Earthquake in Drug Discovery: AlphaFold3 and Molecular Docking Breakthrough Solves Century-Old Challenge in New Drug Development
Isomorphic Labs
AI Drug Developer
May 8, 2024, GoogleDeepMindJointly published with Isomorphic Labs in the journal *Nature* the latest AI model AlphaFold 3 in the field of proteins! This model can accurately predict the structures of biomolecules such as proteins, DNA, RNA, and ligands, as well as their interaction patterns. This follows...Another Major Breakthrough After AlphaFold 2
In predicting drug interactions, AlphaFold 3 has achieved unprecedented accuracy, including the binding of proteins with ligands and antibodies with their target proteins. In the PoseBusters benchmark test,AlphaFold 3 is 50% more accurate than the best existing traditional methods, and it requires no structural information input, making it the first artificial intelligence system to surpass traditional physics-based prediction tools.This ability to predict antibody-protein binding is crucial for understanding various aspects of the human immune response and designing new antibodies.
Frontier Research on Deep Learning in Protein Design: Deep learning's cutting-edge research in the field of protein design mainly focuses on protein structure prediction, protein sequence design, protein-protein interaction prediction, protein function annotation, and protein optimization and screening. These research directions provide new ideas for the development of novel functional proteins and drug targets, and have significant implications in biomedicine, drug discovery, and biomaterials. The application of deep learning in protein design is considered one of the current frontier research areas, and its continuous development brings many innovations and opportunities to fields such as new drug development, biopharmaceuticals, and life science research.
The Three Most Anticipated Technologies in 2024
1. Deep Learning Protein Design
2. CADD Computer-Aided Drug Design
echnology
Deep Learning in the Field of Protein DesignFrontier ResearchMainly focused on protein structure prediction, protein sequence design, protein-protein interaction prediction, protein function annotation, and protein optimization and screening. These research directions provide new ideas for the development of novel functional proteins and drug targets, and are profoundly applied in biomedicine, drug discovery, and biomaterials. The application of deep learning in the field of protein design is considered one of the cutting-edge research directions at present. The future of protein structure prediction and design will be full of innovation and interdisciplinary advancements, offering more possibilities to address significant challenges in biomedicine, bioengineering, and bioenergy.
Top journals published in recent years and their directions:
Nature Communications | Protein Design Using Structure-Based Residue Preferences
Nature biotechnology| Machine Learning for Functional Protein Design
Scientific reports| Deep-WET: A Deep Learning-Based Approach Using Word Embedding Technology with Weighted Features to Predict DNA-Binding Proteins
Cell Systems| Deep Learning Opens a New Era for Protein Design
Nat. Comput. Sci| Rotamer-Free Protein Design Based on Deep Learning
Comput Struct Biotech| Deep Learning for Protein Design: From Structure to Sequence and Function
This course focuses on the fundamentals and cutting-edge advancements in protein design, offering in-depth instruction ranging from protein structure prediction and optimization to de novo protein design. The course is primarily aimed at participants with a programming background, providing detailed explanations of foundational knowledge while incorporating the application of relevant technologies through discussions of frontier literature. By the end of this training, participants will gain an understanding of the underlying logic and basic principles of protein design, acquire hands-on experience with common protein design algorithms, and develop fundamental skills for algorithm development in protein design along with a forward-looking perspective.
Deep Learning Protein Design Curriculum
Day 1: Concepts and Foundations of Deep Learning in Protein Design
1. Basic Concepts
a. What is Deep Learning
b. What is Protein Design c. python Introduction
d. inux WithVS code Introduction
2. How to describe the state of a protein
a. Quantum Mechanics: Time-dependent Schrödinger equation
b. Quantum Chemistry: Time-independent Schrödinger Equation
c. All-Atom Molecular Dynamics Simulation: Langevin Equation
d. Coarse-grained simulation: Generalized Langevin Equation e. Markov State Model: Master Equation
3. Commonly Used Analysis/Methods for Visualizing Proteins and Related Molecules
a. Obtain and observe a protein sequence MSA
b. Usepymol OrchimeraX Visualizing Protein Molecular Systems
c. Generation and Optimization of Small Molecule Structures: rdkit WithGAMESS
d. Visualization of Molecular Dynamics Simulation Trajectories: VMD
e. General Sequence/Structural Analysis Software Package: biopython
f. Biomacromolecule Editing Platform: Discovery Studio
g. Site Conflict Analysis: Frustratomete
h. Simple Biomolecular Cavity and Channel Analysis: CAIN
4. Differences Between Deep Learning Protein Design and Traditional Protein Design
a. The Essence of Deep Learning
b. Traditional Methods: Inferring Probability through Physical Energy
c. Deep Learning: Directly find the规律 of probability distribution from the data
d. Deep learning still has a close connection with traditional physical methods.
e. The Power of Deep Learning in Protein Design
f. The Waterloo of Functional Protein Design: Boltzmann Ruins
5. Structural Modelvs Language Model
a. Structural Model: Based on Graph Neural Network
b. Language Model: As Close As Neighbors
c. Advantages and Disadvantages of Structural Models
d. Advantages and Disadvantages of Language Models
e. Towards Convergence
Day 2: Pioneer in Protein Design: Protein Structure Prediction
1. InAlphaFold How people used to predict protein structures
a. Based on Physical Energy Function: rosetta
b. Traditional Molecular Docking
c. Molecular Dynamics Simulation
d. Homologous Sequence Analysis
2. AlphaFold Series History
a. AlphaFold： Open a new era
b. AlphaFold2： End of an Era
c. AlphaFold3： Towards the Future
3. AlphaFold3 Principle Analysis
a. Not essentially from sequence to structure, But fromMSA To Structure
b. Attention Mechanism
c. Diffusion Model
d. AlphaFold3 Achievements and Shortcomings
4. AlphaFold Practical Operation and Result Analysis
a.AlphaFold2 Practical Operation
b.AlphaFold2 Analysis
c.AlphaFold3 Practical Operation
d.AlphaFold3 Analysis
e.Don't Ignore Information Beyond the Structure
5.Introduction to Other Deep Learning Protein Structure Prediction Software
a.trRosetta
b.OmegaFold
c.ESMFold
Day 3: Fundamentals of Protein Design: From Classical ForcesField to DepthLearning
1. How to calculate the energy of protein conformations?
a) Common Methods for Protein Visualization and Editing
i. Brief Introduction to PyMOL Usage
ii. Brief Introduction to the Use of Chimera
iii. Detailed Explanation of the PDB File Format
iv. Editing protein structures using Python libraries such as Biopython and PyMOL
b) Introduction to Molecular Mechanics and Solvation Energy
i. Molecular Mechanics Formula Format
ii. Calculation Method of Solvation Energy
iii. MM/PBSA Method for Calculating Binding Free Energy
2. Protein Design Method Based on Statistical Potential Functions——Rosetta
a) General definition of statistical potential function
b) Statistical potential functions in protein design
i. Rosetta Statistical Potential Definition
ii. Common Terms and Physical Significance of the Rosetta Energy Function
c) Protein design based on Rosetta potential function
i. Design Process
ii. Experimental Results
3. The Power of Protein-Protein Docking – A Protein Drug Design Pipeline Without Prior Knowledge
a) Introduction to Protein-Protein Docking
i. Definition of Protein-Protein Docking
ii. Introduction to RifGen Docking Method
b) Protein Drug Design
i. Design Process
ii. Experimental Results
4. Deep Learning Takes the Stage —— Protein Design Model ProteinMPNN
a) Introduction to MPNN Message Passing Neural Networks
b) Introduction to ProteinMPNN Model
i. Model Structure Introduction (Input, Output, Parameters...)
ii. Model Usage (Primary Programming Language: Python)
c) Protein design based on ProteinMPNN
i. Design Process
ii. Experimental Results
5. Equal or Worlds Apart? A Comparison of Two Protein Design Methods
a) Deep learning models have a higher sequence recovery rate.
b) Deep learning models can achieve design tasks that rosetta and alphafold cannot complete.
c) Shortcomings of Deep Learning Models
Day Four: Deep Learning Protein Backbone Design
1.Why take the road of designing the backbone first and then designing the sequence
2.Traditional Protein Backbone Design
a.Simple and brute-force structural splicing
b.Rationally mutate amino acids to achieve the desired outcomeStructure
c.RosettaRemodel Introduction
3.Protein Backbone Design Model Based on Optimized Energy: SCUBA
a.SCUBA The basic principle is actually quite simple.
b.Core Difficulties and Overcoming Methods
c.SCUBA Actual Operation
d. Shortcomings
4. Protein Backbone Design Model Based on Diffusion Model
a.Rewrite the TimesRFdiffusion
b. Combined withGAN ofSCUBA-D
c.Combined withVQ ofPVQD
5.The Actual Backbone Design Process from Scratch: A Case Study of Binding Protein Design from Scratch
a.Selection of Functional Pockets
b.Skeleton Generation with Constraints
c.Iterative Optimization
d. Special Means
Day 5: Downstream Applications Based on Alphafold
1. Key Issues and Solutions in AF2 Multimeric Protein Structure Prediction
1.1 Sequence Concatenation Pairing Problem in Multiple Sequence Alignment
1.2 Template Matching Problem
2. Using AF2 for Flexible Docking of Proteins and Peptides
2.1 Geometric and Physicochemical Complementarity of Protein Surfaces
2.3 Peptide Flexibility/Conformation Handling
3. Using AF2 for New Protein Structure and Sequence Design
3.1 trrosetta Fantasy Design
3.2 AF2 Sequence and Structural Fantasy Design
4. Using AF2 for structural clustering to discover new structures and functions
4.1 Introduction and Analysis of Alphadatabase Database Structure
4.2 Introduction to Foldseek Structural Alignment Tool
4.3 New Structure and New Function
5. Using AF2 for Multi-Conformation Prediction and Function Discovery
5.1 MSA Sampling Clustering Analysis and Structure Prediction
5.2 Different MSAs Can Predict Transitions and Functions Between Conformations
6. Utilizing partial algorithm modules of AF2 for model quality assessment, side-chain conformation, etc.
6.1 Triangular Mechanism for Enhancing Protein Model Quality Assessment
6.2 Local Triangular Mechanism and Evoformer for Protein Side Chain Prediction
Day 5: Deep Learning Protein Sequence Design
1. Traditional Protein Sequence Design
a.Based on Force Field
b. Based on Homologous Sequences
c. Towards Data-Driven: ABACUS WithABACUS2
d. ABACUS2 Actual Operation
2. A Masterpiece That Changed an Era: ProteinMPNN
a. InGNN Before: Based onCNN Sequence Design
b.ProteinMPNN Framework Analysis
c.ProteinMPNN The widespread application
d.ProteinMPNN Actual Operation
e. Potential Issues
3. Other Sequence Design Models
a. ESM-IF Introduction
b. ABACUS-R Introduction
c. ABACUS-R Actual Operation
4.MagicalCarbonDesign
a.Subject toAlphaFold Inspiration
b.CarbonDesign Framework Analysis
c.CarbonDesign Reasons for Success
d. However, a high sequence recovery rate does not equate to functional protein sequence.High Success Rate in Column Design
5.The Nightmare of Structure-Based Sequence Design: Intrinsic Disorder Region
a. What are intrinsically disordered regions, Why do intrinsic disordered regions exist
b. Intrinsic disorder regions are crucial for protein function.
c. The Core Reason for Poor Design of Intrinsically Disordered Region Sequences
d. Current Solution
Day 6: Frontiers in Deep Learning for Protein Design
1. Functional Protein Design Based on Ligand Molecules
a. Framework Design: RFdiffusionAllAtoms
b. Sequence Design: LigandMPNN
2. Structure-Sequential Collaborative Design
a. Why Theoretically Collaborative Design is Superior to Traditional Design
b.Challenges of Collaborative Design
c.Current Progress in Collaborative Design
3. Dynamic Protein Structure Prediction and Design
4.KAN In the latent space of deep learning for protein designIn application
a. Explainability of Machine Learning
b.Physics-Informed Machine Learning
c.Kolmogorov-Arnold Representation Theorem
d.KAN vs MLP
e.KAN Potential Advantages
5.How to avoid Boltzmann ruins is the core issue
6.Looking Ahead
a.De Novo Enzyme Design Based on Chemical Reaction Mechanisms
b.Find the spatial distribution of functional proteinsextropy
Structural Prediction Basis:Students will learn to analyze protein sequences using bioinformatics tools, predict their secondary and tertiary structures, and understand the relationship between structure and function.
Model Application and Evaluation:Students will be able to use machine learning and deep learning models for protein structure prediction, while learning how to evaluate the accuracy and reliability of the models and select the appropriate tools for application.
Drug Design:Master the principles of drug design related to protein structure prediction, learn to design drug molecules and peptide drug molecules targeting specific proteins, and understand the mechanisms of protein-drug target interactions.
What you will gain through the course
General-purpose Protein Design Model Based on Deep LearningIn recent years, this course has developed rapidly, focusing on the basics and cutting-edge work of protein design. It provides in-depth teaching from protein structure prediction and optimization to de novo protein design. Starting from scratch, this course explains the foundational knowledge in detail and combines it with the latest literature to explain the application of related technologies. This helps the students,Through this training, participants will understand the underlying logic and basic rules of protein design, master the practical operation of common protein design algorithms, and gain foundational skills for developing protein design algorithms as well as a forward-looking perspective.
Teaching Structure
1. Course Features -- Comprehensive coverage of course technology applications, principles and processes, and practical examples throughout.
2. Learning Mode -- Combining theoretical knowledge with hands-on operation, enabling beginners to quickly master proficiency.
3. Course Service Q&A -- The main lecturer will provide professional answers to the problems you encounter in your actual work.
Course Q&A, Student Reviews, and Materials
The teacher patiently answers every trainee's questions, with nearly 2000 Q&A sessions each training.
Nearly 300 pages of PPTs along with corresponding code, providing software installation and guidance. Preview videos will be sent upon successful registration for pre-study.
Introduction of the Lecturer
The lecturer comes from a TOP university in China and is engaged in artificial intelligence protein design research within a top professor group in China. The current main research focus is on the design of enzymes and binding proteins, as well as protein design using artificial intelligence on an evolutionary scale. They have rich practical experience. Publications have appeared in Applied Physics Letters.eLife, Nucleic Acids Research, ACS Omega, Journal of Molecular Biology and other internationally renowned journals have published several works.
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CADD Computer-Aided Drug Design
Course Objectives
Science Technology
CADD Computer-Aided Drug Design:Based on the research achievements in life sciences such as biochemistry, enzymology, molecular biology, and genetics, and using computational chemistry as a foundation, this method involves simulating, calculating, and predicting the interactions between drugs and receptor biomacromolecules through computer modeling. It examines structural complementarity and property complementarity between drugs and targets to design rational drug molecules. This is a method for designing and optimizing lead compounds, widely applied especially in food, biology, chemistry, medicine, plants, and diseases! The discovery and validation of targets are the first steps in modern new drug development and also one of the bottlenecks in the new drug creation process.
Computer-Aided Drug Design (computer aided drug design) is a method based on computational chemistry that designs and optimizes lead compounds by simulating, calculating, and predicting the interactions between drugs and receptor biomacromolecules through the use of computers. Computer-aided drug design essentially optimizes and designs lead compounds by simulating and calculating the interactions between receptors and ligands. Computer-aided drug design generally includes active site analysis, database searching, and de novo drug design.
CADD Computer-Aided Drug Design Course Schedule
Day 1Morning
Background, Theoretical Knowledge, and Tool Preparation
1. Introduction and Use of the PDB Database
1.1 Introduction to the Database
1.2 Query and Selection of Target Protein Structures
1.3 Download of Target Protein Structure Sequence
1.4 Download and Preprocessing of Target Proteins
1.5 Batch Download Protein Crystal Structures
2. Introduction and Usage of Pymol
2.1 Introduction to Basic Software Operations and Fundamental Knowledge
2.2 Protein-Ligand Interaction Diagram
2.3 Protein-Ligand Small Molecule Surface Diagram, Electrostatic Potential Representation
2.4 Protein-Ligand Structure Superposition and Alignment
2.5 Plotting Interaction Forces
3.Introduction and Use of Notepad
3.1 Introduction to Advantages and Main Functions
3.2 Interface and Basic Operations Introduction
3.3 Plugin Installation and Usage
Afternoon
General Protein
-Ligand Molecular Docking Explanation
1. Introduction to Relevant Theories of Docking
1.1 The Concept and Basic Principles of Molecular Docking
1.2 Basic Methods of Molecular Docking
1.3 Commonly Used Software for Molecular Docking
1.4 General Process of Molecular Docking
2. Conventional Protein-Ligand Docking
2.1 Collection of Receptor and Ligand Molecules
2.2 Processing of Complex Pre-conformations
2.3 Preparation of Receptor and Ligand Molecules
2.4 Protein-Ligand Docking
2.5 Analysis of Docking Results
Taking the main protease of the新冠病毒 protein and related inhibitors as an example
The Next Day
Virtual Screening
1. Introduction and Download of Small Molecule Database
2. Introduction to Related Programs
2.1 Introduction and Usage of OpenBabel
2.2 Introduction and Usage of ChemDraw
3. Preprocessing for Virtual Screening
4. The Process and Practical Demonstration of Virtual Screening
Case: Screening for Main Protease Inhibitors of SARS-CoV-2
5. Result Analysis and Plotting
6. Drug ADME Prediction
6.1 Introduction to ADME Concepts
6.2 Introduction to Relevant Websites and Software for Prediction
6.3 Analysis of Prediction Results
Day Three
Methods for Expanding Docking Usage
1.Protein-Protein Docking
1.1 Application Scenarios of Protein-Protein Docking
1.2 Introduction to Related Programs
1.3 Collection and Preprocessing of Target Proteins
1.4 Calculation Using Examples
1.5 Preset of Key Residues
1.6 Acquisition of Results and File Types
1.7 Analysis of the Results
With the current popular target
PD-1/PD-L1, etc.
2. Docking involving metalloenzyme proteins
2.1 Background Introduction of Metalloenzyme Protein-Ligand
2.2 Collection and Preprocessing of Proteins and Ligand Molecules
2.3 Treatment of Metal Ions
2.4 Docking of Metal Cofactor Protein-Ligand
2.5 Result Analysis
Taking human farnesyltransferase and its inhibitors as an example
3. Protein-polysaccharide molecular docking
4.1 Protein-Glycosaminoglycan Interactions
4.2 Key Points of Docking Processing
4.3 The Process of Protein-Polysaccharide Molecular Docking
4.4 Protein-Polysaccharide Molecular Docking
4.5 Analysis of Related Results
Taking α-glucosidase and polysaccharide molecular docking as examples
5. Nucleic Acid-Small Molecule Docking
5.1 Application Status of Nucleic Acid-Small Molecules
5.2 Introduction to Related Procedures
5.3 Types of Nucleic Acid-Small Molecule Binding
5.4 Nucleic Acid-Small Molecule Docking
5.5 Analysis of Related Results
Human Telomere
g - Quadruple chain and ligand molecular docking as an example.
Introduction to Operation Process and Practical Demonstration
Day Four
Extended Docking Usage Methods
1.Flexible Docking
1.1 Introduction to the Use Cases of Flexible Docking
1.2 Advantages of Flexible Docking
1.3 Protein-Ligand Flexible Docking
Focus: Method for setting flexible residues
1.4 Analysis of Related Results
Cyclin-dependent kinase
2 (CDK2) with ligand 1CK as an example
2. Covalent Docking
2.1 Introduction to Two Covalent Docking Methods
2.1.1 Flexible Side Chain Method
2.1.2 Two-Point Attractor Method
2.2 Collection and Preprocessing of Proteins and Ligands
2.3 Covalent Docking of Covalent Drug Molecules with Target Proteins
2.4 Comparison of Results
Taking the currently popular covalent drugs for COVID-19 as an example.
3. Protein-Hydration Docking
3.1 The Significance and Methods of Hydration in Protein-Ligand Interactions
3.2 Collection and Preprocessing of Proteins and Ligands
3.3 Preparation of Relevant Parameters for Docking
Key Point: The Addition and Treatment of Water Molecules
3.4 Protein-Water-Ligand Docking
3.5 Result Analysis
Acetylcholine-binding protein
(AChBP) with nicotine complex as an example
Day 5
Molecular Dynamics Simulation (Linux and GROMACS Installation and Usage)
1. Introduction and Simple Usage of Linux System
1.1 Common Linux Command Lines
1.2 Common Program Installation on Linux
1.3 Experience: How to Perform Virtual Screening on Linux
2. Introduction to Molecular Dynamics Theory
2.1 Principles of Molecular Dynamics Simulation
2.2 Methods and Related Programs of Molecular Dynamics Simulation
2.3 Introduction to Related Force Fields
3. Introduction and Usage of Gromacs
Focus: Introduction to Main Commands and Parameters
4. Introduction and Use of Origin
Day Six
Execution of Solvated Molecular Dynamics Simulations
1. General Workflow for Handling Solvated Proteins
2. Preparation of Protein Crystals
3. Energy Minimization of Structures
4. Pre-equilibration of the system
5. Unrestricted Molecular Dynamics Simulation
6. Presentation and Interpretation of Molecular Dynamics Results
Taking lysozyme in water as an example
Day Seven
Execution of Protein-Ligand Molecular Dynamics Simulations
1. Protein-Ligand Processing Workflow in Molecular Dynamics Simulations
2. Preparation of Protein Crystals
3. Preparation of Initial Conformations for Protein-Ligand Docking
4. Preparation of Ligand Molecular Force Field Topology Files
4.1 Brief Introduction to Gauss
4.2 Brief Introduction to Ambertool
4.3 Generating Force Field Parameter Files for Small Molecules
5. Pre-equilibration with separate restraints on temperature and pressure for the complex system
6. Unrestricted Molecular Dynamics Simulation
7. Presentation and Interpretation of Molecular Dynamics Results
8. Trajectory Post-processing and Analysis
Taking the main protease of the新冠病毒 protein and related inhibitors as an example
Course Objectives
Enable students to master proficiently:PDB database, target protein, protein-ligand, protein-ligand small molecule, protein-ligand structure, molecular docking, protein-ligand docking, virtual screening, protein-protein docking, protein-polysaccharide molecular docking, protein-hydration docking, molecular dynamics and other technologies
Training Object
At present, computer-aided drug design serves a wide range of fields and researchers, such as CADD, drug design, pharmaceuticals, drug development, drug screening, new drug development, medicinal chemistry, biopharmaceuticals, immunology, natural products, veterinary drug development, bioinformatics, Chinese medicine pharmacology, Chinese medicine chemistry, network pharmacology, structural pharmacology, food safety, food flavor, food and drug development, food development, anti-tumor drugs, tumor immunology, enzyme engineering, genetics, antibody drugs, agricultural engineering, chemistry, organic synthesis, organic chemistry, structural biology, synthetic biology, and many other scientific researchers.
Training Architecture
1. Course Features -- Comprehensive application of course technology, principles and processes, and practical examples throughout
2. Learning Mode -- Combining theoretical knowledge with hands-on operation, enabling beginners to quickly master proficiency.
3. Course Service Q&A -- The main lecturer will provide professional answers to the questions you encounter in your actual work.
Teaching Materials
The teacher's preparation PPT is nearly 1000 pages. The training software will be provided along with installation guidance. So far, there have been over 30 sessions, with more than 3000 participants. The feedback from the students has been consistently very satisfied, making it the best training course for CADD (Computer-Aided Drug Design) in China.
The lecturer for computer-aided drug design comes from the Institute of Materia Medica at Peking Union Medical College Hospital in China. They specialize in research areas such as virtual drug screening, computer-aided drug design, AI-driven drug discovery, molecular docking, and molecular dynamics, with over a decade of research experience and more than 20 papers published as the first author.
Introduction of the Lecturer
The lecturer for Computer-Aided Drug Design comes from the Institute of Materia Medica at Peking Union Medical College Hospital, a university in China. The lecturer specializes in deep learning, machine learning, virtual drug screening, computer-aided drug design, AI-driven drug discovery, molecular docking, and molecular dynamics.Research in mechanics and other fields, with more than ten years of research experience.
Course Schedule
Deep Learning Protein Design Class Schedule
2024.08.17-2024.08.18（9：00-11：30）--（13：30-17：00）
2024.08.24-2024.08.25（9：00-11：30）--（13：30-17：00）
2024.08.28-2024.08.29（9：00-11：30）--（13：30-17：00）
A total of 6 days of classes via Tencent Meeting live stream, online practical operations, and all recorded sessions provided.
CADD Computer-Aided Drug Design Class Time
2024.08.10-2024.08.11（9：00-11：30）--（13：30-17：00）
2024.08.13-2024.08.16（9：00-11：30）--（13：30-17：00）
2024.8.20-2024.08.23（9：00-11：30）--（13：30-17：00）
A total of 7 days of classes via Tencent Meeting live stream, online practical operations, and all recorded sessions provided.
Training Costs and Benefits
Course Registration Fee:
Deep Learning Protein Design:
Public Price: ￥6880 per person per class (including registration fee, training fee, materials fee, and provision of full post-class playback materials)
Self-funded Price: ￥6,480 per person per class (including registration fee, training fee, materials fee, and provision of full post-class playback materials)
CADD Computer-Aided Drug Design:
Public Price: ￥5,880 per person per class (including registration fee, training fee, materials fee, and provision of full post-class playback materials)
Self-funded Price: ￥5,480 per person per class (including registration fee, training fee, materials fee, and provision of full post-class playback materials)
Heavyweight Offers:
Buy Two, Get One Free (Sign up for two classes and get one free learning spot; the free class can be chosen freely.)
Buy Four, Get Two Free (Sign up for four classes at the same time and get two free learning slots, with the option to choose the free classes)
Offer 1:
Two Classes Together: 10,880 RMB (Original Price: 18,640 RMB)
Three Classes Together: 14,880 RMB (Original Price: 23,620 RMB)
Special offer: 24,880 yuan for one year of free study (You can study any course held by our organization for free within one year)
Special offer: Two years of free study (28,880 yuan allows you to study any course offered by our institution for two full years for free)
Offer 2: Early registration and payment can enjoy a 300 yuan discount (limited to fifteen participants).
Sign up for courses and get previous course replays for free (the number of courses you sign up for equals the number of replays you receive).
(Clickable link for details):
Playback One:This course is a video course! Machine Learning Biomedical Training!
Follow-up Two:This course is a video course! Single-cell spatial transcriptomics training!
Replay Three:This course is a video course! Comparative Genomics Training!
Playback Four:This course is a video course! Machine Learning Proteomics Training
Playback Five:This course is a video course! Machine Learning Microbiomics Training
Replay Six:This course is a video course! Protein Crystal Structure Analysis Training
Replay Seven:This course is a video course! CRISPR-Cas9 Gene Editing Training
Replay Eight:This course is a video course! AIDD Artificial Intelligence Drug Design Training!
Playback Nine:This course is a video course! Machine Learning Metabolomics Training!
Playback Ten:This course is a video course! In-depth learning genomics training!
(Blue text can be clicked to view, all are training video courses)
1. Course Features -- Comprehensive coverage of course technology applications, principles and processes, and practical examples.
2. Learning Mode -- Combining theoretical knowledge with hands-on operation, enabling beginners to quickly master the skills.
3. Course Service Q&A -- The main instructor will provide professional answers to the questions you encounter in your actual work.
Teaching Method: Online live streaming via Tencent Meeting, theory+Hands-on teaching mode, where the teacher guides students step by step through the operations.Starting from scratch, electronicPPTAnd TutorialsOne week before the course starts, all training software will be sent to the students in advance. If there are any questions, they can be resolved through voice communication, screen sharing, and WeChat group discussions. Students and teachers can communicate with each other, as well as students with other students. After the training is completed, the teacher will continue to answer questions for a long time, and the training group will not be disbanded. Previous trainees have consistently given very high evaluations of the training quality and teaching methods!
Tencent Meeting Live Streaming Q&A | Step-by-Step Guidance
Trainees Give High Evaluation to the Training
Trainees Publish in Top Journals After Training
Quoting a sentence from one of the participants at this conference:
Previous Participating Units
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Overseas Colleges and Universities;From MIT, University of Bristol, UC Berkeley, Osaka University, George Mason University, Caltech, University of Manchester, Rice University, Boston University, Texas A&M University, Drake University, American Union University, Princeton University, Stanford University, Imperial College London, KAUST University, Lehigh University, The University of Queensland, The University of Queensland Australia, Yale University, Oxford University, Cambridge University, University of Pittsburgh, University of Sydney, University of Toronto, University of Washington Seattle, University of London, Duke University, University of Tokyo, Columbia University, Cornell University, New York University, Northwestern University, Brown University, University of Washington, etc.
Domestic Colleges and Universities; Participants include over 5,000 trainees from Sun Yat-sen University, Tsinghua University, Zhejiang University, Peking University First Hospital, Peking Union Medical College Hospital of the Chinese Academy of Medical Sciences, Northwest Minzu University, Southwest University, Shandong University, Qiyuan Laboratory, First Medical Center of the General Hospital of the People's Liberation Army, Guangdong Ocean University, Wuhan University, China Agricultural University, Henan Normal University, Nanjing Tech University, Shanghai Jiao Tong University, Southern University of Science and Technology, Nanjing University, Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences, Qinghai Academy of Agriculture and Forestry Sciences, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Shandong University, Heilongjiang Bayi Agricultural University, Second Affiliated Hospital of Nanchang University, Taizhou Central Hospital (Affiliated Hospital of Taizhou University), People’s Hospital Affiliated to Ningbo University, Xinjiang Agricultural University, Beijing Forestry University, Guangxi Medical University, Hunan University of Arts and Science, Binzhou Medical University, Yantai Affiliated Hospital of Binzhou Medical University, South China Normal University, Chinese Research Academy of Environmental Sciences, Yunnan Normal University, Kunming University of Science and Technology, Hubei University of Medicine, Lingang Laboratory, Soochow University, Fuzhou University, Nanfang Hospital, Second Affiliated Hospital of Nanchang University, Shenzhen Traditional Chinese Medicine Hospital, Hunan University of Arts and Science, Henan Institute of Science and Technology, Fujian Provincial Hospital, Xiangya Hospital of Central South University, Shenzhen Traditional Chinese Medicine Hospital, Tongde Hospital of Zhejiang Province, Baotou Teachers’ College of Inner Mongolia University of Science and Technology, Urumqi Center for Disease Control and Prevention, Research Institute of Forestry of the Chinese Academy of Forestry, Lanzhou Institute of Animal Husbandry and Veterinary Medicine of the Chinese Academy of Agricultural Sciences, Ludong University, Hebei University of Engineering, Zhujiang Hospital of Southern Medical University, Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University, Second Affiliated Hospital of Chongqing Medical University, Shanghai Medical College of Fudan University, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Blood Disease Hospital of the Chinese Academy of Medical Sciences (Institute of Hematology of the Chinese Academy of Medical Sciences), Peking University Shenzhen, Hong Kong University of Science and Technology Medical Center, Tianjin Cancer Hospital, Army Medical Center for Special Medicine, First Affiliated Hospital of Air Force Medical University, Jiangnan University, Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences, as well as many companies, research institutes, and universities! Thank you for your recognition of our training! Many others were unable to attend due to scheduling conflicts. This time, we sincerely invite you to participate!
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