用梯度指导遗传算法，加速药物分子设计并提升质量
Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design
- 将梯度信息融入遗传算法，用方向性搜索替代随机探索
- 在多个基准上实现25%的顶10分数提升，收敛速度更快
- 适合药物分子生成与优化领域研究者快速获取优质分子
分子发现为化学工业带来巨大效益。多种分子设计技术被开发以识别具有理想性质的分子。传统优化方法如遗传算法在多个分子设计基准上仍保持领先，但仅依赖随机游走探索，限制了最终解的质量和收敛速度。为此，我们提出一种新方法——梯度遗传算法（Gradient GA），将目标函数的梯度信息引入遗传算法。不同于随机探索，每个候选分子通过沿梯度方向迭代优化，逐步逼近最优解。我们设计了一个由神经网络参数化的可微目标函数，并利用离散朗之万提议（Discrete Langevin Proposal）实现在离散分子空间中的梯度引导。实验表明，该方法显著提升收敛速度与解的质量，优于前沿技术。例如，在顶10分数上相比原始遗传算法最高提升25%。代码已公开于 https://github.com/debadyuti23/GradientGA。
原文摘要 · Abstract (English)
Molecular discovery has brought great benefits to the chemical industry. Various molecule design techniques are developed to identify molecules with desirable properties. Traditional optimization methods, such as genetic algorithms, continue to achieve state-of-the-art results across multiple molecular design benchmarks. However, these techniques rely solely on random walk exploration, which hinders both the quality of the final solution and the convergence speed. To address this limitation, we propose a novel approach called Gradient Genetic Algorithm (Gradient GA), which incorporates gradient information from the objective function into genetic algorithms. Instead of random exploration, each proposed sample iteratively progresses toward an optimal solution by following the gradient direction. We achieve this by designing a differentiable objective function parameterized by a neural network and utilizing the Discrete Langevin Proposal to enable gradient guidance in discrete molecular spaces. Experimental results demonstrate that our method significantly improves both convergence speed and solution quality, outperforming cutting-edge techniques. For example, it achieves up to a 25% improvement in the top-10 score over the vanilla genetic algorithm. The code is publicly available at https://github.com/debadyuti23/GradientGA.
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