Year: 2019
Type: preprint
Abstract: In vivo toxicity testing remains a costly and time-consuming component of any pre-clinical drug development campaign. In particular, LD50 measurements require the loss of animal life but remain a critical component in preventing lethal compounds from entering the clinic. With advances in machine learning, in silico LD50 prediction now has the potential to greatly reduce this burden. We study various types of machine learning models to predict acute oral LD50 measurements in rats as regression and classification problems. We demonstrate that transfer learning a ResNet34 model pretrained on ImageNet with test time augmentation generates the best performing regression model and that random forest augmented with conformal prediction provides a robust methodology to perform classification.
Source: ChemRxiv
Authors: conor parks, Zied Gaieb, Rommie Amaro
Institution: University of California San Diego
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3215718722