Toward machine learning-enhanced high-throughput experimentation for chemistry

Toward machine learning-enhanced high-throughput experimentation for chemistry

High-throughput experimentation in chemistry allows for quick and automated exploration of chemical space to, for example, discover new drugs. Combining machine learning techniques with high-throughput experimentation has the potential to speed up and improve chemical space exploration and optimizat...

Szczegółowa specyfikacja

Opis bibliograficzny
1. autor:	Callaghan, S
Format:	Journal article
Język:	English
Wydane:	Cell Press 2021

Podobne zapisy

Applying machine learning and optimization to high-throughput experimentation
od: Low, Andre Kai Yuan
Wydane: (2021)

Pd-Catalyzed Cross-Coupling, High Throughput Experimentation, and Machine Learning
od: Xu, Jessica
Wydane: (2023)

Machine learning augmented high throughput formulations
od: Shi, Shi Jun
Wydane: (2023)

Accelerated viscosity measurements of polymer solutions using high throughput experimentation and machine learning
od: Chua, Zhong Zhe
Wydane: (2021)

Machine Learning Methods for High Throughput Biological Data
od: Murphy, Michael A.
Wydane: (2024)

A System for High-Throughput Materials Exploration Driven by Machine Learning
od: Siemenn, Alexander E.
Wydane: (2022)

Integrated microbioreactor arrays for high-throughput experimentation
od: Lee, Harry, 1976-
Wydane: (2007)

An apparatus for high throughput muscle cell experimentation
od: Garcia-Webb, Michael G. (Michael Gregory)
Wydane: (2007)

Synthesis of metal alloy catalysts using high-throughput experiments and machine learning optimization
od: Calista, Vania
Wydane: (2023)

High-throughput experimental and computational tools for exploring immunity and the microbiome
od: Papa, Eliseo
Wydane: (2012)

Wireless network throughput improvement using machine learning technique
od: Shyu, Zi Xun
Wydane: (2024)

High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning
od: Yang, Zhenze, i wsp.
Wydane: (2022)

High-throughput functional comparison of promoter and enhancer activities
od: Nguyen, Thomas A., i wsp.
Wydane: (2017)

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
od: Chung, Yunsie, i wsp.
Wydane: (2024)

Biocatalytic ketone reductions using Biobeads for miniaturized high throughput experimentation
od: Chew, Jia Shen, i wsp.
Wydane: (2022)

High throughput screening of small molecules for enhancing adipogenesis in vitro
od: Goh, Jun Wei
Wydane: (2018)

Native mass spectrometry: towards high-throughput structural proteomics.
od: Kondrat, F, i wsp.
Wydane: (2015)

The international MAQC Society launches to enhance reproducibility of high-throughput technologies
od: Shi, L, i wsp.
Wydane: (2017)

Towards high-throughput in situ structural biology using electron cryotomography
od: Böhning, J, i wsp.
Wydane: (2020)

Toward High-throughput, Quantitative Platforms to Identify the Targets of Small Molecules
od: Henry, Catherine Campbell
Wydane: (2023)

Applying statistical technique and experimental design methodology to optimize experimental results of an automated high throughput DNA sequencer
od: Han, Stanley Tong Jee.
Wydane: (2008)

High-throughput structure determination
od: Esnouf, R, i wsp.
Wydane: (2002)

High throughput discovery of heteroaromatic-modifying enzymes allows enhancement of novobiocin selectivity.
od: Patel, S, i wsp.
Wydane: (2011)

High-throughput Photodegradation of Plastics
od: Frankson, Alexis
Wydane: (2024)

High-throughput genomic phenotyping
od: Ericksen, Daniel S. (Daniel Southwick), 1977-
Wydane: (2005)

Experimental chemistry/
od: 250479 Hatch, Richard C.
Wydane: (1972)

Discovery of a potent and selective 5-ht5A receptor antagonist by high-throughput chemistry.
od: Corbett, D, i wsp.
Wydane: (2005)

Fintech related machine learning : experimental investigation on the effects of data variance towards difficulty of learning for deep learning models
od: Chua, Shearman Wei Jie
Wydane: (2021)

Interpretable and Explainable Machine Learning for Materials Science and Chemistry
od: Oviedo, Felipe, i wsp.
Wydane: (2023)

Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery
od: Bradford, Gabriel, i wsp.
Wydane: (2024)

Enhancing Zeolite Synthesis with Tailored Structure-Directing Agents (SDAs) and High-throughput Platform
od: Kwon, Soonhyoung
Wydane: (2024)

Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
od: Callaghan, S
Wydane: (2021)

Structural proteomics: High-throughput methods
od: Owens, R
Wydane: (2015)

High-throughput site-directed mutagenesis
od: Strain-Damerell, C, i wsp.
Wydane: (2019)

High-throughput mapping of regulatory DNA
od: Kooshesh, Kameron, i wsp.
Wydane: (2017)

High-throughput quantification of glycoprotein sialylation
od: Markely, Lam Raga Anggara
Wydane: (2011)

High-Throughput Nonlinear Optical Microscopy
od: Yew, Elijah Y.S., i wsp.
Wydane: (2015)

High−throughput cardiac science on the Grid
Wydane: (2010)

High-Throughput Contact Flow Lithography
od: Hill, William Adam, i wsp.
Wydane: (2017)

High-throughput cloning, expression, and purification
od: Owens, R, i wsp.
Wydane: (2007)