scholarly journals COSMOfrag:  A Novel Tool for High-Throughput ADME Property Prediction and Similarity Screening Based on Quantum Chemistry

2005 ◽  
Vol 45 (5) ◽  
pp. 1169-1177 ◽  
Author(s):  
Martin Hornig ◽  
Andreas Klamt
Keyword(s):  
Quantum Chemistry ◽  
High Throughput ◽  
Property Prediction ◽  
Adme Property

Novel thermally activated delayed fluorescence materials by high-throughput virtual screening: going beyond donor–acceptor design

2021 ◽  
Vol 9 (9) ◽  
pp. 3324-3333 ◽  
Author(s):  
Ke Zhao ◽  
Ömer H. Omar ◽  
Tahereh Nematiaram ◽  
Daniele Padula ◽  
Alessandro Troisi
Keyword(s):  
Virtual Screening ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Delayed Fluorescence ◽  
Thermally Activated Delayed Fluorescence ◽  
Thermally Activated ◽  
Quantum Chemistry Calculations ◽  
Donor Acceptor ◽  
Molecular Semiconductors ◽  
High Throughput Virtual Screening

125 potential TADF candidates are identified through quantum chemistry calculations of 700 molecules derived from a database of 40 000 molecular semiconductors. Most of them are new and some do not belong to the class of donor–acceptor molecules.

Matched Molecular Series Analysis for ADME Property Prediction

2020 ◽  
Vol 60 (6) ◽  
pp. 2903-2914
Author(s):  
Mahendra Awale ◽  
Sereina Riniker ◽  
Christian Kramer
Keyword(s):  
Property Prediction ◽  
Series Analysis ◽  
Adme Property

High-throughput quantum chemistry and virtual screening for OLED material components

2013 ◽  
Author(s):  
Mathew D. Halls ◽  
David J. Giesen ◽  
Thomas F. Hughes ◽  
Alexander Goldberg ◽  
Yixiang Cao
Keyword(s):  
Virtual Screening ◽  
Quantum Chemistry ◽  
High Throughput

scholarly journals Troubleshooting unstable molecules in chemical space

2021 ◽  
Vol 12 (15) ◽  
pp. 5566-5573
Author(s):  
Salini Senthil ◽  
Sabyasachi Chakraborty ◽  
Raghunathan Ramakrishnan
Keyword(s):  
Big Data ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Chemical Space ◽  
Geometry Optimization ◽  
Data Generation ◽  
Structural Rearrangements ◽  
Unstable Molecules

A high-throughput workflow for connectivity preserving geometry optimization minimizes unintended structural rearrangements during quantum chemistry big data generation.

scholarly journals Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction

2019 ◽  
Vol 20 (14) ◽  
pp. 3389 ◽  
Author(s):  
Ke Liu ◽  
Xiangyan Sun ◽  
Lei Jia ◽  
Jun Ma ◽  
Haoming Xing ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Domain Knowledge ◽  
Large Scale ◽  
Molecular Graph ◽  
Data Sets ◽  
Convolutional Network ◽  
Property Prediction ◽  
Recent Success ◽  
Learning Programs ◽  
Adme Property

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R2 values compared with the Cubist benchmark. The median R2 increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.

scholarly journals Path-Augmented Graph Transformer Network

2019 ◽  
Author(s):  
Benson Chen ◽  
Regina Barzilay ◽  
Tommi S Jaakkola
Keyword(s):  
Physical Chemistry ◽  
Quantum Chemistry ◽  
Recent Work ◽  
Higher Order ◽  
Molecular Property ◽  
Property Prediction ◽  
Molecular Graphs ◽  
Graph Properties ◽  
Local Aggregation ◽  
Aggregation Operations

<div>Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN). These models rely on local aggregation operations and can therefore miss higher-order graph properties. To remedy this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are explicitly built on longer-range dependencies in graphstructured data. Specifically, we use path features in molecular graphs to create global attention layers. We compare our PAGTN model against the GCN model and show that our model consistently</div><div>outperforms GCNs on molecular property prediction datasets including quantum chemistry (QM7, QM8, QM9), physical chemistry (ESOL, Lipophilictiy) and biochemistry (BACE, BBBP)2.</div>

scholarly journals Path-Augmented Graph Transformer Network

2019 ◽  
Author(s):  
Benson Chen ◽  
Regina Barzilay ◽  
Tommi S Jaakkola
Keyword(s):  
Physical Chemistry ◽  
Quantum Chemistry ◽  
Recent Work ◽  
Higher Order ◽  
Molecular Property ◽  
Property Prediction ◽  
Molecular Graphs ◽  
Graph Properties ◽  
Local Aggregation ◽  
Aggregation Operations

<div>Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN). These models rely on local aggregation operations and can therefore miss higher-order graph properties. To remedy this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are explicitly built on longer-range dependencies in graphstructured data. Specifically, we use path features in molecular graphs to create global attention layers. We compare our PAGTN model against the GCN model and show that our model consistently</div><div>outperforms GCNs on molecular property prediction datasets including quantum chemistry (QM7, QM8, QM9), physical chemistry (ESOL, Lipophilictiy) and biochemistry (BACE, BBBP)2.</div>

scholarly journals Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

2014 ◽  
Vol 7 (2) ◽  
pp. 698-704 ◽  
Author(s):  
Johannes Hachmann ◽  
Roberto Olivares-Amaya ◽  
Adrian Jinich ◽  
Anthony L. Appleton ◽  
Martin A. Blood-Forsythe ◽  
...  
Keyword(s):  
Quantum Chemistry ◽  
High Throughput ◽  
Organic Photovoltaics ◽  
High Performance ◽  
Clean Energy

scholarly journals Accurate and Rapid Prediction of pKa of Transition Metal Complexes: Semiempirical Quantum Chemistry with a DataAugmented Approach

2020 ◽  
Author(s):  
Vivek Sinha ◽  
Jochem Jan Laan ◽  
Evgeny Pidko
Keyword(s):  
Transition Metal ◽  
Quantum Chemistry ◽  
Dft Calculations ◽  
High Throughput ◽  
Density Functional ◽  
Tight Binding ◽  
Reactivity Descriptors ◽  
Hydride Complexes ◽  
Computational Screening ◽  
Representative Model

<div> <p>Rapid and accurate prediction of reactivity descriptors of transition metal (TM) complexes is a major challenge for contemporary quantum chemistry. Recently developed GFN2-xTB method based on the density functional tight-binding theory (DFT-B) is suitable for high-throughput calculation of geometries and thermochemistry for TM complexes albeit with a moderate accuracy. Herein we present a data-augmented approach to improve substantially the accuracy of GFN2-xTB for the prediction of thermochemical properties using pK<sub>a</sub> values of TM hydrides as a representative model example. We constructed a comprehensive database for ca. 200 TM hydride complexes featuring the experimentally measured pK<sub>a</sub>’s as well as the GFN2-xTB optimized geometries and various computed electronic and energetic descriptors. The GFN2-xTB results were further refined and validated by DFT calculations with the hybrid PBE0 functional. Our results show that although the GFN2-xTB performs well in most cases, it fails to adequately desribe TM complexes featuring multicarbonyl and multihydride ligand environments. The dataset was analyzed with the partial least squares (OLS) fitting and was used to construct an automated machine learning (AutoML) approach for the rapid estimation of pK<sub>a</sub> of TM hydride complexes. The results obtained show a high predictive power of the very fast AutoML model (RMSE ~ 2.7) comparable to that of the much slower DFT calculations (RMSE ~ 3). The presented data-augmented quantum chemistry-based approach is promising for high-throughput computational screening workflows of homogeneous TM-based catalysts.</p> </div> <br>

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