Protein ligand binding improved by blending atomic and density information
Blending experimental electron density information to protein-ligand interaction
Machine Learning
Summary
Predicting how proteins and molecules fit together is important for drug design, but most methods use only one static picture of the molecules. The authors created CoDE, a new tool that combines detailed 3D information from experimental electron density maps with atomic positions. This helps capture uncertainty and multiple shapes at binding sites. Using many examples, their method learned to better understand protein-ligand interactions and to suggest new molecules that fit well. Their approach shows that including electron density data adds useful detail beyond just coordinates.
What this means in practice
- •For drug discovery teams: Improve prediction of how molecules bind to target proteins using a combined atomic and density-based model for better lead optimization.
- •For computational chemists: Generate new ligand candidates conditioned on protein pockets with enhanced structural detail from electron density to guide molecule design.
Authors
Seonghyun Park, Shuan Chen, Jun Jin Choong, Noboru Kuno, Gyubok Lee, Kaushalya Madhawa, Keisuke Ozawa, Hikaru Shindo, Sungsoo Ahn
Abstract
Machine learning models for protein-ligand interactions commonly rely on atomic coordinates, which are constructed by fitting molecular models to experimental electron-density maps. However, most models represent each complex as a single fitted structure, which can omit information about conformational heterogeneity, positional uncertainty, and local density support for individual atoms at binding interfaces. To fill this gap, we present CoDE, a Coordinate and Density Encoder, pre-trained on experimental density maps. We represent each binding site on a shared three-dimensional voxel grid combining atomic occupancy, experimental electron density, and its gradient magnitude. Using approximately 100K curated complexes, we pre-train a channel-wise vision transformer to jointly reconstruct masked atomic and density channels, learning a representation that integrates structural and experimental information. We evaluate the frozen encoder on two downstream tasks: (i) binding-affinity regression under leakage-controlled splits and (ii) pocket-conditioned ligand generation. Across these settings, CoDE improves over an identically pre-trained coordinate-only encoder and remains competitive with task-specific baselines, showing that density complements fitted atomic coordinates for protein-ligand interaction modeling and that the density-blended representation is useful for structure-based ligand generation.