Brocidiacono Michael, Popov Konstantin I, Koes David Ryan, Tropsha Alexander
Eshelman School of Pharmacy, University of North Carolina at Chapel Hill.
Department of Computational and Systems Biology, University of Pittsburgh.
ArXiv. 2023 Jul 26:arXiv:2307.12090v2.
Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iteratively refines a ligand pose. We combine these two approaches by training a pose scoring function in a diffusion-inspired manner. In our method, PLANTAIN, a neural network is used to develop a very fast pose scoring function. We parameterize a simple scoring function on the fly and use L-BFGS minimization to optimize an initially random ligand pose. Using rigorous benchmarking practices, we demonstrate that our method achieves state-of-the-art performance while running ten times faster than the next-best method. We release PLANTAIN publicly and hope that it improves the utility of virtual screening workflows.
分子对接旨在预测小分子在蛋白质结合位点的三维构象。传统的对接方法通过最小化一个受物理启发的评分函数来预测配体构象。最近，有人提出了一种扩散模型，该模型可以迭代优化配体构象。我们通过以扩散启发的方式训练构象评分函数，将这两种方法结合起来。在我们的方法PLANTAIN中，使用神经网络开发了一个非常快速的构象评分函数。我们即时参数化一个简单的评分函数，并使用L-BFGS最小化来优化一个初始随机的配体构象。通过严格的基准测试实践，我们证明我们的方法在性能上达到了当前最优水平，同时运行速度比次优方法快十倍。我们公开发布了PLANTAIN，希望它能提高虚拟筛选工作流程的效用。
