128 lines
3.2 KiB
Markdown
128 lines
3.2 KiB
Markdown
# AI-Driven Design of 16-Membered Macrolides: From Traditional Antibiotic Optimization to Intelligent Molecular Generation
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This file is the default primary outline for the current project.
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Each new task round should read this file first to determine the target chapter and subsection.
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If this file is missing or clearly outdated, an agent may extract a provisional outline from the Word draft and write:
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- `review-outline.generated.md`
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Once the generated outline is manually confirmed, it should be folded back into this file.
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---
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## Abstract
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## Chapter 1. Introduction
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### 1.1 Development and challenges of antibiotics
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#### 1.1.1 Overview
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#### 1.1.2 Macrolide antibiotics
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#### 1.1.3 Current research status of macrolide antibiotics
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### 1.2 Drug resistance in macrolides
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#### 1.2.1 Resistance mechanisms of macrolide antibiotics
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#### 1.2.2 Binding sites of macrolide antibiotics
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### 1.3 Quantitative structure-activity relationships
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#### 1.3.1 Molecular representation
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#### 1.3.2 1D-QSAR
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#### 1.3.3 2D-QSAR
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#### 1.3.4 3D-QSAR
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### 1.4 Molecular energy minimization
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#### 1.4.1 Molecular mechanics
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#### 1.4.2 Quantum mechanics
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#### 1.4.3 Hybrid quantum mechanics / molecular mechanics (QM/MM)
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#### 1.4.4 Conformational ensemble sampling
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### 1.5 AI-based innovative antibiotic design
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## Chapter 2. Current status of macrocycle design methods
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### 2.1 Evolution of computer-aided macrocycle design
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#### 2.1.1 Early geometric matching and fragment stitching
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#### 2.1.2 Structured fragment-linking algorithms
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#### 2.1.3 Commercial and semi-automated tools
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#### 2.1.4 LigMac: end-to-end structure-guided design
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#### 2.1.5 Molecular-field-based fragment replacement tools
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#### 2.1.6 Web platforms with configurable linker libraries
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### 2.2 Generative deep learning for macrocycle design
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### 2.3 Reinforcement learning optimization for macrocycle design
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## Chapter 3. Specialized models and strategies for macrocycle generation
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### 3.1 Generative models based on fragment linking / cyclization
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### 3.2 Generative models for macrocyclic peptides and special scaffolds
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### 3.3 Generative models and tools for macrolides
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#### 3.3.1 PKS-based generation of macrolides
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## Chapter 4. AI-driven molecular generation techniques
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### 4.1 Sequence-based generative models (SMILES representation)
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### 4.2 Molecular graph-based generative models
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### 4.3 GAN and reinforcement learning methods
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### 4.4 Emerging diffusion models and 3D generation
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## Chapter 5. Generative models for macrocyclic molecules
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### 5.1 Generative models for macrocycles
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#### 5.1.1 Challenges in macrocycle design
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#### 5.1.2 Macformer: macrocycle structure generation
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#### 5.1.3 MacroHop: macrocycle scaffold generation
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#### 5.1.4 MacroEvoLution: evolutionary macrocycle design
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#### 5.1.5 HELM-GPT: macrocyclic peptide generation
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### 5.2 Specialized tools for macrolides
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#### 5.2.1 PKS Enumerator
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#### 5.2.2 SIME: biosynthesis-inspired macrocycle design
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#### 5.2.3 Biosynthesis-driven macrocycle design strategies
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### 5.3 Fixed-scaffold structure generation strategies
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#### 5.3.1 Scaffold-constrained generation
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#### 5.3.2 Site-directed generation
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#### 5.3.3 Fragment stitching and side-chain enumeration
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## Conclusion and outlook
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## References
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