PLANET v2.0: A comprehensive Protein-Ligand Affinity Prediction Model Based on Mixture Density Network

2026-01-12

AI Summary

Traditional protein–ligand affinity prediction models are limited by their inability to accurately represent binding contact maps, which compromises virtual screening efficiency. This work proposes PLANET v2.0, a novel framework that integrates graph neural networks with mixture density networks through a multi-objective training strategy. It innovatively employs two Gaussian mixture models to probabilistically capture the spatial distribution and energetic characteristics of non-covalent interactions, enabling joint prediction of binding poses and affinities in the form of probability densities. The final affinity score is derived via mathematical expectation. On the CASF-2016 benchmark, PLANET v2.0 significantly outperforms both its predecessor PLANET and Glide SP across all four tasks—scoring, ranking, docking, and screening—and demonstrates exceptional robustness on a large-scale commercial dataset.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Protein-Ligand Affinity Prediction
• Binding Mode Prediction
• Contact Map Representation
• Scoring Function
• Virtual Screening

Innovation

• Mixture Density Network
• Protein-Ligand Affinity Prediction
• Gaussian Mixture Model
• Multi-objective Training
• Virtual Screening