6 papers
Can phenotypic activity be predicted without experimental readouts?
Télio Cropsal, Rocío Mercado
Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for phenotypic prediction, avoid…
TACK: A Statistical Evaluation of Degradation Activity on a Novel TArgeting Chimeras Knowledge Dataset
Stefano Ribes, Nils Dunlop, Rocío Mercado
Proteolysis-targeting chimeras (PROTACs) represent a promising therapeutic modality that induces targeted protein degradation by hijacking the ubiquitin-proteasome system. However,…
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
Siddharth Betala, Samuel P. Gleason, Ali Ramlaoui +12
Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented explorat…
Sustainable Materials Discovery in the Era of Artificial Intelligence
Sajid Mannan, Rupert J. Myers, Rohit Batra +3
Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current genera…
Compressing Biology: Evaluating the Stable Diffusion VAE for Phenotypic Drug Discovery
Télio Cropsal, Rocío Mercado
High-throughput phenotypic screens generate vast microscopy image datasets that push the limits of generative models due to their large dimensionality. Despite the growing populari…
deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models
Frederik Lizak Johansen, Ulrik Friis-Jensen, Erik Bjørnager Dam +3
Novel materials drive advancements in fields ranging from energy storage to electronics, with crystal structure characterization forming a crucial yet challenging step in materials…