3D EQUIVARIANT DIFFUSION FOR TARGET-AWARE MOLECULE GENERATION AND AFFINITY PREDICTION

Abstract

Rich data and powerful machine learning models allow us to design drugs for a specific protein target in silico. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free models as the atomic interaction in the 3D space is explicitly modeled. However, current 3D target-aware models either rely on the voxelized atom densities or the autoregressive sampling process, which are not equivariant to rotation or easily violate geometric constraints resulting in unrealistic structures. In this work, we develop a 3D equivariant diffusion model to solve the above challenges. To achieve target-aware molecule design, our method learns a joint generative process of both continuous atom coordinates and categorical atom types with a SE(3)-equivariant network. Moreover, we show that our model can serve as an unsupervised feature extractor to estimate the binding affinity under proper parameterization, which provides an effective way for drug screening. To evaluate our model, we propose a comprehensive framework to evaluate the quality of sampled molecules from different dimensions. Empirical studies show our model could generate molecules with more realistic 3D structures and better affinities towards the protein targets, and improve binding affinity ranking and prediction without retraining.

1. INTRODUCTION

Rational drug design against a known protein binding pocket is an efficient and economical approach for finding lead molecules (Anderson, 2003; Batool et al., 2019) and has attracted growing attention from the research community. However, it remains challenging and computationally intensive due to the large synthetically feasible space (Ragoza et al., 2022) , and high degrees of freedom for binding poses (Hawkins, 2017) . Previous prevailed molecular generative models are based on either molecular string representation (Bjerrum and Threlfall, 2017; Kusner et al., 2017; Segler et al., 2018) or graph representation (Li et al., 2018; Liu et al., 2018; Jin et al., 2018; Shi et al., 2020) , but both representations do not take the 3D spatial interaction into account and therefore not well suited for target-aware molecule generation. With recent development in structural biology and protein structure prediction (Jumper et al., 2021) , more structural data become available (Francoeur et al., 2020) and unlock new opportunities for machine learning algorithms to directly design drugs inside 3D binding complex (Gebauer et al., 2019; Simm et al., 2020a; b) . Recently, new generation of generative models are proposed specifically for the target-aware molecule generation task (Luo et al., 2021; Ragoza et al., 2022; Tan et al., 2022; Liu et al., 2022; Peng et al., 2022) . However, existing approaches suffer from several drawbacks. For instance, Tan et al. ( 2022) does not explicitly model the interactions between atoms of molecules and proteins in the 3D space, but only considers the target as intermediate conditional embeddings. For those that do consider the atom interactions in the 3D space, Ragoza et al. ( 2022) represents the 3D space as voxelized grids and model the proteins and molecules using 3D Convolutional Neural Networks (CNN). However, this model is not rotational equivariant and cannot fully capture the 3D inductive * Equal Contribution 1

