Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models
Weixin Xie, Jianhang Zhang, Qin Xie, Chaojun Gong, Youjun Xu, Luhua, Lai, Jianfeng Pei

TL;DR
This paper introduces TransPharmer, a pharmacophore-informed generative model that effectively produces structurally novel, bioactive ligands, demonstrated by successful case studies including potent PLK1 inhibitors with new scaffolds.
Contribution
The paper presents TransPharmer, a novel model integrating pharmacophore fingerprints with GPT, enabling scaffold hopping and generation of bioactive, structurally novel compounds.
Findings
TransPharmer outperforms existing models in pharmacophore-guided molecule generation.
Three of four designed compounds for PLK1 showed submicromolar activity.
The most potent compound, IIP0943, has a potency of 5.1 nM and high selectivity.
Abstract
Deep generative models have gained significant advancements to accelerate drug discovery by generating bioactive chemicals against desired targets. Nevertheless, most generated compounds that have been validated for potent bioactivity often exhibit structural novelty levels that fall short of satisfaction, thereby providing limited inspiration to human medicinal chemists. The challenge faced by generative models lies in their ability to produce compounds that are both bioactive and novel, rather than merely making minor modifications to known actives present in the training set. Recognizing the utility of pharmacophores in facilitating scaffold hopping, we developed TransPharmer, an innovative generative model that integrates ligand-based interpretable pharmacophore fingerprints with generative pre-training transformer (GPT) for de novo molecule generation. TransPharmer demonstrates…
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Taxonomy
TopicsComputational Drug Discovery Methods · Chemical Synthesis and Analysis · Cell Image Analysis Techniques
