NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer.
Molecules
; 29(8)2024 Apr 19.
Article
in En
| MEDLINE
| ID: mdl-38675687
ABSTRACT
Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Molecules
Journal subject:
BIOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Suiza