Structure-aware generation of drug-like molecules

• URL: http://arxiv.org/abs/2111.04107v1
• Date: Sun, 7 Nov 2021 15:19:54 GMT
• Title: Structure-aware generation of drug-like molecules
• Authors: Pavol Drot\'ar, Arian Rokkum Jamasb, Ben Day, C\u{a}t\u{a}lina Cangea, Pietro Li\`o
• Abstract summary: Deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design) We propose a novel supervised model that generates molecular graphs jointly with 3D pose in a discretised molecular space. We evaluate our model using a docking benchmark and find that guided generation improves predicted binding affinities by 8% and drug-likeness scores by 10% over the baseline.
• Score: 2.449909275410288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-based drug design involves finding ligand molecules that exhibit structural and chemical complementarity to protein pockets. Deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design), avoiding exhaustive virtual screening of chemical space. Most generative de-novo models fail to incorporate detailed ligand-protein interactions and 3D pocket structures. We propose a novel supervised model that generates molecular graphs jointly with 3D pose in a discretised molecular space. Molecules are built atom-by-atom inside pockets, guided by structural information from crystallographic data. We evaluate our model using a docking benchmark and find that guided generation improves predicted binding affinities by 8% and drug-likeness scores by 10% over the baseline. Furthermore, our model proposes molecules with binding scores exceeding some known ligands, which could be useful in future wet-lab studies.

Related papers

Err

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.