Multiple Machine Learning Models Combined with Virtual Screening and Molecular Docking to Identify Selective Human ALDH1A1 inhibitors

2021 ◽  
pp. 107950
Author(s):  
Gera Narendra ◽  
Baddipadige Raju ◽  
Himanshu Verma ◽  
Bharti Sapra ◽  
Om Silakari
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structure-based Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
Accelerate Structure ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate structure-based virtual screening using machine learning models. It has been validated using datasets both from literature (12 datasets, each containing three million molecules docked with FRED) and in-house sources (one dataset of four million compounds docked with Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of the top one percent scoring molecules after docking 10 % of the dataset for the literature data, whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

scholarly journals Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3592
Author(s):  
Jiwon Choi ◽  
Jun Seop Yun ◽  
Hyeeun Song ◽  
Yong-Keol Shin ◽  
Young-Hoon Kang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
African Swine Fever Virus ◽  
Antiviral Agents ◽  
Antiviral Drug ◽  
African Swine Fever ◽  
Antiviral Drugs ◽  
Fever Virus ◽  
Learning Models ◽  
Machine Learning Models

African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.

Designing promising molecules for organic solar cells via machine learning assisted virtual screening

2019 ◽  
Vol 7 (29) ◽  
pp. 17480-17488 ◽  
Author(s):  
Harikrishna Sahu ◽  
Feng Yang ◽  
Xiaobo Ye ◽  
Jing Ma ◽  
Weihai Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Cells ◽  
Virtual Screening ◽  
Organic Solar Cells ◽  
Rational Design ◽  
Learning Models ◽  
High Performing ◽  
Machine Learning Models

Rational design of new OPV molecules via virtual screening of candidate materials using high-performing machine learning models.

scholarly journals Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking

2021 ◽  
Author(s):  
Philipe Oliveira Fernandes ◽  
Diego Magno Martins ◽  
Aline de Souza Bozzi ◽  
João Paulo A. Martins ◽  
Adolfo Henrique de Moraes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Learning Models ◽  
Kinase Activation ◽  
Abl Kinase ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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