scholarly journals Efficient Multistate Reweighting and Configurational Mapping Algorithms for Very Large Scale Thermodynamic Property Prediction from Molecular Simulations

2014 ◽  
Author(s):  
Himanshu Paliwal
Keyword(s):  
Thermodynamic Property ◽  
Large Scale ◽  
Molecular Simulations ◽  
Mapping Algorithms ◽  
Property Prediction

scholarly journals Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Juncai Li ◽  
Xiaofei Jiang
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Large Scale ◽  
Molecular Data ◽  
Molecular Property ◽  
Property Prediction ◽  
Learning Framework ◽  
Learning Techniques ◽  
Potential Benefits ◽  
Current Sequence

Molecular property prediction is an essential task in drug discovery. Most computational approaches with deep learning techniques either focus on designing novel molecular representation or combining with some advanced models together. However, researchers pay fewer attention to the potential benefits in massive unlabeled molecular data (e.g., ZINC). This task becomes increasingly challenging owing to the limitation of the scale of labeled data. Motivated by the recent advancements of pretrained models in natural language processing, the drug molecule can be naturally viewed as language to some extent. In this paper, we investigate how to develop the pretrained model BERT to extract useful molecular substructure information for molecular property prediction. We present a novel end-to-end deep learning framework, named Mol-BERT, that combines an effective molecular representation with pretrained BERT model tailored for molecular property prediction. Specifically, a large-scale prediction BERT model is pretrained to generate the embedding of molecular substructures, by using four million unlabeled drug SMILES (i.e., ZINC 15 and ChEMBL 27). Then, the pretrained BERT model can be fine-tuned on various molecular property prediction tasks. To examine the performance of our proposed Mol-BERT, we conduct several experiments on 4 widely used molecular datasets. In comparison to the traditional and state-of-the-art baselines, the results illustrate that our proposed Mol-BERT can outperform the current sequence-based methods and achieve at least 2% improvement on ROC-AUC score on Tox21, SIDER, and ClinTox dataset.

scholarly journals Dynamic design: manipulation of millisecond timescale motions on the energy landscape of cyclophilin A

2020 ◽  
Vol 11 (10) ◽  
pp. 2670-2680 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Arun A. Gupta ◽  
Gogulan Karunanithy ◽  
Antonia S. J. S. Mey ◽  
Charis Georgiou ◽  
...  
Keyword(s):  
Large Scale ◽  
Energy Landscape ◽  
Molecular Simulations ◽  
Cyclophilin A ◽  
Dynamic Design ◽  
Loop Motions

Molecular simulations were used to design large scale loop motions in the enzyme cyclophilin A and NMR and biophysical methods were employed to validate the models.

scholarly journals Large-scale Molecular Simulations of Hypervelocity Impact of Materials

2013 ◽  
Vol 58 ◽  
pp. 167-176 ◽  
Author(s):  
Andres Jaramillo-Botero ◽  
Qi An ◽  
Patrick L. Theofanis ◽  
William A. Goddard
Keyword(s):  
Large Scale ◽  
Molecular Simulations ◽  
Hypervelocity Impact

Erratum: Large scale molecular simulations of nanotoxicity

2014 ◽  
Vol 6 (4) ◽  
pp. 344-344
Author(s):  
Camilo A. Jimenez-Cruz ◽  
Seung-gu Kang ◽  
Ruhong Zhou
Keyword(s):  
Large Scale ◽  
Molecular Simulations

scholarly journals Bulk ionic screening lengths from extremely large-scale molecular dynamics simulations

2020 ◽  
Vol 56 (100) ◽  
pp. 15635-15638
Author(s):  
Johannes Zeman ◽  
Svyatoslav Kondrat ◽  
Christian Holm
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Large Scale ◽  
Molecular Simulations ◽  
Scaling Relations ◽  
Dynamics Simulations

Large-scale molecular simulations reveal two screening lengths satisfying distinct scaling relations but with unprecedented accuracy no underscreening is detected for concentrated ionic bulk systems.

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.

Sign in / Sign up

Export Citation Format

Share Document