Zeng2022 - Prediction of molecular properties and drug targets using a self-supervised learning framework

ABSTRACT: ImageMol is a Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks. This model is fine tuned on 13 assays concerned with a number of target categories ranging from viral entry to toxicity in humans. These interactions are formulated as binary classification tasks. Model Type: Predictive machine learning model. Model Relevance: SARS-CoV-2 Anti viral screening. Model Encoded by: Dhanshree Arora (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos4cxk

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2405080005 | BioModels | 2024-05-08

REPOSITORIES: BioModels