Using molecular embeddings in QSAR modeling: does it make a difference?
With the consolidation of deep learning in drug discovery, several novel algorithms for
learning molecular representations have been proposed. Despite the interest of the …
learning molecular representations have been proposed. Despite the interest of the …
[HTML][HTML] A recurrent neural network model to predict blood–brain barrier permeability
The rapid development of computational methods and the increasing volume of chemical
and biological data have contributed to an immense growth in chemical research. This field …
and biological data have contributed to an immense growth in chemical research. This field …
[PDF][PDF] A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data.
Anomaly detection in high dimensional data is a critical research issue with serious
implication in the real-world problems. Many issues in this field still unsolved, so several …
implication in the real-world problems. Many issues in this field still unsolved, so several …
Representative feature selection of molecular descriptors in QSAR modeling
J Li, D Luo, T Wen, Q Liu, Z Mo - Journal of Molecular Structure, 2021 - Elsevier
Quantitative structure-activity relationship (QSAR) has been widely applied to many fields
such as molecular toxicity detection and biological activity predictions. The screening of …
such as molecular toxicity detection and biological activity predictions. The screening of …
A deep learning approach to predict blood-brain barrier permeability
The blood–brain barrier plays a crucial role in regulating the passage of 98% of the
compounds that enter the central nervous system (CNS). Compounds with high permeability …
compounds that enter the central nervous system (CNS). Compounds with high permeability …
A tied-weight autoencoder for the linear dimensionality reduction of sample data
Dimensionality reduction is a method used in machine learning and data science to reduce
the dimensions in a dataset. While linear methods are generally less effective at …
the dimensions in a dataset. While linear methods are generally less effective at …
Prediction of chromatography conditions for purification in organic synthesis using deep learning
M Vaškevičius, J Kapočiūtė-Dzikienė, L Šlepikas - Molecules, 2021 - mdpi.com
In this research, a process for develo** normal-phase liquid chromatography solvent
systems has been proposed. In contrast to the development of conditions via thin-layer …
systems has been proposed. In contrast to the development of conditions via thin-layer …
Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity
Dimensionality reduction techniques are crucial for enabling deep learning driven
quantitative structure-activity relationship (QSAR) models to navigate higher dimensional …
quantitative structure-activity relationship (QSAR) models to navigate higher dimensional …
Auto-KPCA: A Two-Step Hybrid Feature Extraction Technique for Quantitative Structure–Activity Relationship Modeling
Quantitative structure-activity relationship (QSAR) modeling is an established approach for
drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge …
drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge …
A paralleled embedding high-dimensional Bayesian optimization with additive Gaussian kernels for solving CNOP
Abstract Conditional Nonlinear Optimal Perturbation (CNOP) is widely used in atmospheric
and oceanic predictability studies. Solving CNOP is essentially a nonlinear optimization …
and oceanic predictability studies. Solving CNOP is essentially a nonlinear optimization …