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.

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