A data science roadmap for open science organizations engaged in early-stage drug discovery
Abstract The Structural Genomics Consortium is an international open science research
organization with a focus on accelerating early-stage drug discovery, namely hit discovery …
organization with a focus on accelerating early-stage drug discovery, namely hit discovery …
The Road Ahead for Metal–Organic Frameworks: Current Landscape, Challenges and Future Prospects
This perspective highlights the transformative potential of Metal–Organic Frameworks
(MOFs) in environmental and healthcare sectors. It discusses work that has advanced …
(MOFs) in environmental and healthcare sectors. It discusses work that has advanced …
Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models
Advanced artificial intelligence (AI) systems with access to millions of research papers could
inspire new research ideas that may not be conceived by humans alone. However, how …
inspire new research ideas that may not be conceived by humans alone. However, how …
Looking Back the Nonlinear Optical Crystals in a Functionalized Unit's Perspective
Nonlinear optics, signifying a revolutionary paradigm change within the realm of optics, has
ushered in a transformative era by employing the nonlinear optical crystals to manipulate …
ushered in a transformative era by employing the nonlinear optical crystals to manipulate …
Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
Inspired by the recent success of machine-learned interatomic potentials for crystal structure
prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor …
prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor …
Efficient evolutionary search over chemical space with large language models
Molecular discovery, when formulated as an optimization problem, presents significant
computational challenges because optimization objectives can be non-differentiable …
computational challenges because optimization objectives can be non-differentiable …
Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis
C Scheurer, K Reuter - Nature Catalysis, 2025 - nature.com
Self-driving laboratories (SDLs) represent a cutting-edge convergence of machine learning
with laboratory automation. SDLs operate in active learning loops, in which a machine …
with laboratory automation. SDLs operate in active learning loops, in which a machine …
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Automation is one of the cornerstones of contemporary material discovery. Bayesian
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
Machine learning has been pervasively touching many fields of science. Chemistry and
materials science are no exception. While machine learning has been making a great …
materials science are no exception. While machine learning has been making a great …
Reaction blueprints and logical control flow for parallelized chiral synthesis in the Chemputer
M Šiaučiulis, C Knittl-Frank, SH M. Mehr… - Nature …, 2024 - nature.com
Despite recent proliferation of programmable robotic chemistry hardware, current chemical
programming ontologies lack essential structured programming constructs like variables …
programming ontologies lack essential structured programming constructs like variables …