Traditional Machine and Deep Learning for Predicting Toxicity Endpoints.

ABSTRACT: Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for applying deep learning techniques has increased considerably. This investigation compares the traditional physico-chemical descriptor and machine learning-based approaches through autoencoder generated descriptors to two different descriptor-free, Simplified Molecular Input Line Entry System (SMILES) based, deep learning architectures of Bidirectional Encoder Representations from Transformers (BERT) type using the Mondrian aggregated conformal prediction method as overarching framework. The results

SUBMITTER: Norinder U 

PROVIDER: S-EPMC9822478 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature