Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models
Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory
versatility and accuracy in fields such as drug discovery because they are based on …
versatility and accuracy in fields such as drug discovery because they are based on …
Identification of host–guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study
MH Lee - Physical Chemistry Chemical Physics, 2020 - pubs.rsc.org
Booming progress has been made in both the molecular design concept and the
fundamental electroluminescence (EL) mechanism of thermally activated delayed …
fundamental electroluminescence (EL) mechanism of thermally activated delayed …
Deep learning and random forest approach for finding the optimal traditional chinese medicine formula for treatment of alzheimer's disease
HY Chen, JQ Chen, JY Li, HJ Huang… - Journal of Chemical …, 2019 - ACS Publications
It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's
disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) …
disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) …
Random forest refinement of pairwise potentials for protein–ligand decoy detection
An accurate scoring function is expected to correctly select the most stable structure from a
set of pose candidates. One can hypothesize that a scoring function's ability to identify the …
set of pose candidates. One can hypothesize that a scoring function's ability to identify the …
A simple neural network implementation of generalized solvation free energy for assessment of protein structural models
S Long, P Tian - RSC advances, 2019 - pubs.rsc.org
Rapid and accurate assessment of protein structural models is essential for protein structure
prediction and design. Great progress has been made in this regard, especially by recent …
prediction and design. Great progress has been made in this regard, especially by recent …
Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions
E Moman, MA Grishina, VA Potemkin - Journal of Computer-Aided …, 2019 - Springer
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern
drug discovery and one that still poses major challenges. The choice of the appropriate …
drug discovery and one that still poses major challenges. The choice of the appropriate …
Pair Potentials as Machine Learning Features
Atom pairwise potential functions make up an essential part of many scoring functions for
protein decoy detection. With the development of machine learning (ML) tools, there are …
protein decoy detection. With the development of machine learning (ML) tools, there are …
A Celebration of Women in Computational Chemistry
In support of the International Women's Day 2019 theme “Balance for Better” and to address
gender parity in science, the Journal of Chemical Information and Modeling, for the first time …
gender parity in science, the Journal of Chemical Information and Modeling, for the first time …
Using machine learning in accuracy assessment of knowledge-based energy and frequency base likelihood in protein structures
K Serafimova, I Mihaylov, D Vassilev, I Avdjieva… - … Science–ICCS 2020 …, 2020 - Springer
Many aspects of the study of protein folding and dynamics have been affected by the
accumulation of data about native protein structures and recent advances in machine …
accumulation of data about native protein structures and recent advances in machine …
Refinement of pairwise potentials via logistic regression to score protein‐protein interactions
Protein‐protein interactions (PPIs) are ubiquitous and functionally of great importance in
biological systems. Hence, the accurate prediction of PPIs by protein‐protein docking and …
biological systems. Hence, the accurate prediction of PPIs by protein‐protein docking and …