MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS

Abstract

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragmentbased LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures.

1. INTRODUCTION

Recent years have witnessed the great success of deep learning in drug design. Among the progress, deep generative models that aim to generate molecules with desirable physicochemical and pharmacological properties are of particular importance. These models range from string-based (Gómez-Bombarelli et al., 2018) and graph-based methods (Jin et al., 2018; Xie et al., 2021) to recent 3D geometry-based methods (Gebauer et al., 2019; Luo & Ji, 2021) . Molecule drugs can only affect certain biological functions and pathways by binding to the target proteins. However, the complexity of the context information, geometric constraints, and moleculeprotein interactions bring great challenges. Therefore, few deep learning models have been developed to generate molecules that bind to specific protein binding sites (a.k.a. structure-based drug design). Early attempts modify the pocket-free models by incorporating scoring functions like docking scores between generated molecules and pockets to guide the ligand generation (Li et al., 2021) . Another line of works convert the 3D pocket structures to molecular string or graph representations for conditional generation (Skalic et al., 2019; Xu et al., 2021a) . They fail to model how molecules interact with their target proteins explicitly in 3D space. Recently, a series of 3D generative models are proposed to generate 3D molecules that bind to given protein pockets (Luo & Ji, 2021; Liu et al., 2022; Peng et al., 2022) . They use 3D graph neural networks for context encoding and achieve equivariance. However, most of these works do not consider chemical priors and may generate invalid molecules with unrealistic substructures. Their atom-wise generation scheme also leads to inefficient molecule sampling. In this work, we propose a novel Fragment-based LigAnd Generation framework (FLAG) for structure-based drug design, where the molecules are generated fragment-by-fragment. To generate 3D molecules, we first preprocess the dataset and extract molecular fragments with high occurrence frequencies (i.e., motif) as "building blocks" for new molecules. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information including the protein pocket and the intermediate molecular graph. Secondly, our model selects the focal motif, predicts the next motif type, and attaches the new motif to the generated molecule. Attaching motifs in 3D space is a great challenge. Inspired by the fact that the flexibility of molecular geometries lies largely in the degree of rotatable bond (A bond in a molecule is rotatable if cutting this bond creates two connected components of the molecule, each of which has at least two atoms) (Axelrod & Gomez-Bombarelli, 2022), we employ cheminformatics tools (Bento et al., 2020) to efficiently determine the bond lengths/angles and trains neural networks to predict torsion angles. Leveraging this insight can significantly reduce the searching space of atom/motif coordinates. For example, the CrossDocked dataset (Francoeur et al., 2020) has, on average, m = 24 heavy atoms, corresponding to a 3m-dimensional euclidean space, but only around 5 torsion angles of rotatable bonds. Furthermore, the rotation angle is predicted to further adjust the geometries of generated molecules. Inspired by force fields in physics (Rappé et al., 1992) , a novel structure refinement is finally applied to optimize the molecule structures. We conduct extensive evaluations to evaluate our approach. Experimental results show that: (1) our method is able to generate diverse drug-like molecules with high binding affinity to target proteins; (2) FLAG is much faster than most of the baseline methods at sampling new molecules; (3) thanks to the design of fragment-based generation, our method outperforms baselines by a large margin on generating valid molecules with realistic substructures.

2. RELATED WORK

Motif-based Molecule Generation. To generate more valid molecules with realistic substructures, many models adopt prior knowledge of chemical motifs, also known as fragments or rationales, as building blocks to generate or optimize molecules (Jin et al., 2018; 2020a; Podda et al., 2020; Jin et al., 2020b; Chen et al., 2021a; Seo et al., 2021; Xie et al., 2021; Chen et al., 2021b; Guo et al., 2022; Flam-Shepherd et al., 2022) . For example, JT-VAE (Jin et al., 2018) first decomposes the molecular graphs into junction trees, where each node in the tree represents a substructure of the molecule. Then JT-VAE adopts the variational autoencoder as the framework and learns to reconstruct the molecular graph fragment-by-fragment. Similarly, RationaleRL extracts rationales that lead to different properties of molecules by MCTS. Then it is trained to expand rationales to complete molecular graphs with reinforcement learning. However, the aforementioned methods cannot generate 3D molecules directly and consider the complicated context information of binding sites. 3D Molecule Generation. With the development of geometric deep learning, many recent works explore 3D molecular geometry generations with given 2D molecular graphs (Mansimov et al., 2019; Simm & Hernandez-Lobato, 2020; Luo et al., 2021b; Shi et al., 2021; Ganea et al., 2021; Xu et al., 2021b; 2022) , or from scratch (Gebauer et al., 2019; Hoogeboom et al., 2022; Nesterov et al., 2020; Gebauer et al., 2022; Luo & Ji, 2021; Satorras et al., 2021) . Comparatively, the task of structurebased drug design is more challenging. Firstly, the 2D molecular graph is unknown. Secondly, the generated molecules should fit well with the binding pockets with high binding affinity. Finally, the aforementioned works usually deal with small organic molecules and may be insufficient to generate 3D drug-like molecules with larger molecule weights. For more detailed discussions on molecule generation, we recommend readers refer to the comprehensive survey (Du et al., 2022) . Structure-based Drug Design. Structure-based drug design aims to generate 3D molecules that bind to specific binding sites. LiGAN (Ragoza et al., 2022) first approaches this problem using a conditional variational autoencoder trained on atomic density grid representations of protein-ligand structures. Then the molecular structures of ligands are constructed by atom fitting and bond inference from the generated atom densities. As a preliminary work, LiGAN employs 3D CNN as the encoder, which does not satisfy the desirable equivariance property. The follow-up works achieve

availability

Our code is publicly available at https://github.com/zaixizhang

