MolData, a molecular benchmark for disease and target based machine learning.

ABSTRACT: Deep learning's automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChEMBL offer the screening data for millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological descriptions which can hinder their usage by the machine learning community. In this work, a comprehensive

SUBMITTER: Keshavarzi Arshadi A 

PROVIDER: S-EPMC8899453 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature