Learning to design drug-like molecules in three-dimensional space using deep generative models

• URL: http://arxiv.org/abs/2104.08474v1
• Date: Sat, 17 Apr 2021 07:30:23 GMT
• Title: Learning to design drug-like molecules in three-dimensional space using deep generative models
• Authors: Yibo Li, Jianfeng Pei and Luhua Lai
• Abstract summary: Ligand Neural Network (L-Net) is a novel graph generative model for designing drug-like molecules with high-quality 3D structures. L-Net is capable of generating chemically correct, conformationally valid, and highly druglike molecules.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep generative models for molecular graphs are gaining more and more attention in the field of de novo drug design. A variety of models have been developed to generate topological structures of drug-like molecules, but explorations in generating three-dimensional structures are still limited. Existing methods have either focused on low molecular weight compounds without considering drug-likeness or generate 3D structures indirectly using atom density maps. In this work, we introduce Ligand Neural Network (L-Net), a novel graph generative model for designing drug-like molecules with high-quality 3D structures. L-Net directly outputs the topological and 3D structure of molecules (including hydrogen atoms), without the need for additional atom placement or bond order inference algorithm. The architecture of L-Net is specifically optimized for drug-like molecules, and a set of metrics is assembled to comprehensively evaluate its performance. The results show that L-Net is capable of generating chemically correct, conformationally valid, and highly druglike molecules. Finally, to demonstrate its potential in structure-based molecular design, we combine L-Net with MCTS and test its ability to generate potential inhibitors targeting ABL1 kinase.

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