构建分子生成模型评估平台，统一标准推动药物研发智能化
A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design
- 基于MOSES平台系统评测不同生成模型的性能表现
- 发现各类模型在分子有效性、独特性和新颖性上各有优劣
- 为药物设计AI研究提供可复现的基准与方向参考
新药研发是现代科学的重大挑战，成本高、周期长。深度生成模型有望通过高效探索庞大的化学空间加速这一进程。然而，该领域缺乏标准化评估方法，导致不同方法难以公平比较。本研究对Molecular Sets（MOSES）平台进行了全面分析，这是一个用于分子设计中生成模型评估的综合性基准框架。通过对循环神经网络、变分自编码器和生成对抗网络等多种生成架构的严格评估，考察其在保持特定化学性质的同时生成有效、唯一且新颖分子结构的能力。结果表明，不同架构在各项指标上表现出互补优势，揭示了化学空间探索与利用之间的复杂权衡。研究深入剖析了当前分子生成技术的前沿水平，并为未来人工智能驱动的药物发现奠定了基础。
原文摘要 · Abstract (English)
The development of novel pharmaceuticals represents a significant challenge in modern science, with substantial costs and time investments. Deep generative models have emerged as promising tools for accelerating drug discovery by efficiently exploring the vast chemical space. However, this rapidly evolving field lacks standardized evaluation protocols, impeding fair comparison between approaches. This research presents an extensive analysis of the Molecular Sets (MOSES) platform, a comprehensive benchmarking framework designed to standardize evaluation of deep generative models in molecular design. Through rigorous assessment of multiple generative architectures, including recurrent neural networks, variational autoencoders, and generative adversarial networks, we examine their capabilities in generating valid, unique, and novel molecular structures while maintaining specific chemical properties. Our findings reveal that different architectures exhibit complementary strengths across various metrics, highlighting the complex trade-offs between exploration and exploitation in chemical space. This study provides detailed insights into the current state of the art in molecular generation and establishes a foundation for future advancements in AI-driven drug discovery.
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