-2021.12.15

(19:00-22:00)

Online practice

2021.12.16-2021.12.17

(19:00-22:00)

Online practice

2021.12.18-2021.12.19

(9:00-11:30)---(13:30-17:00)

Online practice

2021.12.23-2021.12.24

(19:00-22:00)

Online practice

( Up to seven days of training Dry cargo is full. Practice on the computer )

Registration fee

Everyone ¥5880 element ( Including registration fee , Training fee 、 Data fee )

Discount one : Two or more applicants can enjoy 400 Yuan discount

Discount two : Students who sign up for payment in advance + Forward to the circle of friends or to the academic exchange group to enjoy everyone 300 Yuan discount ( Limited to 15 name )

Discount three : To sign up 5 More than people include 5 people , One free training place

For registration fees, you can issue formal reimbursement invoices and provide relevant payment certificates 、 Invitation , Reimbursement invoices can be issued in advance 、 Documents are used for expense reimbursement

Training benefits

Artificial intelligence will be presented if you sign up and pay successfully Python Boutique video and common drug design software installation guidance video ,CADD Excellent course courseware of drug design 、 Students who participate in this course can participate in the later organization of the unit for free “ Artificial intelligence drug discovery and design ” Special training course ( Any issue can )

Way of teaching

Teaching methods and student feedback

Live broadcast through Tencent conference Online , The training adopts Kaimai sharing screen and wechat group to solve doubts , Students and teachers communicate 、 Students communicate with students , After the training, the teacher will solve the problem for a long time , The trainees in the previous training period have highly rated the training quality and teaching methods

 

 

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