Taking the leap between analytical chemistry and artificial intelligence: A tutorial review
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 …
areas, emerging as a vibrant discipline with the capacity to process large amounts of …
A review on artificial intelligence enabled design, synthesis, and process optimization of chemical products for industry 4.0
With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention
for its performance in solving particularly complex problems in industrial chemistry and …
for its performance in solving particularly complex problems in industrial chemistry and …
Large-scale chemical language representations capture molecular structure and properties
J Ross, B Belgodere, V Chenthamarakshan… - Nature Machine …, 2022 - nature.com
Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …
property predictions, which is of interest in drug discovery and material design. Various …
MolGpka: A Web Server for Small Molecule pKa Prediction Using a Graph-Convolutional Neural Network
p K a is an important property in the lead optimization process since the charge state of a
molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane …
molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane …
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction
The properties of the drug may be altered by the combination, which may cause unexpected
drug–drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs …
drug–drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs …
Predicting polymers' glass transition temperature by a chemical language processing model
We propose a chemical language processing model to predict polymers' glass transition
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …
Application of message passing neural networks for molecular property prediction
Accurate molecular property prediction, as one of the classical cheminformatics topics, plays
a prominent role in the fields of computer-aided drug design. For instance, property …
a prominent role in the fields of computer-aided drug design. For instance, property …
Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry
SA Kumar, TD Ananda Kumar… - Future Medicinal …, 2022 - Taylor & Francis
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …
optimization in drug discovery research, requires molecular representation. Previous reports …
D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual …
Y Shi, X Zhang, Y Yang, T Cai, C Peng, L Wu… - Computers in Biology …, 2023 - Elsevier
Resource-and time-consuming biological experiments are unavoidable in traditional drug
discovery, which have directly driven the evolution of various computational algorithms and …
discovery, which have directly driven the evolution of various computational algorithms and …
Accurate physical property predictions via deep learning
Neural networks and deep learning have been successfully applied to tackle problems in
drug discovery with increasing accuracy over time. There are still many challenges and …
drug discovery with increasing accuracy over time. There are still many challenges and …