Machine learning for a sustainable energy future

Z Yao, Y Lum, A Johnston, LM Mejia-Mendoza… - Nature Reviews …, 2023 - nature.com
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

Everything is connected: Graph neural networks

P Veličković - Current Opinion in Structural Biology, 2023 - Elsevier
In many ways, graphs are the main modality of data we receive from nature. This is due to
the fact that most of the patterns we see, both in natural and artificial systems, are elegantly …

MACE: Higher order equivariant message passing neural networks for fast and accurate force fields

I Batatia, DP Kovacs, G Simm… - Advances in Neural …, 2022 - proceedings.neurips.cc
Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …

A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection

M Jin, HY Koh, Q Wen, D Zambon… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Equibind: Geometric deep learning for drug binding structure prediction

H Stärk, O Ganea, L Pattanaik… - International …, 2022 - proceedings.mlr.press
Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

Torsional diffusion for molecular conformer generation

B Jing, G Corso, J Chang… - Advances in Neural …, 2022 - proceedings.neurips.cc
Molecular conformer generation is a fundamental task in computational chemistry. Several
machine learning approaches have been developed, but none have outperformed state-of …

Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem

BL Trippe, J Yim, D Tischer, D Baker… - arXiv preprint arXiv …, 2022 - arxiv.org
Construction of a scaffold structure that supports a desired motif, conferring protein function,
shows promise for the design of vaccines and enzymes. But a general solution to this motif …

Multi-modal molecule structure–text model for text-based retrieval and editing

S Liu, W Nie, C Wang, J Lu, Z Qiao, L Liu… - Nature Machine …, 2023 - nature.com
There is increasing adoption of artificial intelligence in drug discovery. However, existing
studies use machine learning to mainly utilize the chemical structures of molecules but …

A comprehensive survey on deep graph representation learning

W Ju, Z Fang, Y Gu, Z Liu, Q Long, Z Qiao, Y Qin… - Neural Networks, 2024 - Elsevier
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Uni-mol: A universal 3d molecular representation learning framework

G Zhou, Z Gao, Q Ding, H Zheng, H Xu, Z Wei, L Zhang… - 2023 - chemrxiv.org
Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …