Persistent spectral–based machine learning (PerSpect ML) for protein-ligand binding affinity prediction
Molecular descriptors are essential to not only quantitative structure-activity relationship
(QSAR) models but also machine learning–based material, chemical, and biological data …
(QSAR) models but also machine learning–based material, chemical, and biological data …
Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction
Molecular descriptors are essential to not only quantitative structure activity/property
relationship (QSAR/QSPR) models, but also machine learning based chemical and …
relationship (QSAR/QSPR) models, but also machine learning based chemical and …
[HTML][HTML] Improvement of prediction performance with conjoint molecular fingerprint in deep learning
The accurate predicting of physical properties and bioactivity of drug molecules in deep
learning depends on how molecules are represented. Many types of molecular descriptors …
learning depends on how molecules are represented. Many types of molecular descriptors …
Persistent spectral based ensemble learning (PerSpect-EL) for protein–protein binding affinity prediction
Protein–protein interactions (PPIs) play a significant role in nearly all cellular and biological
activities. Data-driven machine learning models have demonstrated great power in PPIs …
activities. Data-driven machine learning models have demonstrated great power in PPIs …
AutoQSAR: an automated machine learning tool for best-practice quantitative structure–activity relationship modeling
Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate
and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results …
and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results …
Ollivier persistent Ricci curvature-based machine learning for the protein–ligand binding affinity prediction
Efficient molecular featurization is one of the major issues for machine learning models in
drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC …
drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC …
Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction
Artificial intelligence (AI) techniques have already been gradually applied to the entire drug
design process, from target discovery, lead discovery, lead optimization and preclinical …
design process, from target discovery, lead discovery, lead optimization and preclinical …
[HTML][HTML] A fingerprints based molecular property prediction method using the BERT model
N Wen, G Liu, J Zhang, R Zhang, Y Fu… - Journal of Cheminformatics, 2022 - Springer
Molecular property prediction (MPP) is vital in drug discovery and drug reposition. Deep
learning-based MPP models capture molecular property-related features from various …
learning-based MPP models capture molecular property-related features from various …
[HTML][HTML] Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction
With the great advancements in experimental data, computational power and learning
algorithms, artificial intelligence (AI) based drug design has begun to gain momentum …
algorithms, artificial intelligence (AI) based drug design has begun to gain momentum …
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce
the high cost and time of drug discovery. After more than five decades of continuing …
the high cost and time of drug discovery. After more than five decades of continuing …