生成内容： 一、总框架：两阶段对比图（分

MJ Bot 更新时间：2025-08-18 01:03:46
提示词：Generating content: 1. Overall Framework: Two-stage comparison diagram (can be divided left/right or up/down; recommended to be left-right for clearer comparison) Title: BEC-Pred Innovation Logic — "Pre-training lays the foundation → Fine-tuning captures the specifics" Core Structure: Place "Traditional Model Pain Points" on the left, and "BEC-Pred Innovation Process" on the right 2. Left Side: Issues with Traditional Models (using “negative cases” to highlight innovation) 1. Data Singularity Draw a "small box" (rectangle, light gray color), inside write: Only enzyme reaction data training Next to it add a red cross ❌ + text: Lacking universal reaction logic support → Few and unbalanced enzyme reaction data, the model hasn’t even learned “how chemical reactions change”! 2. Simple Architecture Draw a "shallow network" (for example, draw 3 stacked rectangles representing a simple NN), label: Traditional shallow model (like single-layer NN) Add accompanying text: Hard to capture complex reaction features → Changes in chemical bonds and relationship between substrates and products, traditional models “can’t understand”! 3. Right Side: Innovations of BEC-Pred (divided into two stages, connected by arrows) Stage 1: Pre-training (General reactions → Learning basic rules) 1. Data Input Draw a "large database" (3D rectangle, choose blue color), label: USPTO Database (1.1 million+ organic reactions) Add an arrow connecting it to the "BERT Pre-training Module" (draw the iconic “multi-layer stacking” of Transformer, label BERT pre-training) 2. Advantages of BERT Architecture Draw "multi-layer Transformers" (at least draw 3 layers, each layer labeled multi-head attention), next to it add text: BERT's multi-layer attention, like a “magnifying glass” focusing on molecular changes → Learning thoroughly “how chemical reactions change” (how bonds break/form, how molecules transform) Stage 2: Fine-tuning (Enzyme reactions → Learning specific features) 1. Data Input Draw a "small but refined enzyme library" (3D rectangle, choose orange color), label: ECREACT Database (enzyme reactions with EC labels) Add an arrow connecting it to the "BERT Fine-tuning Module" (reuse the Transformer graphic from Stage 1, label BERT fine-tuning) 2. Model Output Draw a "classification head" (small rectangle, label Classifier), arrow connected to "EC number" (e.g., label EC 3.1.1.2, corresponding to esterase) Add text: Using enzyme-specific reactions + EC labels, allowing the model to focus on “how enzyme catalysis is special” → Precisely corresponding to EC numbers! 4. Auxiliary Elements: Details to make the diagram more vivid Molecular Change Example Next to the "Pre-training" and "Fine-tuning" modules, draw a small case study (e.g., esterase catalytic reaction): Substrate SMILES: OC(OC(C)(C)O)O... (ester structure) Product SMILES: OC(C)(C)O + ... (products after ester bond cleavage) Use arrows to indicate bond cleavage points, reflecting BERT's attention “focusing on key changes”. Comparison Arrows Between the traditional model and BEC-Pred, draw bidirectional comparison arrows, label: Traditional Model: Directly learn enzyme reactions → “No foundation, can't grasp accurately” BEC-Pred: First learn general → then learn specific → “Foundation strong, positioning accurate” --ar 1:1 --v 7 --stylize 100

中文提示词：生成内容： 一、总框架：两阶段对比图（分左右 / 上下都行，推荐左右对比更直观） 标题：BEC-Pred 创新逻辑 ——「预训练打基础 → 微调抓专属」 核心结构：左边放「传统模型痛点」，右边放「BEC-Pred 创新流程」 二、左边：传统模型的问题（用 “负面案例” 衬托创新） 1. 数据单一 画一个 “小盒子”（矩形，颜色浅灰），里面写： 只用酶反应数据训练 旁边加 红色叉号❌ + 文字： 缺乏通用反应逻辑支撑 → 酶反应数据少、不均衡，模型连“化学反应咋变的”都没学透！ 2. 架构简单 画一个 “浅层网络”（比如画 3 层矩形堆叠，代表简单 NN），标： 传统浅模型（如单层NN） 旁边配文字： 难抓复杂反应特征 → 底物→产物的化学键变化、位点关系，传统模型“看不懂”！ 三、右边：BEC-Pred 的创新（分两阶段，用箭头串联） 阶段 1：预训练（通用反应→学基础规律） 1. 数据输入 画一个 “大数据库”（立体矩形，颜色选蓝色），标： USPTO数据库（110万+有机反应） 加箭头连到 “BERT 预训练模块”（画 Transformer 标志性的 “多层堆叠”，标BERT预训练） 2. BERT 架构优势 画 “多层 Transformer”（至少画 3 层，每层标多头注意力），旁边配文字： BERT的多层注意力，像“放大镜”盯分子变化 → 学透“化学反应咋变的”（键咋断/咋生成、分子咋转化） 阶段 2：微调（酶反应→学专属特征） 1. 数据输入 画一个 “小而精的酶库”（立体矩形，颜色选橙色），标： ECREACT数据库（带EC标签的酶反应） 加箭头连到 “BERT 微调模块”（复用阶段 1 的 Transformer 图，标BERT微调） 2. 模型输出 画 “分类头”（小矩形，标Classifier），箭头连到 “EC 编号”（比如标EC 3.1.1.2，对应酯酶） 配文字： 用酶专属反应+EC标签，让模型聚焦“酶催化咋特殊” → 精准对应EC数！ 四、辅助元素：让图更生动的细节 分子变化示例 在 “预训练” 和 “微调” 模块旁，画个小案例（比如酯酶催化反应）： 底物 SMILES：OC(OC(C)(C)O)O...（酯类结构） 产物 SMILES：OC(C)(C)O + ...（酯键断裂后的产物） 用箭头标键断裂位点，体现 BERT 注意力 “盯关键变化”。 对比箭头 在传统模型和 BEC-Pred 之间，画 双向对比箭头，标： 传统模型：直接学酶反应→“没基础，抓不准” BEC-Pred：先学通用→再学专属→“基础牢，定位准” 一键画同款
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