ADMET modeling approaches in drug discovery
LLG Ferreira, AD Andricopulo - Drug discovery today, 2019 - Elsevier
Highlights•ADMET modeling plays a pivotal part in drug discovery.•Chemoinformatics has
evolved into robust machine learning approaches.•Comprehensive web-based platforms for …
evolved into robust machine learning approaches.•Comprehensive web-based platforms for …
Artificial intelligence driven hydrogen and battery technologies–A review
The world has recognized the importance of renewable energy and is moving towards a
rapid transition to renewable energy and energy efficiency. Advances in electrolysis and …
rapid transition to renewable energy and energy efficiency. Advances in electrolysis and …
[HTML][HTML] New machine learning and physics-based scoring functions for drug discovery
Scoring functions are essential for modern in silico drug discovery. However, the accurate
prediction of binding affinity by scoring functions remains a challenging task. The …
prediction of binding affinity by scoring functions remains a challenging task. The …
Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak
Epidemic is a rapid and wide spread of infectious disease threatening many lives and
economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and …
economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and …
DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach
Cancer genome and other sequencing initiatives are generating extensive data on non-
synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In …
synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In …
[HTML][HTML] A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China
C Chen, W He, H Zhou, Y Xue, M Zhu - Scientific reports, 2020 - nature.com
Groundwater is unique resource for agriculture, domestic use, industry and environment in
the Heihe River Basin, northwestern China. Numerical models are effective approaches to …
the Heihe River Basin, northwestern China. Numerical models are effective approaches to …
ThunderSVM: A fast SVM library on GPUs and CPUs
Support Vector Machines (SVMs) are classic supervised learning models for classification,
regression and distribution estimation. A survey conducted by Kaggle in 2017 shows that …
regression and distribution estimation. A survey conducted by Kaggle in 2017 shows that …
Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters
R Meenal, AI Selvakumar - Renewable Energy, 2018 - Elsevier
This paper evaluates the accuracy of Support Vector Machine (SVM), Artificial Neural
Network (ANN) and empirical solar radiation models with different combination of input …
Network (ANN) and empirical solar radiation models with different combination of input …
3d-prnn: Generating shape primitives with recurrent neural networks
The success of various applications including robotics, digital content creation, and
visualization demand a structured and abstract representation of the 3D world from limited …
visualization demand a structured and abstract representation of the 3D world from limited …
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
H Yoon, SC Jun, Y Hyun, GO Bae, KK Lee - Journal of hydrology, 2011 - Elsevier
We have developed two nonlinear time-series models for predicting groundwater level
(GWL) fluctuations using artificial neural networks (ANNs) and support vector machines …
(GWL) fluctuations using artificial neural networks (ANNs) and support vector machines …