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Zeng2022 - Prediction of HIV Growth Inhibition using ImageMol

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: Probability of HIV inhibition. 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/eos6hy3

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2405130001 | BioModels | 2024-05-13

REPOSITORIES: BioModels

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Publications

Accurate prediction of molecular targets using a self-supervised image representation learning framework.

Zeng Xiangxiang X   Xiang Hongxin H   Yu Linhui L   Wang Jianmin J   Li Kenli K   Nussinov Ruth R   Cheng Feixiong F  

Research square 20220407

The clinical efficacy and safety of a drug is determined by its molecular targets in the human proteome. However, proteome-wide evaluation of all compounds in human, or even animal models, is challenging. In this study, we present an unsupervised pre-training deep learning framework, termed ImageMol, from 8.5 million unlabeled drug-like molecules to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabeled molecular i  ...[more]

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