What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

Pyzer‐Knapp, Edward O.; Suh, Changwon; Gómez‐Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Aspuru‐Guzik, Alán

doi:10.1146/annurev-matsci-070214-020823

Cited by 263 publications

(232 citation statements)

References 78 publications

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“…Here, a virtual screening technique, which is an effective method in time-critical problems ( 25 ), was applied to determine the structure and energy of an interface. This virtual screening technique has been used in drug discovery, in which a prediction model is constructed using machine learning from a relatively small data set and a large database consisting of the actual data, and the data predicted by the prediction model are constructed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of interface structures and energies via virtual screening

et al. 2016

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Section: Introductionmentioning

confidence: 99%

Prediction of interface structures and energies via virtual screening

et al. 2016

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“…Because the structure and properties of molecules are determined by the laws of quantum mechanics, ultimately chemical discovery must be based on fundamental quantum principles. Indeed, electronic structure calculations and intelligent data analysis (machine learning) have recently been combined aiming towards the goal of accelerated discovery of chemicals with desired properties12345678. However, so far the majority of these pioneering efforts have focused on the construction of reduced models trained on large data sets of density-functional theory calculations.…”

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confidence: 99%

Quantum-chemical insights from deep tensor neural networks

et al. 2017

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Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

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“…The experimental results showed that the two selected cases had higher average heat collection rates than all the existing cases in our previous measurement database. Being similar to a previous chemical HTS concept proposed by Pyzer-Knapp et al [52], we reconstruct the process of this …”

Section: High-throughput Screening (Hts)mentioning

confidence: 96%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

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Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a highperformance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

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What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

Cited by 263 publications

References 78 publications

Prediction of interface structures and energies via virtual screening

Prediction of interface structures and energies via virtual screening

Quantum-chemical insights from deep tensor neural networks

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

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