Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022‏ - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Artificial intelligence in drug discovery: recent advances and future perspectives

J Jiménez-Luna, F Grisoni, N Weskamp… - Expert opinion on drug …, 2021‏ - Taylor & Francis
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

QSAR without borders

EN Muratov, J Bajorath, RP Sheridan… - Chemical Society …, 2020‏ - pubs.rsc.org
Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Universal differential equations for scientific machine learning

C Rackauckas, Y Ma, J Martensen, C Warner… - arxiv preprint arxiv …, 2020‏ - arxiv.org
In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

Automating drug discovery

G Schneider - Nature reviews drug discovery, 2018‏ - nature.com
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in
which various characteristics of compounds—including efficacy, pharmacokinetics and …

Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review

R Meli, GM Morris, PC Biggin - Frontiers in bioinformatics, 2022‏ - frontiersin.org
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Q Bai, S Tan, T Xu, H Liu, J Huang… - Briefings in …, 2021‏ - academic.oup.com
Deep learning is an important branch of artificial intelligence that has been successfully
applied into medicine and two-dimensional ligand design. The three-dimensional (3D) …

Simulation intelligence: Towards a new generation of scientific methods

A Lavin, D Krakauer, H Zenil, J Gottschlich… - arxiv preprint arxiv …, 2021‏ - arxiv.org
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era

Y **g, Y Bian, Z Hu, L Wang, XQS **e - The AAPS journal, 2018‏ - Springer
Over the last decade, deep learning (DL) methods have been extremely successful and
widely used to develop artificial intelligence (AI) in almost every domain, especially after it …

Taking the leap between analytical chemistry and artificial intelligence: A tutorial review

LB Ayres, FJV Gomez, JR Linton, MF Silva… - Analytica Chimica …, 2021‏ - Elsevier
The last 10 years have witnessed the growth of artificial intelligence into different research
areas, emerging as a vibrant discipline with the capacity to process large amounts of …