Structure-based de novo drug design using 3D deep generative models

By Yibo Li, Jianfeng Pei, Luhua Lai. 2021

150 citations recorded by the source corpus.

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Tianfan Fu
2. Shengchao Liu
3. Jimeng Sun
4. Yuanqi Du

In Materials Chemistry

1. Shengchao Liu
2. Jimeng Sun
3. Tianfan Fu
4. Yuanqi Du

In Molecular Biology

1. Tianfan Fu
2. Shengchao Liu
3. Jimeng Sun
4. Yuanqi Du

Citations in the corpus

• Artificial intelligence in drug development (2025, unknown): “…scoring functions, incorporating constraints— such as disease-related gene expression features 109 , pharmacophores 110 , protein sequences 87 or structures 102,111–114 , binding affinity 115 and protein–ligand interactions 116,117 —can also direct models toward generating desired molecules.”
• Structure-based drug design with equivariant diffusion models (2022, unknown): “We employ widely-used metrics to assess the quality of our generated molecules [14, 15]: (1) Vina Score is an empirical estimation of binding affinity between small molecules and their target pocket; (2) QED is a simple quantitative estimation of drug-likeness combining several desirable molecular…”
• MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, background): “DeepLigBuilder [181] instead represents molecules in 3D geometries which are constructed by 3D coordinates and atom types.”
• Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets (2022, unknown): “Early approaches modify the pocket-free models by integrating evaluation functions like docking scores between sampled molecules and pockets to guide the candidate searching (Li et al., 2021).”