Drug discovery represents a time-consuming and financially intensive process, and virtual screening can accelerate it. Scoring functions, as one of the tools guiding virtual screening, have their precision closely tied to screening efficiency. In our previous study, we developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork), but it suffers from the defect in representing protein-ligand contact maps. Incorrect binding modes inevitably lead to poor affinity predictions, so accurate prediction of the protein-ligand contact map is desired to improve PLANET. In this study, we have proposed PLANET v2.0 as an upgraded version. The model is trained via multi-objective training strategy and incorporates the Mixture Density Network to predict binding modes. Except for the probability density distributions of non-covalent interactions, we innovatively employ another Gaussian mixture model to describe the relationship between distance and energy of each interaction pair and predict protein-ligand affinity like calculating the mathematical expectation. As on the CASF-2016 benchmark, PLANET v2.0 demonstrates excellent scoring power, ranking power, and docking power. The screening power of PLANET v2.0 gets notably improved compared to PLANET and Glide SP and it demonstrates robust validation on a commercial ultra-large-scale dataset. Given its efficiency and accuracy, PLANET v2.0 can hopefully become one of the practical tools for virtual screening workflows. PLANET v2.0 is freely available at https://www.pdbbind-plus.org.cn/planetv2.

翻译：药物发现是一个耗时且成本高昂的过程，虚拟筛选可以加速这一进程。作为指导虚拟筛选的工具之一，评分函数的精度与筛选效率密切相关。在我们之前的研究中，我们开发了一个名为PLANET（Protein-Ligand Affinity prediction NETwork）的图神经网络模型，但它在表示蛋白质-配体接触图方面存在缺陷。不正确的结合模式必然导致亲和力预测不佳，因此需要准确预测蛋白质-配体接触图以改进PLANET。在本研究中，我们提出了升级版本PLANET v2.0。该模型通过多目标训练策略进行训练，并整合了混合密度网络来预测结合模式。除了非共价相互作用的概率密度分布外，我们创新性地采用另一个高斯混合模型来描述每个相互作用对的距离与能量关系，并通过计算数学期望来预测蛋白质-配体亲和力。在CASF-2016基准测试中，PLANET v2.0展现出优异的评分能力、排序能力和对接能力。与PLANET和Glide SP相比，PLANET v2.0的筛选能力显著提升，并在一个商业超大规模数据集上得到了稳健验证。鉴于其效率和准确性，PLANET v2.0有望成为虚拟筛选工作流程中的实用工具之一。PLANET v2.0可在 https://www.pdbbind-plus.org.cn/planetv2 免费获取。