Best practices for artificial intelligence in life sciences research.

We describe 11 best practices for the successful use of artificial intelligence and machine learning in pharmaceutical and biotechnology research at the data, technology and organizational management levels.

Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts.

Artificial Intelligence has advanced at an unprecedented pace, backing recent breakthroughs in natural language processing, speech recognition, and computer vision: domains where the data is euclidean in nature. More recently, considerable progress has been made in engineering deep-learning architectures that can accept non-Euclidean data such as graphs and manifolds: geometric deep learning. This progress is of considerable interest to the drug discovery community, as molecules can naturally be represented as graphs, where atoms are nodes and bonds are edges.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models.

Density of states and critical behavior of the Coulomb glass.

We present zero-temperature simulations for the single-particle density of states of the Coulomb glass. Our results in three dimensions are consistent with the Efros and Shklovskii prediction for the density of states. Finite-temperature Monte Carlo simulations show no sign of a thermodynamic glass transition down to low temperatures, in disagreement with mean-field theory.