Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models
Abstract This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained […]
Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1
Abstract Background Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic […]
SE(3) Equivariant Topologies for Structure-based Drug Discovery
Abstract Modeling protein-ligand interactions is a challenging task that has been approached through an array of perspectives. From physics-based computational approaches to […]
HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed […]
Ro5 Bioactivity Lab: Identification of Drug Candidates for COVID-19
The public health emergency known as the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has […]