LOGICS: Learning optimal generative distribution for designing de novo chemical structures.

ABSTRACT: In recent years, the field of computational drug design has made significant strides in the development of artificial intelligence (AI) models for the generation of de novo chemical compounds with desired properties and biological activities, such as enhanced binding affinity to target proteins. These high-affinity compounds have the potential to be developed into more potent therapeutics for a broad spectrum of diseases. Due to the lack of data required for the training of deep generative models, however, some of these approaches have fine-tuned their molecular generators using data obtained from a separate predictor. While these studies show that generative models can produce structures with the desired target properties, it remains unclear whether the diversity of the generated structur

SUBMITTER: Bae B 

PROVIDER: S-EPMC10483765 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature