Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Abstract Machine learning (ML) has enabled ground-breaking advances in the healthcare
and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

A Gentle Introduction to Graph Neural Networks Distill About Prize Submit A Gentle Introduction
to Graph Neural Networks Neural networks have been adapted to leverage the structure and …

Automatic Chemical Design is a framework for generating novel molecules with optimized
properties. The original scheme, featuring Bayesian optimization over the latent space of a …

The accurate modeling and prediction of small molecule properties and bioactivities depend
on the critical choice of molecular representation. Decades of informatics-driven research …

Drug discovery aims to find novel compounds with specified chemical property profiles. In
terms of generative modeling, the goal is to learn to sample molecules in the intersection of …

Interpretability of machine learning models is critical to scientific understanding, AI safety, as
well as debugging. Attribution is one approach to interpretability, which highlights input …

Despite a sea of interpretability methods that can produce plausible explanations, the field
has also empirically seen many failure cases of such methods. In light of these results, it …

Graph neural networks are able to solve certain drug discovery tasks such as molecular
property prediction and de novo molecule generation. However, these models are …