3D Interaction Geometric Pre-training for Molecular Relational Learning

Molecular relational learning (MRL) focuses on understanding the interaction dynamics between molecules and has gained significant attention from researchers thanks to its diverse applications [20]. For instance, understanding how a medication dissolves in different solvents (medication-solvent interaction) is vital in pharmacy [30, 26, 3], while predicting the optical and photophysical properties of chromophores in various solvents (chromophore-solvent interaction) is essential for material discovery [16]. Because of the expensive time and financial costs associated with conducting wet lab experiments to test the interaction behavior of all possible molecular pairs [31], machine learning methods have been quickly embraced for MRL. Despite recent advancements in MRL, previous works tend to ignore molecules' 3D geometric information and instead focus solely on their 2D topological structures. However, in molecular science, the 3D geometric information of molecules (Figure 1 (a)) is crucial for understanding and predicting molecular behavior across various contexts, ranging from physical properties [1] to biological functions [10, 46]. This is particularly important in MRL, as geometric information plays a key role in molecular interactions by determining how molecules recognize, interact, and bind with one another in their interaction environment [34]. In traditional molecular dynamics simulations, explicit solvent models, which directly consider the detailed environment of molecular interaction, have demonstrated superior performance compared to implicit solvent models, which simplify the solvent as a continuous medium, highlighting the significance of explicitly modeling the complex geometries of interaction environments [47]. However, acquiring stereochemical structures of molecules is often very costly, resulting in limited availability of such 3D geometric information for downstream tasks [23].