One of the major applications of generative models for drug discovery targets the lead-optimization
phase. During the optimization of a lead series, it is common to have scaffold …

Synthetic yield prediction using machine learning is intensively studied. Previous work has
focused on two categories of data sets: high-throughput experimentation data, as an ideal …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous
and complex tissues. However, most experimental protocols for spatial transcriptomics …

Despite growing interest and success in automated in-silico molecular design, questions
remain regarding the ability of goal-directed generation algorithms to perform unbiased …

Molecular generative artificial intelligence is drawing significant attention in the drug design
community, with several experimentally validated proof of concepts already published. …

Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among
classes. We propose a new semi-supervised deep generative model that explicitly models …

Epidemiological modelling has played a key role in proposing, analyzing and justifying non-pharmaceuticals
interventions in response to the COVID-19 pandemic. Despite its …

Synthetic yield prediction using machine learning is intensively studied. Previous work focused
on two categories of datasets: High-Throughput Experimentation data, as an ideal case …

Synthetic yield prediction using machine learning is intensively studied. While previous
work focused on an ideal use case, High-Throughput Experiment datasets, predicting yields …