Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …
Organic reactivity from mechanism to machine learning
As more data are introduced in the building of models of chemical reactivity, the mechanistic
component can be reduced until 'big data'applications are reached. These methods no …
component can be reduced until 'big data'applications are reached. These methods no …
Automated experimentation powers data science in chemistry
Y Shi, PL Prieto, T Zepel, S Grunert… - Accounts of Chemical …, 2021 - ACS Publications
Conspectus Data science has revolutionized chemical research and continues to break
down barriers with new interdisciplinary studies. The introduction of computational models …
down barriers with new interdisciplinary studies. The introduction of computational models …
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
Accurate prediction of chemical reactions in solution is challenging for current state-of-the-
art approaches based on transition state modelling with density functional theory. Models …
art approaches based on transition state modelling with density functional theory. Models …
A transformer model for retrosynthesis
We describe a Transformer model for a retrosynthetic reaction prediction task. The model is
trained on 45 033 experimental reaction examples extracted from USA patents. It can …
trained on 45 033 experimental reaction examples extracted from USA patents. It can …
Artificial intelligence and automation in computer aided synthesis planning
In this perspective we deal with questions pertaining to the development of synthesis
planning technologies over the course of recent years. We first answer the question: what is …
planning technologies over the course of recent years. We first answer the question: what is …
Mechanistic Inference from Statistical Models at Different Data-Size Regimes
The chemical sciences are witnessing an influx of statistics into the catalysis literature.
These developments are propelled by modern technological advancements that are leading …
These developments are propelled by modern technological advancements that are leading …
Discovery of novel chemical reactions by deep generative recurrent neural network
Abstract The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular
structures opened a novel paradigm in compound design, weaknesses (stability & feasibility …
structures opened a novel paradigm in compound design, weaknesses (stability & feasibility …
Emulsion liquid membrane for simultaneous extraction and separation of copper from nickel in ammoniacal solutions
G Zhu, Y Wang, Q Huang, R Zhang, D Chen… - Minerals …, 2022 - Elsevier
The separation of copper and nickel in ammonia solution is challenging in hydrometallurgy.
In this work, an emulsion liquid membrane (ELM) was constructed using M5640 as an …
In this work, an emulsion liquid membrane (ELM) was constructed using M5640 as an …
Comprehensive analysis of applicability domains of QSPR models for chemical reactions
Nowadays, the problem of the model's applicability domain (AD) definition is an active
research topic in chemoinformatics. Although many various AD definitions for the models …
research topic in chemoinformatics. Although many various AD definitions for the models …